bacterial adhesion experiment

Methods, Models, and Analysis of Bacterial Adhesion

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• Itzhak Ofek 3 &
• Ronald J. Doyle 4  

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It is axiomatic to consider that most living and nonliving surfaces have a tendency to be colonized by microorganisms. The importance of microbial adhesion and colonization to surfaces was not appreciated until molecular techniques were applied to analyze modes and mechanisms of cell—substratum interactions. As more and more techniques became available, new knowledge was gained that made it possible to understand the modulation of the adhesion and subsequent colonization of many microorganisms. To date, no single experimental system has been developed that can be used to adequately characterize all aspects of microbe—substratum interactions. It is therefore essential that the reliabilities, advantages, and limitations of the existing techniques be understood. Most techniques employed in the study of adhesion yield restricted amounts of information, usually about defined events in a complicated series of interactions. This chapter considers methods for the study of adhesion. Consideration is given to model systems, methods for separating adherent from nonadherent cells, controlled and uncontrolled variables in experimental design, and approaches used in analyzing adhesion data. Finally, methods related to the identification and regulation of expression of adhesins and their receptors are reviewed.

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Albertsson, P.A. 1958. Particle fractionation in liquid two-phase system. The composition of some phase systems and the behavior of some model particles in them. Application to the isolation of cell walls from microorganisms. Biochim. Biophys. Acta 27 : 378–395.

Article   PubMed   CAS   Google Scholar  

Allison, D.G. and I.W. Sutherland. 1984. A staining technique for attached bacteria and its correlation to extracellular carbohydrate production. J. Microbiol. Meth. 2 : 93–99.

Article   CAS   Google Scholar  

Aronson, M., O. Medalia, L. Schon, D. Mirelman, N. Sharon, and I Ofek. 1979. Prevention of colonization of the urinary tract of mice with Escherichia coli by blocking of bacterial adherence with methyl alpha-D-mannopyranoside. J. Infect. Dis. 139 : 329–332.

Athamna, A. and I Ofek. 1988. Enzyme-linked immunosorbent assay for quantitation of attachment and ingestion stages of bacterial phagocytosis. J. Clin. Microbiol. 26 : 62–66.

PubMed   CAS   Google Scholar  

Baselsky, V.S. and C.D. Parker. 1978. Intestinal distribution of Vibrio cholerae in orally infected infant mice: kinetics of recovery of radiolabel and viable cells. Infect. Immun. 21 : 518–525.

Google Scholar  

Beachey, E.H. and I. Ofek. 1976. Epithelial cell binding of group A streptococci by lipoteichoic acid on fimbriae denuded of M proteins. J. Exp. Med. 143 : 759–771.

Brennan, M.J., J.H. Hannah and E. Leininger. 1991. Adhesion of Bordetella pertussis to sulfatides and to the GalNacß4Gal sequence found in glycosphingolipids. J. Biol. Chem. 266 : 18827–18831.

Clark, W.B., L.L. Bammann, and R.J. Gibbons. 1978. Comparative estimates of bacterial affinities and adsorption sites on hydroxyapatite surfaces. Infect. Immun. 19 : 846–853.

Costerton, J.W. 1980. Some techniques involved in study of adsorption of microorganisms to surfaces. In: Bitton, G. and K.C. Marshall (eds.), Adsorption of Microorganisms to Surfaces . John Wiley & Sons, New York, pp. 403–423.

Cowan, M.M., K.G. Taylor, and R.J. Doyle. 1986. Kinetic analysis of Streptococcus sanguis adhesion to artificial pellicle. J. Dent. Res. 65 : 1278–1283.

Cowan, M.M., K.G. Taylor, and R.J. Doyle. 1987a. Role of sialic acid in the kinetics of Streptococcus sanguis adhesion to artificial pellicle. Infect. Immun. 55 : 1552–1557.

CAS   Google Scholar  

Cowan, M.M., K.G. Taylor, and R.J. Doyle. 1987b. Energetics of the initial phase of adhesion of Streptococcus sanguis to hydroxylapatite. J. Bacteriol. 169 : 2995–3000.

de Man, P., B. Cedergren, S. Enerback, A.C. Larsson, H. Leffler, A.L. Lundell, B. Nilsson, and C. Svanborg-Eden. 1987. Receptor-specific agglutination tests for detection of bacteria that bind globoseries glycolipids. J. Clin. Microbiol. 25 : 401–406.

PubMed   Google Scholar  

Doyle, R.J. 1991. Strategies in experimental microbial adhesion research. In: Mozes, N, P.S. Handley, H.J. Busscher, and P.G. Rouxhet (eds.), Microbial Cell Surface analysis-Structural and Physiocochemical Methods . VCH Publishers, New York, pp. 293–316.

Doyle, R.J., J.D. Oakley, K.R. Murphy, D. McAlister, and K.G. Taylor. 1985. Graphical analyses of adherence data. In: Mergenhagen, S.E. and B. Rosan (eds.), Molecular Basis of Oral Microbial Adhesion . American Society for Microbiology, Washington, pp. 109–113.

Drake, D., K.G. Taylor, A.S. Bleiweis, and R.J. Doyle. 1988. Specificity of the glucanbinding lectin of Streptococcus cricetus. Infect. Immun . 56 : 1864–1872.

Duguid, J.P. and R.R. Gillies. 1957. Fimbriae and adhesive properties of dysentery bacilli. J. Pathol. Bacteriol. 74 : 397–411.

Duguid, J.P. and D.C. Old. 1980. Adhesive properties of Enterobacteriaceae . In: Beachey, E.H. (ed.), Bacterial Adherence (Receptors and Recognition , Vol. 6) . London, Chapman and Hall, pp. 184–217.

Duguid, J.P., M.R. Darekar, and D.W.F. Wheater. 1976. Fimbriae and infectivity in Salmonella typhimurium. J. Med. 11ficrobiol. 9 : 459–473.

Elbein, A.D., B.A. Sanford, M.A. Ramsey, and Y.T. Pan. 1981. Effect of inhibitors of glycoprotein biosynthesis and bacterial adhesion. In: Elliot, K., M. O’Connor, and J. Whelan (eds.), Adhesion and Microorganism Pathogenicity (Ciba Foundation Symposium 80) . Pitman Medical, London, pp. 270–282.

Ellen, R.P. and R.J. Gibbons. 1972. M protein-associated adherence of Streptococcus pyogenes to epithelial surfaces: prerequisite for virulence. Infect. Immun. 5 : 826–830.

Ellen, R.P. and R.J. Gibbons. 1973. Parameters affecting the adherence and tissue tropisms of Streptococcus pyogenes. Infect. Immun . 9 : 85–91.

Filler, S.G., L.G. Der, C.L. Mayer, P.D. Christenson, and J.E. Edwards, Jr. 1987. An enzyme-linked immunosorbent assay for quantifying adherence of Candida to human vascular endothelium. J. Infect. Dis. 156 : 561–566.

Firon, N., D. Duksin, and N. Sharon. 1985. Mannose-specific adherence of Escherichia coli to BHK cells that differ in their glycosylation patterns. FEMS Microbiol. Lett. 27 : 161–165.

Goldhar, J., A. Zilberberg, and I. Ofek. 1986. Infant mouse model of adherence and colonization of intestinal tissues by enterotoxigenic strains of Escherichia coli isolated from humans. Infect. Immun. 52 : 205–208.

Hjerten, S., J. Rosengren, and S. Pahlman. 1974. Hydrophobic interaction chromatography. The synthesis and the use of some alkyl and aryl derivatives of agarose. J. Chromatogr. 101 : 281–288.

Karlsson, J. 1986. Animal glycolipids as attachment sites for microbes. Chem. Phys. Lipids 42 : 153–172.

Klotz, I.M. 1982. Numbers of receptor sites from Scatchard graphs: facts and fantasies. Science 217 : 1247–1249.

Kluepfel, D.A. and S.G. Pueppke. 1985. Isotherm for adsorption of Agrobacterium tumefaciens to susceptible potato (Solanum tuberosum L.) tissues. Appl. Environ. Microbiol. 49 : 1351–1355.

Liljemark, W.F., C.G. Bloomquist, and L.J. Fenver. 1985. Characteristics of the adherence of oral Hemophilius species to an experimental salivary pellicle and to other oral bacteria, In: Mergenhagen, S. and B. Rosan (eds.), Molecular Basis of Oral Microbial Adhesion . American Society for Microbiology, Washington, pp. 94–102.

Ludwicka, A., L.M. Switalski, A. Lundin, G. Pulverer, and T. Wadstrom. 1985. Bioluminescent assay for measurement of bacterial attachment to polyethylene. J. Microbiol. Methods 4 : 169–177.

Mackowiak, P.A. and M. Marling-Cason. 1984. A comparative analysis of in vitro assays of bacterial adherence. J. Microbiol. Methods 2 : 147–158.

Article   Google Scholar  

McEachran, D.W. and R.T. Irvin. 1986. A new method for the irreversible attachment of cells or proteins to polystyrene tissue culture plates for use in the study of bacterial adhesion. J. Microbiol. Methods 5 : 99–111.

Myerthall, D.L. and T.H. Thomas. 1983. Kinetics of adherence of Actinomyces viscosus to saliva-coated silica and hydroxyapatite beads. J. Gen. Microbiol. 129 : 1387–1395.

Nesbitt, W.E., R.J. Doyle, K.G. Taylor, R.H. Staat, and R.R. Arnold. 1982. Positive cooperativity in the binding of Streptococcus sanguis to hydroxylapatite. Infect. Immun. 35 : 157–165.

Ofek, I. and E.H. Beachey. 1978. Mannose binding and epithelial cell adherence of Escherichia coli. Infect. Immun. 22 : 247–254.

Ofek, I., D. Mirelman, and N. Sharon. 1977. Adherence of Escherichia coli to human mucosal cells mediated by mannose receptors. Nature 265 : 923–625.

Ofek, I., H.S. Courtney, D.M. Schifferli, and E.H. Beachey. 1986. Enzyme-linked immunosorbent assay for adherence of bacteria to animal cells. J. Clin. Microbiol. 24 : 512–516.

Pooniah, S., S.N. Abraham, M.E. Dokter, C.D. Wall, and R.D. Endres. 1989. Mitogenic stimulation of human lymphocytes by the mannose-specific adhesin on Escherichia coli type 1 fimbriae. J. Immunol. 142 : 992–998.

Reid, G. 1989. Local and diffuse bacterial adherence on uropithelial cells. Curr. Microbiol. 18 : 93–97.

Rosan, B., R. Eifert, and E. Golub. 1985. Bacterial surfaces, salivary pellicles and plaque formation. In: Mergenhagen, S.E. and B. Rosan (eds.), Molecular Basis of Oral Microbial Adhesion . American Society for Microbiology, Washington, pp. 69–76.

Rosenberg, M. 1981. Bacterial adherence to polystyrene: a replica method of screening for bacterial hydrophobicity. Appl. Environ. Microbiol. 42 : 375–377.

Rosenberg, M., D. Gutnick, and E. Rosenberg. 1980. Adherence of bacteria to hydrocarbons: a simple method for measuring cell surface hydrophobicity. FEMS Microbiol. Leu. 9 : 29–33.

Rosenberg, M.A., E.A. Perry, D.L. Gutnick, E. Rosenberg, and I. Ofek. 1981. Adherence of Acinetobacter calcoacetius RAG-1 to human epithelial cells and to hexadecane. Infect. Immun. 33 : 29–33.

Rosenstein, I.J., D. Grady, J.M.T. Hamilton-Miller, and W. Brumfitt. 1985. Relationship between adhesion of Escherichia coli to uro-epithelial cells and the pathogenesis of urinary infection: problems in methodology and analysis. J. Med. Microbiol. 20 : 335–344.

Scaletsky, C.A., M. Silva, and R. Trabulsi, 1984. Distinctive patterns of adherence of enteropathogenic Escherichia coli to HeLa cells. Infect. Immun. 45 : 534–536.

Schadow, K.H., W.A. Simpson, and S.D. Christensen. 1988. Characteristics of adherence to plastic tissue culture plates of coagulase negative staphylococci exposed to subinhibitory concentrations of antibiotics. J. Infect. Dis. 157 : 71–77.

Smyth, C.J., P. Jonsson, E. Olsson, O. Soderlind, R. Rosengren, S. Hjerten, and T. Wadstrom. 1978. Differences in hydrophobic surface characteristics of porcine enteropathogenic Escherichia coli K88 antigen as revealed by hydrophobic interaction chromatography. Infect. Immun. 22 : 462–472.

Stanislawski, L., W.A. Simpson, D.L. Hasty, N. Sharon, E.H. Beachey, and I. Ofek. 1985. Role of fibronectin in attachment of Streptococcus pyogenes and Escherichia coli to human cell lines and isolated oral epithelial cells. Infect. Immun. 48 : 257–259.

Svanborg-Eden, C. R. Freter, L. Hagberg, R. Hull, S. Hull, H. Leffler, and G. Schoolnik. 1982. Inhibition of experimental ascending urinary tract infection by an epithelial cell-surface receptor analogue. Nature 298 : 560–562.

Valentin-Weigand, P., G.S. Chhatwal, and H. Blobel. 1987. A simple method for quantitative determination of bacterial adherence to human and animal epithelial cells. Microbiol. Immunol . 31 : 1017–1023.

Woolfson, A.D., S.P. Gorman, D.F. McCafferty, and D.S. Jones. 1987. On the statistical evaluation of adherence assays. J. Appl. Bacteriol. 63 : 147–151.

Wyatt, J.E., S.M. Poston, and W.C. Noble. 1990. Adherence of Staphylococcus aureus to cell monolayers. J. Appl. Bacteriol. 69 : 834–844.

Zilberberg, A., J. Goldhar, and I. Ofek. 1983. Adherence of enterotoxigenic Escherichia coli (ETEC) strains to mouse intestine segments analyzed by Langmuir adherence isotherms. FEMS Microbiol. Lett. 16 : 225–228.

Zilberberg, A., I. Ofek, and J. Goldhar. 1984. Affinity of adherence in vitro and colonization of mice intestine by enterotoxigenic Escherichia coli (ETEC). FEMS Microbiol. Lett. 23 : 103–106.

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Ofek, I., Doyle, R.J. (1994). Methods, Models, and Analysis of Bacterial Adhesion. In: Bacterial Adhesion to Cells and Tissues. Springer, Boston, MA. https://doi.org/10.1007/978-1-4684-6435-1_2

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Research Article

A Short–Time Scale Colloidal System Reveals Early Bacterial Adhesion Dynamics

Affiliation Unité de Génétique des Biofilms, Institut Pasteur, CNRS URA 2172, Paris, France

Affiliation Laboratoire Physico-Chimie Curie, Institut Curie, CNRS UMR 168, Université Paris VI , Paris, France

* To whom correspondence should be addressed. E-mail: [email protected]

• Christophe Beloin, 
• Ali Houry, 
• Manuel Froment, 
• Jean-Marc Ghigo, 
• Nelly Henry

• Published: July 8, 2008
• https://doi.org/10.1371/journal.pbio.0060167
• Reader Comments

The development of bacteria on abiotic surfaces has important public health and sanitary consequences. However, despite several decades of study of bacterial adhesion to inert surfaces, the biophysical mechanisms governing this process remain poorly understood, due, in particular, to the lack of methodologies covering the appropriate time scale. Using micrometric colloidal surface particles and flow cytometry analysis, we developed a rapid multiparametric approach to studying early events in adhesion of the bacterium Escherichia coli . This approach simultaneously describes the kinetics and amplitude of early steps in adhesion, changes in physicochemical surface properties within the first few seconds of adhesion, and the self-association state of attached and free-floating cells. Examination of the role of three well-characterized E. coli surface adhesion factors upon attachment to colloidal surfaces—curli fimbriae, F-conjugative pilus, and Ag43 adhesin—showed clear-cut differences in the very initial phases of surface colonization for cell-bearing surface structures, all known to promote biofilm development. Our multiparametric analysis revealed a correlation in the adhesion phase with cell-to-cell aggregation properties and demonstrated that this phenomenon amplified surface colonization once initial cell-surface attachment was achieved. Monitoring of real-time physico-chemical particle surface properties showed that surface-active molecules of bacterial origin quickly modified surface properties, providing new insight into the intricate relations connecting abiotic surface physicochemical properties and bacterial adhesion. Hence, the biophysical analytical method described here provides a new and relevant approach to quantitatively and kinetically investigating bacterial adhesion and biofilm development.

Author Summary

When bacteria grow on solid surfaces, they can form three-dimensional communities called biofilms. Within these complex structures, bacteria can develop specific tolerance to different microbiocides, causing serious health and economic problems. Investigations of the key molecular events involved in biofilm formation have shown that surface-exposed adhesin proteins promote this process, but many questions remain regarding the mechanisms and biophysics of surface adhesion. We introduced an original approach to investigating the very early steps in bacterial adhesion that uses dispersed colloidal surfaces as microbial adhesion substrates. Using flow cytometry, we performed a quantitative real-time analysis of adhesion kinetics of several strains of the bacterium Escherichia coli , which were genetically engineered to produce well-characterized cell-surface adhesins that are known to promote biofilm development. We provide evidence for previously unknown adhesin-dependent behaviors, such as clear-cut differences in the very initial phases of surface colonization. We also demonstrate that initial adhesion correlates with almost instant surface property changes, and that cell-to-cell association might serve as an amplification mechanism for surface colonization. We therefore provide a new understanding of the intricate relationships between the physico-chemistry of abiotic surfaces and bacterial adhesion.

Citation: Beloin C, Houry A, Froment M, Ghigo J-M, Henry N (2008) A Short–Time Scale Colloidal System Reveals Early Bacterial Adhesion Dynamics. PLoS Biol 6(7): e167. https://doi.org/10.1371/journal.pbio.0060167

Academic Editor: Alain Filloux, Imperial College London, United Kingdom

Received: November 28, 2007; Accepted: May 29, 2008; Published: July 8, 2008

Copyright: © 2008 Beloin et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by a joint Action Concertée Incitative (ACI) Interface Physique-Chimie-Biologie. NH is supported by the Institut Curie and CNRS. J-MG and CB are supported by the Institut Pasteur, the CNRS, the Network of Excellence EuroPathoGenomics (LSHB-CT-2005–512061), and the Fondation BNP PARIBAS.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: FCM, flow cytometry; FL1, mean fluorescence intensity in channel 1 (BP530/15nm); FL3, mean fluorescence intensity in channel 3 (LP650nm); fl i , bacterial cell unitary fluorescence in FL1; FSC, forward scattering; GFP, green fluorescent protein; PBS, phosphate-buffered saline; PI, propidium iodide; PYR, pyranine; R1, free particles scattering region; R2, free particles fluorescence (channel 1) region; R3, bacteria-associated particles fluorescence (channel 1) region; R b , bacteria scattering region; R flag , bacterial aggregates fluorescence (channel 1) region in bacteria records; R flb , bacteria region; SSC-H, side scattering height

Introduction

Bacterial growth on surfaces and interfaces leads to the formation of three-dimensional communities called biofilms [ 1 ]. Biofilm-specific tolerance to different biocidal treatments used in health care facilities stimulated the investigation of key molecular events in biofilm formation, and molecular factors promoting bacterial adhesion have been characterized, including surface-exposed adhesins and polysaccharidic polymers. However, initial cell surface attachment itself is still poorly understood, and many questions remain regarding biophysical aspects of the adhesion process due to lack of appropriate investigation tools [ 2 , 3 ].

Physico-chemical approaches based on the Derjaguin-Landau-Verwey-Overbeek (DLVO) [ 4 ] theory, although reliable in predicting interactions between well-controlled model hard spheres, have often proven inappropriate for modeling bacterial adhesion [ 5 ]. This is likely due to the multiplicity of parameters involved in the adhesion process, influenced both by biological and environmental factors. On the other hand, most biological approaches to surface colonization rely on procedures that develop on an hourly or daily scale. Hence, interpretations are often made at the final adsorption stage or at equilibrium, when initial adhesion is blurred by subsequent biofilm development steps, thus preventing precise kinetic analysis of the adhesion process [ 3 ]. Alternative short-term approaches are therefore needed to provide quantitative and kinetic information on the early stage of bacterial interaction with a surface.

We introduced a new methodology which takes advantage of advanced flow cytometry (FCM) analyses [ 6 ] to characterize initial bacterial cell surface attachment. Only a few studies monitoring bacterial attachment to mammalian cells using FCM have been published [ 7 , 8 ]. Thus far, stream techniques involved in FCM have appeared incompatible with the study of bacterial adhesion usually studied on macroscopic plane surfaces. We have overcome this apparent antagonism by choosing dispersed surfaces in the form of well-characterized micrometric particles as adhesion substrates in order to explore initial events of surface colonization by the bacterium Escherichia coli . We used charged particles as adhesion substrates, representing the most widespread situation for surfaces immerged in aqueous media due either to their inherent ionization state or to the water ion structure at the interfaces. Then, we examined the contribution of three well-characterized E. coli adhesion factors with a time resolution of a few seconds and the precision of one bacterium per particle. This revealed several phases leading to surface colonization, and evidenced clear-cut differences strongly dependent on the nature of the adhesin expressed at the cell surface. We also explored the contribution of cell-to-cell aggregation properties and particle surface physicochemical property changes throughout the colonization process. This analytical procedure therefore opens up new perspectives in the understanding of bacterial adhesion to abiotic surfaces.

Description and Characterization of the Micrometric Colloidal System

To develop a short–time scale method for studying early bacterial adhesion, we used spherical micrometric colloidal particles as adhesion substrates, both positively charged (aminated, NH 3 + ) and negatively charged (carboxylated, COO – ) with zeta potentials of +45 mV and −55 mV, respectively. Suspensions of 10-μm particles were resuspended in phosphate-buffered saline (PBS) and examined by microscopy and FCM. 80% of the events detected with the two types of particles were concentrated in scattering value region R1 (free particles scattering region), corresponding to a monodispersed population ( Figure 1 A and unpublished data).

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(A) FCM scattering plot and corresponding bright field microscope images of a COO – particle sample washed in water and suspended in PBS. 80% of events were concentrated in the scattering value region R1 corresponding to a monodispersed, 10-μm-diameter population. Outliers corresponded to a population of slightly larger particles and a few doublets. Cationic (NH 3 + ) particles displayed the same scattering characteristics as anionic (COO – ) particles.

(B) Microscopic images of PYR- (2nd column) and PI- (3rd column) labeled cationic (1st row) and anionic (2nd row) particles. Dye concentration equal to 16.7 nM.

https://doi.org/10.1371/journal.pbio.0060167.g001

Particle surface charge properties were traced using an anionic dye (pyranine, PYR) and a cationic dye (propidium iodide, PI), the fluorescent labeling of which was detected by microscope imaging ( Figure 1 B) and FCM ( Figure S1 ). Both probes displayed fast (adsorption half-time < 1 s) and selective electrostatic adsorption onto particles carrying opposite charges. PYR was emitted in the FL1 channel [mean fluorescence intensity in channel 1] (525 ± 10 nm) and PI in the FL3 channel [mean fluorescence intensity in channel 3] (>670 nm). Labeling properties were conserved in M63B1 medium, the rather high ionic strength (≈200 mM versus 150 mM,e.g., for PBS) medium used in this study both for growth of the bacteria and the adhesion assay (see below), indicative of strong and irreversible association, which demonstrated that the chosen pairs of dyes were flexible surface-charge reporters in the heterogeneous context of a bacterial suspension.

FCM Monitoring of Isolated and Aggregated Bacteria in Suspension

To determine optimal recording conditions for bacterial adhesion in the colloidal system, we analyzed samples composed of planktonic cultures of fluorescent and non-fluorescent E. coli K-12 strains MG1655 and MG1655 gfp , respectively, mixed at various proportions. Due to their smaller size, bacteria had to be analyzed with higher scattering photomultiplier gains than colloidal particles and appeared in a distinct acquisition window. We isolated a bacterial signal in scattering region R b containing at least 98% of the recorded events ( Figure 2 A ) . Depending on sample composition, fluorescence plots FL1/forward scattering (FSC) stemming from R b events displayed one or two subsets, each with a characteristic mean fluorescence FL1 value conserved in the whole sample series ( Figure 2 B– 2 D ) . The ratio of the number of cells counted in high and low fluorescence subsets accurately matched values expected from the mixtures ( Figure S2 ), indicating that the recorded signals originated from single cells. Indeed, if more than one cell was detected in a single event, then higher number of subsets depending on the number of detected cells would have appeared with intermediate fluorescence intensities. This enabled us to determine a cell unitary fluorescence value fl i from the mean FL1 fluorescence of an R flb gate (bacteria region) containing the fluorescent bacteria ( Figure 2 B). When green fluorescent protein (GFP)-labeled cells were allowed to rest at room temperature for 2 h, we observed the formation of cell aggregates that produced higher scattering and a fluorescence signal defining a new gate, R flag (bacterial aggregates fluorescence (channel 1) region in bacteria records) ( Figure 2 E). The aggregate scattering parameters were sufficiently different from those of colloidal particles to avoid any confusion between the two types of objects even when aggregate sizes increased. Therefore, the R flag number of events (N(R flag )), mean fluorescence (FL1(R flag )), and mean forward scattering (FSC(R flag )) enabled us to report the dispersion state of the cell suspension.

Scattering (A) and fluorescence dot plots of all GFP-labeled (B), all non GFP-labeled cells (C) and a mixture (50/50) of both (D). Aliquots of exponentially growing cultures were diluted in PBS to obtain a 5 × 10 6 cells/ml suspension.

(E) FL1 dot plots of a slightly aggregated cell population (R flag , right plot); in this case, 3% of the objects are considered aggregates.

https://doi.org/10.1371/journal.pbio.0060167.g002

Quantitative and Qualitative Multiparametric Monitoring of Cell Adhesion Events in a Colloidal Suspension

To study bacterial adhesion onto colloidal substrates, GFP-labeled E. coli cells were brought into contact with cationic particles under gentle stirring, producing mild shear stress on the order of a few tens of piconewtons per contact. While this stirring abolished gravity effects, it also screened cell motility. Therefore, although bacterial motility has been shown to play a role in surface colonization under static conditions [ 9 ], its effect is here masked by velocity gradients due to stirring-induced hydrodynamic shear. The question of the role of bacterial self-propulsion through flagellar motility is thus not addressed here. The FCM signal was recorded on aliquots taken at different times from the cell-particle sample using a sample flow speed of 1 μl/s and an acquisition time equal to 5 s. Direct microscopic observation showed that bacteria adhered to colloidal particles, leading to the emergence of a new cluster of higher fluorescence on particle FL1/FSC cytograms, corresponding to particles carrying at least one bacterial cell ( Figure 3 A and B). Bare (R2) and colonized particles (R3) were thus easily discriminated on the basis of their fluorescence intensity and described by the two particle subsets stemming from the R1 gate ( Figure 3 B). As shown in an experiment performed using a 200-fold excess of colloidal particles over bacteria ( Figure S3 )—which ensured that only rare but single cell adhesion events would statistically appear—bacterial fluorescence was not affected by adhesion to particles. Therefore, the mean number of particle-bound bacteria ( n F ) could be obtained simply by dividing mean particle fluorescence (FL1(R1)) by bacterial unitary fluorescence ( fl i = FL1(R flb )).

(A) Microscope images of MG1655 gfp E. coli cells and particles brought into contact with particles in M63B1 medium for 20 min. Bright-field and bright-field fluorescence combined images are shown. Adhesive events can be observed on the combined image; examples are indicated by white arrows.

(B) Corresponding fluorescence dot plots are shown for particles alone (left) and cells and particles brought into contact for 20 min (right). Gate R2 corresponds to bare particles, whereas gate R3 corresponds to colonized particles.

https://doi.org/10.1371/journal.pbio.0060167.g003

For any value of f c , we could thus calculate the mean of the corresponding statistical distribution n P = – ln (1 – f c ) . This provided a direct means of characterizing adhesion distribution and detecting eventual cooperative effects by comparing n P to experimental value n F calculated from mean fluorescence. This also enabled the definition of a cooperative index λ, given by the ratio n F / n P .

To achieve a complete description of the cell particle suspension, 5 μl of a 10 −6 M PI solution were added to each aliquot taken from the incubation sample. Labeling performed just before the FCM analysis and after the cell–particle interaction allowed us to characterize the particle surface charge. It did not, however, affect cell–particle association, as checked by parallel recordings of unlabeled and labeled samples (unpublished data).

The complete analysis enabled us to determine, for each sample test, the free-floating cell aggregation state, the cell particle surface association degree, as well as the particle surface charge state. It also reported the potential cooperative character of cell to particle surface binding.

The colloidal system approach therefore enabled full quantification of the early bacterial adhesion process. Moreover, the sensitivity of signal recording enabled us to determine adhesion kinetics taking place within the range of few seconds after cell–particle contact.

Curli Expression Alters the Kinetics of Initial Attachment of Bacteria to an Abiotic Surface

Curli are thin aggregative fimbriae assembled at the cell surface of most Enterobacteriaceae, in which they have been shown to promote adhesion to abiotic surfaces [ 10 ]. They form 6–12-nm-diameter structures whose length varies between 0.5 and 1 μm [ 11 ]. To explore the contribution of curli to early steps in bacterial adhesion, we used fluorescent E. coli either overexpressing curli due to a mutation in ompR , the positive regulator of curli expression (MG1655 gfpompR234 ) [ 10 ], or deprived of curli (MG1655 gfp Δ csgA ). We compared the adhesion profiles of mid-exponential phase growth cultures on aminated particles using a cell-to-particle ratio close to 200. FCM analyses of 10 μl aliquots taken from both cultures at ∼30 s intervals revealed that the presence of curli strongly modified early initial attachment of bacteria to an abiotic surface. The adhesion kinetics of the strain constitutively expressing curli displayed two distinct binding phases ( Figure 4 A). After 20 min of adhesion, the extent of particle surface colonization, as reported by the mean number of bound bacteria per particle n F , was more than a hundred times higher than that of the curli-deficient mutant strain ( Figure S4 and Figure 4 A and 4 B). In contrast, strain MG1655 gfp Δ csgA exhibited single-phase kinetics characterized by a steady state corresponding to a low level of binding reached within the first seconds after particle contact ( Figure 4 B).

Adhesion kinetic curves of MG1655 gfpompR234 (A) and MG1655 gfp Δ csgA (B). Particles and cells were brought into contact at time t = 0. Cell to particle ratio was around 200 and particle concentration equal to 6 × 10 6 /ml.

(C) Colonized particle fraction kinetics are shown for two sequential additions of fresh NH 3 + particles to MG1655 gfpompR234 cells. 50 μl of stock particles were added to a cell/particle sample after 9 min incubation once the first colonization phase leveled off.

(D) Charge inversion of NH 3 + particle during surface colonization. FL3 intensity in time due to PI adsorption (2 × 10 −8 M) is shown for MG1655 gfpompR234 (▴) and MG1655 gfp Δ csgA (•).

(E) Bright field and fluorescence microscope images of MG1655 gfpompR234 engaged in second-phase colonization of NH 3 + particles.

https://doi.org/10.1371/journal.pbio.0060167.g004

To quantitatively analyze these cell surface adhesion profiles, we considered the first adhesion phase ranging from 10 s to 10 min after particle/bacteria initial contact. Dynamics of early surface binding, n F ( t ) was adjusted to the first-order–like kinetics equation n F ( t ) = N max (1 – exp ( -k a t )) for both types of bacterial cells, with N max the maximum mean colonization level displayed at plateau and k a the apparent time constant of the adhesive process. The colonization plateau was obtained in both cases in the presence of a large fraction of free-floating cells.

For MG1655 gfp Δ csgA, the steady state was characterized by an N max value equal to 0.25 ± 0.3 bacteria per particle (bact part −1 ), which corresponded to the attachment of one cell for every four particles. This very restricted binding occurred rapidly after cell–particle contact, with an initial colonization rate reported by N max k a equal to 1.3 bact part −1 min −1 . The rapid establishing of this low-level steady state ( t 1/2 = 0.13 min) indicated that cell and particle surfaces were essentially repulsive to each other under the conditions of the experiment, contrary to what could have been expected from opposite charge (negative cells and positive particles) surfaces. This point is further investigated below.

The curli producer strain MG1655 gfpompR234 displayed, in phase I, a ten times higher colonization level than the curli-defective strain ( N max = 3.2 ± 0.5 bact part −1 ) and exhibited a longer characteristic time ( t 1/2 = 2.8 min and an initial binding rate of 0.8 bact part −1 min −1 ) ( Figure 4 A and 4 B). This indicated that, in the presence of curli, cell and particle surfaces did not experience the repulsive potential observed between curli-defective cells and particle surfaces. Nevertheless, in this phase I, MG1655 gfpompR234 cells exhibited a colonization plateau in between three and four attached cells per particle, which represented a rather sparse surface occupation. This could be due to particle surface heterogeneity—of molecular nature or concentration—resulting in a low density of adhesive sites for bacteria in this first adhesive phase. Fresh particles added at the plateau to the cell–particle suspension displayed the same colonization kinetics as those initially present at t = 0 ( Figure 4 C), demonstrating that remaining floating bacteria still had the capacity to adhere to freshly added particles. On the other hand, no additional colonization occurred (stable n F ) when additional bacteria were added at the colonization plateau (unpublished data).

These results suggest establishment, on the particle surface, of a repulsive potential that limits MG1655 gfp Δ csgA adhesion but is partially overcome by the presence of curli adhesion to the surface of MG1655 gfpompR234.

Contact with the Cell Suspension Induces Rapid Surface Conversion That Modifies Initial Surface Properties and Colonization Rates

To investigate the possible origin of the repulsive potential, we monitored particle surface charge during colonization using the cationic dye PI labeling procedure (see above). As shown above ( Figure S1 ), aminated particles suspended in PBS or M63B1 fresh medium did not adsorb the dye. Their FL3 intensity remained at the level of the background, corresponding to particles in the absence of dye. In contrast, when PI was added to the analysis test tubes taken from the incubation sample in the presence of exponentially grown bacteria, we measured a rapid increase in the PI labeling signal both with curli producer and nonproducer bacteria ( Figure 4 D). This indicated that the particle surface—initially positively charged—had turned into a negative surface potential, which enabled PI adsorption. This PI labeling affected both cell-free and colonized particles in the suspension ( Figure S5 A), suggesting that soluble species present in the bacterial supernatant were responsible for this effect. Consistently, aminated particles resuspended in filter-sterilized culture supernatants were labeled with PI, confirming production by bacterial cultures of anionic molecules. Furthermore, labeling intensity increased when supernatant stemmed from a longer overnight culture (unpublished data). No significant bacterial cell death was observed within the culture before or after incubation, as indicated by the absence of noticeable number of red-labeled bacteria on fluorescence microscopy images.

Indeed, in case of cell death, the dye contained in our samples would cross cell membrane and accumulate inside the cell through DNA association. Thus, only a small, naturally occurring bacterial lysis, which releases bacterial products in the medium, is expected to take place in our experiments, suggesting that this phenomenon could contribute only marginally to the surface properties changes observed. When COO – particles were tested, we observed that PI labeling, initially high with such anionic surface functionalization, slightly decreased, suggesting that these surfaces were also modified by cell supernatant, which finally set the exposed surfaces to similar potentials ( Figure S5 B). Zeta potential measurements were performed on particles first incubated in bacterial culture media coming from cultures of the F plasmid-bearing strain and then suspended at 10% v/v in water for measurements. Value shifted for cationic particles from +36 mV in unspent medium (+45 in pure water) to −32 mV after 15 min incubation in 12-h culture spent medium. Anionic particles displayed slightly reduced negative charge, shifting from −65mV in unspent medium to −42 mV after medium conditioning, completely corroborating the behavior reported by dyes labeling. Very similar results were obtained with supernatant from MG1655 strain (unpublished data). This surface charge conversion to a negative potential was thus likely to explain the repulsive potential observed against bacteria.

To further investigate this, MG1655 gfpompR234 or MG1655 gfp Δ csgA bacteria were placed in contact with both positively (NH 3 + ) and negatively (COO – ) charged particles, simultaneously and in the same sample. PI labeling enabled us to differentiate both types of particles within the sample ( Figure S5 C). For both strains, COO – particle colonization was only slightly reduced compared to colonization on NH 3 + particles, as if cells sensed very similar surfaces (unpublished data).

These results indicate that negative surface charge conversion induced by bacterial supernatants determines the surface potential of the interaction with cells independently of the initial surface state. In the absence of curli, this negative and thus repulsive potential dominates the interaction. Expression of curli at the cell surface obviously enabled overcoming this repulsion either by superimposing a much stronger binding force or by allowing the interaction to occur at a longer surface separation distance where the electrostatic potential had dropped. Indeed, in this high ionic strength medium, the Debye length is short and the electrostatic interactions quickly vanish with distance.

A Second Colonization Phase Observed with Curli-Expressing Bacteria Was Correlated with an Increase in Free-Floating and Surface-Associated Bacterial Aggregates

After the initial binding phase, the MG1655 gfpompR234 colonization kinetics profile systematically displayed a sudden increase about 10–12 min after cell–particle contact ( Figure 4 A and 4 E). Concomitantly, we observed a drastic increase in the population contained in the gate characteristic of bacterial aggregates (R flag ), along with the appearance in the colloid plot of a new population collected in gate R5 (bacterial aggregates fluorescence (channel 1) region in particles records), corresponding to larger size particle-free bacterial aggregates ( Figure 5 A– 5 C). The kinetics curve showed that MG1655 gfpompR234— but not MG1655 gfp Δ csgA— aggregation started after a 10-min lag, suggesting that aggregation could result from a biological shift in curli production. Indeed, when cells were incubated 15 min in the presence of 100 μg/ml of the translation inhibitor chloramphenicol before adhesion test, the second adhesive phase was abolished as well as the drastic cell-cell aggregation increase (unpublished data). During this second phase, we also observed a rapid exponential increase in the cooperative index ( Figure 5 D), which indicated that already occupied particles were preferentially but not exclusively colonized. In addition, a synchronized increase in PI labeling of colonized particles ( Figure 4 D), but no change in cell-free particle labeling (unpublished data), showed that this second phase colonization induced an increase in the negative potential of the colonized surfaces.

Bacteria (left) and colloid (right) dot plots of MG1655 gfpompR234 brought into contact with NH 3 + particles before (A) and after (B) second colonization phase onset. (C) Number of events comprised in the R flag gate in the presence (black diamond) or absence (gray diamond) of particles. Cells were taken from a culture (DO = 0.5) and resuspended by pipetting in an adequate volume of buffer at time t = 0. (D) Time dependence of cooperative index, λ, during MG1655 gfpompR234 colonization.

https://doi.org/10.1371/journal.pbio.0060167.g005

Cell-cell aggregation also occurred when the experiment was conducted in the absence of particles, therefore ruling out the hypothesis of a surface-induced process ( Figure 5 C).

Kinetics of Particle Colonization Is Dependent on the Nature of Adhesion Factors Expressed by Bacteria

To evaluate the specificity of our observations with curli-expressing bacteria, we tested the adhesive properties of E. coli strains expressing other well-characterized adhesion factors. We first analyzed the adhesion profile of an E. coli strain that constitutively produces the autotransported adhesin Ag43, a short, 10-nm, surface-exposed adhesin known to promote cell-cell interactions and biofilm formation [ 12 – 14 ]. Ag43-mediated cell-to-cell association was confirmed by the presence of a significant number of events in aggregate-characteristic gates ( Figure 6 A). However, interestingly, the adhesion profile of Ag43 + cells (MG1655 gfp PcL flu ) was very close to that of the Ag43-depleted strain (MG1655 gfp Δ flu ) ( Figure 6 B) . This shows that, by contrast with curli expression, the strong cell-to-cell aggregation induced upon Ag43 expression did not correlate with surface attachment. This is probably due to lack of efficient initial bacterial adhesion enabling further anchoring of bacterial aggregates.

Ag43 (A and B); F-pili (C and D) were examined under the same conditions as in Figure 4 . (black circles) corresponds to cells producing indicated surface adhesin and (gray circles) to cells not producing surface adhesin. (E) Cooperative index in time for F pili–expressing cell colonization of NH 3 + particles.

https://doi.org/10.1371/journal.pbio.0060167.g006

Next we examined the adhesive behavior of an E. coli strain expressing the F-conjugative pilus, which promotes both initial adhesion and biofilm maturation [ 15 ]. We compared colonization of the F-free strain (MG1655 gfp ) with an F-carrying strain (MG1655 gfp F). We observed that F expression supported a significant one-phase surface attachment the kinetics of which could be approximated to a first-order-like process ( Figure 6 C). The plateau was obtained at N max =2.7 bact part −1 with a time constant k a = 0.16 min −1 , indicating an initial adhesion rate of N max k a = 0.45 bact min −1 part −1 , close to that of curli-expressing strain MG1655 gfpompR234 in phase I (see Figure 4 C). Meanwhile, the F pilus-expressing cells did not exhibit significant self-association properties under these conditions ( Figure 6 D), indicating that this appendage did not support strong cell-to-cell interactions as curli or Ag43 did. Moreover, no second adhesive phase was observed and the cooperative index of F-carrying strains displayed no significant deviation from the unit ( Figure 6 E), which is consistent with the idea of a second adhesive phase correlated with both initial phase one association and strong cell-to-cell association.

Taken together, these results show that the kinetics of particle colonization are dependent on the nature of the expressed adhesion factor.

While bacterial adhesion genetics is being actively explored and produces increasing information on molecular factors that promote biofilm, the precise mechanism by which bacteria adhere to inert surfaces is not well understood [ 16 , 17 ].

We analyzed initial steps in bacterial attachment to abiotic surfaces by developing a new methodological approach that combined the use of a colloidal micrometric bead suspension as adhesion substrate and FCM. This strategy enabled the design of a real-time, multiparametric analysis to simultaneously monitor surface attachment, free-floating bacteria, and the surface physico-chemical state in the same sample, with the precision of a single cell and a time resolution of a few tens of seconds, without any separation step. Previous original attempts to assess initial binding kinetics used quartz microbalance equipment [ 18 ]. Although quartz resonance microbalance provides online monitoring of the physical state of the adsorbed cell layer reported by frequency shifts, it cannot be directly related to the numbers of attached cells. In contrast, FCM using colloids as adhesion substrates enabled us to directly monitor adhesion kinetics, providing initial binding rates that are unequivocal parameters of cell-surface association. These parameters are unaffected by bacterial features that are not part of initial adhesion, such as the division rate or resistance to shear or gene-dependent transition leading to biofilm. Moreover, the multi-parametric nature of FCM offered the opportunity of correlating initial adhesion with surface property changes and free-floating cell aggregation shifts, two phenomena involved in surface colonization. FCM kinetics are obtained on large cell populations with high statistical weight even for small subsets (>1%). However, unlike microscope imaging, this technique does not allow monitoring the fate of a single bacterial cell over time [ 19 , 20 ].

We showed that expression of several well-defined adhesion factors induced significant differences in early adhesion profiles recorded after cell surface exposure. We first demonstrated the direct implication of curli and F-conjugative pilus in cell surface attachment within the first minutes of contact. In contrast, the strain constitutively expressing short surface adhesin Ag43 displayed no significant rapid initial surface binding. Yet this appendage was implicated in abiotic surface colonization in various models [ 9 , 21 ]. This suggests that the Ag43 adhesin does not contribute to early steps in interactions between bacteria and the surface; rather, it may participate in subsequent steps of biofilm maturation not investigated in our short–time scale approach.

Kinetic characteristics of this initial surface binding support the hypothesis of the formation of a finite number of links between cell and particle surfaces rather than a physicochemical surface interaction, which would saturate either at cell population exhaustion or at surface overcrowding. Binding could occur either from the formation of a molecular complex between bacterial appendages and binding sites exposed at the particle surface after adsorption of surface active molecules produced by the cell suspension, or from hydrophobic interactions enabling cell strong adhesion on sparse and limited zones distributed over the particle surface. On the other hand, we show here, for all strains studied irrespective of the nature of exposed surface appendages, that surfaces undergo significant charge changes immediately when placed in contact with bacterial suspensions. The surface conditioning of host or environmental origin has been frequently addressed [ 2 , 22 , 23 ]. However, bacteria themselves produce macromolecules with sufficient surface activity to play a role in bacterial adhesion. This surface conversion induced by anionic surface-active molecules contained in the bacterial suspension could be one of the main reason why no clear relationship between initial surface physicochemical properties and bacterial adhesion has been established up to now [ 24 ], and that it might also partially explain why the DLVO theory has been generally unsuccessful in describing bacterial colonization.

We have no information as yet on the nature of the anionic biomacromolecule involved in the observed fast surface conversion, although bacterial surface polysaccharides such as released capsule fragment or LPS might be good anionic macromolecular candidates [ 25 , 26 ]. This surface conditioning accounts for the repulsive potential observed between MG1655 gfp Δ csgA and the particle surface. This phenomenon is of particular interest in elucidating bacterial adhesion mechanisms, since it might constitute a mechanism by which nature selects the adhesive organisms, by first setting a repulsive surface potential.

In addition to initial surface contact, curli-expressing bacteria exhibited a second kinetic phase that correlated with the onset of significant bacterial self-aggregation. This second colonization phase could be due to the accumulation of curli subunits at the surface of the bacteria and developed cooperatively. Suppression of this second phase by chloramphenicol treatment known to stop translation suggests that de novo synthesis of curli during the adhesion process is responsible for curli accumulation (unpublished data). Consistently, when curli genes were placed under the control of an anhydrotetracyclin inducible promoter, ptetO [ 27 ], both aggregation and adhesion increased with increasing concentration of the inducer (unpublished data). Interestingly, although the strain constitutively expressing adhesin Ag43 displayed a strong aggregation phenotype, as expected from previous reports, it did not display secondary surface colonization [ 13 , 27 , 28 ]. Consistently, no second colonization phase was observed with E. coli expressing the F-pilus, which exhibited no rapid free-floating self-association in our experimental conditions using dispersed exponentially grown bacteria. Altogether, this demonstrates that early surface-bound cells could serve as anchors initiating cell associations, in good agreement with the role played by curli in biofilm maturation [ 10 , 29 , 30 ] and suggests that extensive cell-cell aggregation can amplify surface colonization, provided sufficient initial surface binding has occurred.

Many reviews on bacterial adhesion have contributed to spreading the notion of a general two-step mechanism comprising primary reversible adhesion, in which most bacteria leave the surface on-and-off to join the planktonic phase, followed by secondary irreversible attachment [ 2 , 20 , 22 , 31 ]. This so-called reversible-to-irreversible transition actually describes differential resistance to shear due to contact maturation in an out-of-equilibrium process. In experiments presented here, we monitored only irreversible adhesion in the thermodynamic sense; indeed no spontaneous binding shift upon dilution of suspension was observed. We show here that these irreversible interactions could take place during the first seconds of the contact independently of a prior reversible step. It is very likely that in our experiments, reversible events such as those described by Agladze et al. are instantaneously discarded by exposure to hydrodynamic flow (stirring and FCM shear flow) [ 32 ].

In conclusion, we have introduced a new short–time resolution tool for quantitative and statistical analysis of cell-surface adhesion. It enables determination of initial cell-surface binding kinetics and analysis of initial adhesive behaviors conferred by different bacterial cell surface structures. Beyond initial adhesion, we show that cell–cell aggregation properties held by several surface appendages amplify surface colonization once initial adhesion is established. This suggests a biofilm development scenario in which various adhesion factors contribute to different but complementary tasks to colonize the abiotic surface. During this process, micrometric structures such as curli and F-pilus support the formation of initial contact with the surface, the properties of which are strongly influenced by surface-active biomacromolecules. Further elucidation of molecular interactions behind initial steps in bacterial adhesion might help to elaborate original approaches to limiting biofilm development.

Finally, we believe that quantitative and kinetic dissection of early adhesion events could also represent a powerful way to investigate other aspects of biofilm development, including evaluation of potential antiadhesive compounds, gene expression upon surface contact or strain competition and cooperation during surface colonization.

Materials and Methods

Bacterial strains and growth conditions..

Constitutive curli producers (MG1655 gfpompR234 ) were obtained by transducing, into gfp -tagged MG1655 (MG1655 gfp ), the ompR234 mutation that specifies a gain of function allele of ompR , a gene encoding an activator of the curli operon [ 10 ]. MG1655 gfp F carries a derivative of the F-conjugative plasmid. Non-curli producers (MG1655 gfp Δ csgA ), non-Ag43 producers (MG1655 gfp Δ flu ), as well as constitutive Ag43 (MG1655 gfp PcL flu ) producers were constructed by a three-step PCR procedure as described in [ 33 , 34 ]. The latter strains were constructed by introducing, in front of the flu gene, the km PcL cassette, which enables constitutive expression of chromosomal target genes [ 27 ]. Primers used to perform, verify, and sequence the different constructions are listed in Table S1 . All strains were grown in lysogeny broth (LB) medium or, for adhesion experiments, in defined M63B1 medium with 0.4% glucose (M63B1Glu) at 30 °C for curli experiments or 37 °C for other experiments. Antibiotics were added when required: kanamycin (Km, 50 μg/ml), chloramphenicol (Cm, 25 μg/ml), ampicillin (Amp, 100 μg/ml), spectinomycin (Spec, 50 μg/ml), and tetracycline (Tet, 7.5 μg/ml).

Colloidal surfaces.

10-μm-diameter polystyrene latex particles functionalized with carboxyl groups (COO – ) were purchased from Polysciences. Particles were used either with their initial carboxyl functionalization after extensive washing and re-suspension in M63B1 minimal medium, or after surface treatment with polyethylenimine permethobromide a cationic, 6,300 molecular weight, branched polymer according to the principle initially introduced by Decher [ 35 ]. Briefly, particles were washed in pure water by filtration over 0.60 μm of diameter filter, adjusted to a concentration close to 10 6 /ml and gently stirred for 5 min in the presence of 3 × 10 −3 M polyethylenimine (monomer concentration), which corresponds to an excess of positive charges compared to the negative charges of the colloidal particles. The permethylated amine polymer adsorbed rapidly onto the negatively charged particle, producing a net charge inversion and providing a cationic amino-functionalization (NH 3 + particles). Particles were then washed four times before being concentrated five times in the experiment buffer, usually PBS or M63B1. The quality and stability of the deposit were checked by measuring the zeta potential in water using a Malvern zetasizer nano ZS Malverninstrument: −55 mV and +45 mV for COO – and NH 3 + particles, respectively. The deposit remained stable for several weeks, therefore confirming the robust adsorption already mentioned by other authors [ 36 ]. The colloid particles used in this study did not display any bactericidal activity against E. coli cells.

FCM analyses of bacteria and colloid suspensions were performed using a Becton-Dickinson flow cytometer (Facscalibur). GFP and green dye PYR emissions were recorded in fluorescence channel FL1 (band pass centered on 530 nm). The PI signal was collected in channel FL3 (>650 nm). Two different acquisitions were performed on each sample to collect either the bacterial signal or the colloid signal (lower FSC and SSC (side scattering) amplifications were used for colloids, but the same fluorescence gain settings were used for both objects). At least 1,500 events were recorded in colloid acquisition and 15,000 in that of bacteria. Data were analyzed using CellQuest (BDIS) and FlowJo (Tree Star) multivariate analysis software.

Adhesion assay.

Cells and particles were brought into contact in a round-bottom tube. An adequate volume of exponentially growing bacterial suspension—DO 590 = 0.5–0.6 in M63B1Glu—was usually injected at time t = 0 into a particle suspension adjusted to the appropriate concentration in M63B1 and stirred at room temperature on a soft vortex (1,000 rpm min −1 ). Total volume of this incubator was usually equal to 500 μl. Aliquots of 5–20 μl were taken at given incubation times for immediate analysis in FCM in 300 μl PBS supplemented with 5 picomoles PI or microscope imaging.

Supporting Information

Figure s1. surface charge detection by fcm.

FCM dot plots of PYR- (2nd column) and PI- (3rd column) labeled cationic (1st row) and anionic (2nd row) particles. Five picomoles of the appropriate dye were introduced in each particle sample.

https://doi.org/10.1371/journal.pbio.0060167.sg001

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Figure S2. GFP + and GFP – E. coli Mixture in FCM Analysis

Fluorescence dot plots of GFP + and GFP − cell mixtures 50/50, 10/90, and 1/99 from left to right. Aliquots of exponentially growing cultures were diluted in PBS to obtain a 5 × 10 6 cells/ml suspension. Numbers of cells in each population very closely matched proportions expected from the mixtures.

https://doi.org/10.1371/journal.pbio.0060167.sg002

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Figure S3. Fluorescence Signal Calibration

FCM signal of particles alone (A) or cell-particle contact using 100-fold excess of particles over bacteria (B), ensuring that only rare but single cell adhesion events would statistically appear compared with the signal of free-floating bacteria, fl i , recorded in parallel on the same sample (C). Mean fluorescence (FL1) intensity of single cell–carrying particles, calculated on ten independent recordings (72 single cell–carrying particles detected), was found equal to 42 ± 5, whereas free-floating bacteria displayed a mean fluorescence of 41 ± 3, calculated for 98% of the population around the distribution mode (FL1). An example of data is shown here. Gate R2 corresponds to bare particles, whereas gate R3 corresponds to colonized particles.

https://doi.org/10.1371/journal.pbio.0060167.sg003

(462 KB PDF)

Figure S4. MG1655 gfpompR234 and MG1655 gfp Δ csgA Surface Colonization

FCM dot plots and kinetic curves of MG1655 gfpompR234 (A) and MG1655 gfp Δ csgA (B). Particles and cells were brought into contact at time t = 0. Plots show net segregation between cell-bearing particles and free particles. Cell to particle ratio was around 200 and particle concentration equal to 6 × 10 6 /ml.

https://doi.org/10.1371/journal.pbio.0060167.sg004

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Figure S5. Surface Charge Inversion Effects

(A) FL1 and FL3 dot plots showing red labeling of both colonized and cell-free particles before (2 left panels) and from 1 min after (3 right panels) cell–particle contact for MG1655 gfpompR234 and NH 3 + particles. We introduced 5 picomoles of PI in all analysis samples and collected mean particle fluorescence in channel 3 (FL3) corresponding to PI emission (>670 nm).

(B) Kinetics of surface conversion for both cationic (█) and anionic (•) particles.

In panel (C), parallel colonization of anionic and cationic particles within the same sample tube is shown. From left to right: scattering, FL3 and FL1 signals of particle mixture brought into contact with MG1655 gfpompR234 cells for 5 min. Gate R7 (particles high fluorescence intensity (channel 3) region) comprises COO – particles with their high PI labeling; R8 (particles low fluorescence intensity (channel 3) region) includes NH 3 + particles.

https://doi.org/10.1371/journal.pbio.0060167.sg005

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Table S1. Primers Used in This Study

https://doi.org/10.1371/journal.pbio.0060167.st001

(38 KB DOC)

Acknowledgments

We thank F. Gaboriaud, J. Bibette and J.M. Betton for critical reading of the manuscript.

Author Contributions

CB, JMG, and NH conceived and designed the experiments. CB, AH, MF, and NH performed the experiments. CB, AH, and NH analyzed the data. CB, JMG, and NH wrote the paper.

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Article Contents

Introduction, materials and methods, acknowledgements, authors' contributions, conflict of interest statement, data availability.

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Quantification of bacterial adhesion to tissue in high-throughput kinetics

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Nimrod Shteindel, Danielle Gutman, Gil Atzmon, Yoram Gerchman, Quantification of bacterial adhesion to tissue in high-throughput kinetics, Biology Methods and Protocols , Volume 8, Issue 1, 2023, bpad014, https://doi.org/10.1093/biomethods/bpad014

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Bacterial adhesion to tissue is the starting point for many pathogenic processes and beneficial interactions. The dynamics and speed of adhesion (minutes) make high-resolution temporal kinetic data important, but this capability is absent from the current toolset. We present a high-throughput method with a second-to-minute kinetic resolution, testing the adhesion of Pseudomonas aeruginosa PAO1 wild-type, flagella-, pili-, and quorum-sensing mutants to human embryonic kidney (HEK293) cells. Adhesion rates were in good correlation with HEK293 confluence, and the ways in which various bacterial mutations modified adhesion patterns are in agreement with the published literature. This simple assay can facilitate drug screening and treatment development as well as provide a better understanding of the interactions of pathogenic and probiotic bacteria with tissues, allowing the design of interventions and prevention treatments.

Bacterial interactions, in both pathogenic and probiotic contexts, depend on the bacterial ability to adhere to the host cells [ 1 ]. The importance of this process led to the development of multiple methods of study. These include extraction of bacteria from tissue, quantification by plating, and counting colony-forming units [ 2 ]; extraction of bacterial DNA and quantification by quantitative PCR [ 3 ]; microscopy imaging for counting bacteria after tissue fixation [ 4 ], with or without fluorescence dye labeling [ 5 ]; fluorescence-activated cell sorting (FACS) after fluorescence labeling of the bacteria [ 6 ]; or fluorescence microscopy [ 7 ]. Nevertheless, besides direct microscopy of fluorescently labeled bacteria, all the above methods are destructive and therefore preclude kinetic quantification, and when using them, we are likely to miss temporal differences that may be critical to our understanding of the adhesion process. Last, these methods require expensive equipment and/or are labor intensive, adding further complication to screening experiments [ 8 ].

We developed a simple method based on [ 8 ] to allow rapid high-throughput measurement of bacterial adhesion kinetics to tissue culture. We utilize fluorescently tagged bacteria (here Pseudomonas aeruginosa PAO1 expressing Green Fluoresence Protein), and follow the kinetic of its adhesion to human cells (human embryonic kidney; HEK293) in a multitier 96-well plate using a plate reader capable of measuring bottom fluorescence. To mask the fluorescence emitted by nonadhering, planktonic bacteria, we add a dye (Allura Red) that absorbs fluorescence at both the excitation and emission wavelengths of the fluorophore, masking nonbottom-adhered bacteria.

Instrumentation

This assay requires a plate fluorimeter with bottom-reading capability. Here we use a multimode plate reader (Synergy HT, Biotek, VT, USA). When testing other instruments, we find that filter-based fluorimeters were superior to monochromator-based fluorimeter (Biotek H1), probably because the broader wavelength spectrum typically measured in filter-based systems allows a more sensitive measurement, although this could be model dependent. Fluorescence was measured from the bottom position (excitation 485/20 nm, emission 528/20 nm, gain 60). OD 600nm (optical density at 600 nanometer) was measured using the same plate reader in a volume of 100 µl (optical path of 0.3 cm) using a sterile buffer as blank.

Strains and culture conditions

Human cells.

HEK293 [ 9 ] were chosen as they are easy to cultivate and a very common surface-adhering cell line, with relevance to P. aeruginosa ability to infect kidneys [ 10 ]. The cells were seeded in a 96-well plate at 5K, 10K, 20K, and 30K cells per well, in Dulbecco’s Modified Eagle medium (Biological Industries, Israel) supplemented with 10% Fetal Bovine Serum (Biological Industries) and 1% Penicillin–Streptomycin antibiotics (Biological Industries), 100 µl per well. The plate was incubated in a CO 2 incubator with constant humidity at a temperature of 37°C, at 5.5% CO 2 , for 24 h. Shortly before the addition of the bacteria confluence was estimated, and the medium was removed and replaced with 50 µl of Dulbecco’s Phosphate Bufferd Saline (DPBS; Sartorius, Israel, Cat#02-023-1A) for bacterial adherence experiments.

Human cells confluence

The confluence of the HEK293 cells was estimated after the 24-h incubation using Nikon Eclipse TS100 (Nikon, USA) inverted microscope equipped with Nikon 10×/0.25 objective. Pictures were taken with an iPhone X (Apple, USA) using an aftermarket adapter with 10X ocular, and the images were analyzed for confluence by eye and by using the Cell and Gene Therapy Catapult web application [ 11 ] ( https://ct.catapult.org.uk/resources/confluency-tool ), gaining very similar results.

Pseudomonas aeruginosa PAO1 ( P. aeruginosa PAO1) was used as model bacteria. Pseudomonas aeruginosa is a common opportunistic pathogen infecting a great variety of plant and animal species including humans [ 12 ], and notorious for its adherence to the lung tissue of cystic fibrosis patients [ 13 ], severe burns [ 14 ], contamination of I.V. catheters and intubation devices [ 15 , 16 , respectively), and kidney infection [ 10 ]. These bacteria are also common adhesion and biofilm formation models [ 17 ]. The w.t. and Δ flg F were purchased from the Washington University Manoil laboratory mutant library [ 18 ]; the Δ pqs A and Δ las I mutants [ 19 ] were kindly provided by Professor Miguel Camara of Nottingham University; the Δ fli C and Δ pil A mutants [ 20 ] were kindly provided by Professor Martin Welch of Cambridge University. All P. aeruginosa strains tested were transformed by electroporation with the pMRP9-1 plasmid, for constitutive expression of GFPmut2 and Carbenicillin resistance [ 21 ], kindly provided by Professor Ehud Banin of the Bar Ilan University.

Preparation of bacteria for adherence experiments

Before each experiment, bacteria were cultivated in 6 ml of M9 medium complemented with 0.4% glucose and 200 μg/ml Carbenicillin (all from Merc-Sigma, Israel) in 18 mm glass test tubes for 18 h at 37°C, shaking at 120 round per minute (RPM).

Bacteria were collected by centrifugation (5000 g, 1 min, 25°C), washed twice in DPBS without calcium and magnesium, and re-suspended in DPBS (as above), diluted to OD 600nm = 0.1 (measured in 100 µl/well in a 96-well plate), and supplemented with 1.6 mg/ml Allura-red AC dye from a stock solution of 100 mg/ml (Dye bought from Sigma, Israel; National Center for Biotechnology Information, PubChem Compound Summary for CID 33258, Allura Red AC. https://pubchem.ncbi.nlm.nih.gov/compound/Allura-Red-AC . Accessed July 31, 2023) [ 8 ]. For total fluorescence quantification, P. aeruginosa was diluted to OD 600nm  = 0.05 with DPBS as measured for 100 µl/well in a 96-well plate (equivalent to 0.15 in 1 cm cuvette), fluorescence was measured, and results were used to normalize adhesion results.

Measuring adhesion kinetics to HEK293 cells in various confluences

Fifty microliters of P. aeruginosa PAO1 w.t. was added to each well of the plate containing HEK293 cells in the indicated confluences, resulting in a final dye concentration of 0.8 mg/ml and final bacterial culture density of OD 600nm = 0.05. The plate was immediately loaded onto the plate reader, and bottom fluorescence was read every 30 s for 1 h. Each treatment was carried out in 12 replicate wells. An 8-channel multichannel pipettor was used for speed and the whole plate was loaded in <1 min.

Kinetic adhesion of P. aeruginosa PAO1 mutants

HEK293 cells were cultivated (as in “Human cells” section) to a confluence of 20 000 cells per well. The various P. aeruginosa PAO1 mutant cultures were cultivated and prepared as described above and added in 50 µl, six duplicate wells per mutant culture. Adhesion measurements were carried out as described earlier, taking a measurement every 60 s for 1 h. After the kinetics measurements, the plate was gently emptied and the liquid was replaced with 100 µl of sterile DPBS supplemented with 0.8 mg/ml dye, measuring bottom fluorescence once more to observe adhesion in the absence of unattached bacteria.

Normalizing fluorescence

Different P. aeruginosa mutants were found to have a different base fluorescence, even at similar bacterial density, probably due to differences in GFP expression. To correct for this difference, we normalized the kinetic and post-kinetic fluorescence measurements. To this end, after growth, washing, and dilution the bacteria to OD 600nm <0.5 (see “Preparation of bacteria for adherence experiments” section), 100 µl of each bacterial strain was measured for OD 600 nm (as a measure of bacterial density) and for fluorescence (without dye, to average the whole population). The OD and fluoresence measurements were used to calculate the normalization fluorescence as detailed in Equation (1) .

Calculation of normalized fluorescence

Differentiating total and strongly adhered bacteria

After kinetics, in order to differentiate between strongly and loosely adhering bacteria, we removed the liquid from each well by pipetting and refilled the wells with 100 µl of DPBS supplemented with 0.8 mg/ml dye for a uniform background. Next, bottom fluorescence was measured once more to assess adhesion in the absence of nonadhering or loosely adhering bacteria.

Statistical analysis

Statistical analysis was performed using SPSS (IBM SPSS Statistics for Windows, Version 24.0. Armonk, NY: IBM Corp.), and post hoc tests used Tukey LSD.

The method described here is illustrated in Fig. 1 . Briefly, the human cells are attached to the bottom of the wells, and fluorescence-tagged bacteria are added to the liquid above the cells ( Fig. 1a ). Allura-red, a nontoxic dye with strong optical absorbance at the excitation and emission wavelength of GFP ( Fig. 1c ), is added with bacteria to quench fluorescence from planktonic nonadhering bacteria that are further away from the human cells ( Fig. 1b ).

Illustration of the method for quantifying bacterial adhesion to HEK293 cells in the absence ( a ) and presence ( b ) of dye. Bacteria are illustrated in green (as they express GFP) and HEK293 cells in orange. In the absence of dye ( a ) the fluorescence of both adhering and suspended bacteria is read, while with dye ( b ) the GFP excitation and emission of light is decaying due to absorption by the dye molecules. ( c ) Absorbance spectrum of Allura-red AC (the dye; red fill), excitation (cyan), and emission (green) spectrum of GFP. The addition of dye blocks excitation light from reaching suspended bacteria (b), optically isolating the signal produced by adhering bacteria (Adapted with permission from Shteindel et al. [ 8 ] and from Shteindel and Gerchman [ 21 ]).

Seeding different numbers of HEK293 cells into the wells resulted in different and correlated confluency ( Fig. 2a ). Measuring adherence of P. aeruginosa PAO1 to the different HEK293 cell numbers revealed a strong effect; with very small adherence without HEK293 cells ( Fig. 2b , “0 HEK cells”) and higher cell concentration/confluence resulting in faster and higher final adherence of the bacteria ( Fig. 2b ). When correlating the last kinetic point fluorescence with HEK293 cell concentration, a very good linear correlation was evident up to 20 000 cells ( Fig. 3a , dashed curve fit; R 2 = 0.9792). Having 30 000 cells did not improve the bacterial adherence (compare Figs 2b and 3a , 20 000 and 30 000 HEK293 cells/well), and regression quality degraded when the 30 000 cells data were included ( Fig. 3a , dashed and smooth regression lines; R 2 = 0.8658 and 0.9792, with and without 30 000 HEK293 cells, respectively). When correlating bacterial adherence to HEK293 confluence ( Fig. 3b , dashed and smooth regression lines), the difference between correlations was negligible ( R 2 = 0.9877 and 0.9924, respectively), suggesting bacteria prefer to adhere to the exposed cell/tissue surface rather than between the cells/the exposed plastic surface.

Adhesion of P. aeruginosa PAO1 w.t. to HEK293 cells in various cell confluences. ( a ) Number of seeded HEK293 cells per well, estimated confluence, and representative microscopy images. ( b ) Adhesion kinetics of the GFP-labeled P. aeruginosa for different HEK293 concentrations/confluences; dots stand for measurement times, flanking thin lines are for ±1 SD, n  = 12 per treatment.

Correlation between HEK293 cells concentration/confluences and P. aeruginosa PAO1 w.t. adhesion. ( a ) Last kinetics point adhesion in different HEK293 concentrations; n  = 12 per treatment, error bars stand for ±1 SD. ( b ) The same data as in (a) but fitted against HEK293 confluence (see Fig. 2a ). For both (a) and (b), the data were fit to linear model for the whole data (smooth line) and for the three first HEK cells concentrations/confluencies (dashed line). Capital letters above symbols denote statistically significant different results as determined by One-way ANOVA with Tukey post-hoc test, P  < 0.01.

When comparing different mutants ( Fig. 4 ), results fit our current understanding of P. aeruginosa adhesion behavior– bacteria readily adhere to eukaryotic cells [ 22 , 23 ], using the flagellum for initial adhesion to the surface of the cells [ 24 ]. This causes flagella mutants (Δ flg F and Δ fli C) to display no significant adhesion kinetics ( Fig. 4a ) and low total adherence ( Fig. 4a and b ). When testing for strong adherence (i.e. after the removal of liquid and un-/loosely adhered bacteria) and total adherence (last kinetic time point, Fig. 4b ), these mutants also display low strong adherence (31 and 10%, respectively). The lack of kinetic change in fluorescence also demonstrated that sedimentation is not an issue in this system – the fluorescence of these mutants remains low and constant throughout the kinetic experiment.

Adhesion of various P. aeruginosa mutants to HEK293 at 20 000 cells per well ( a ). Adhesion kinetics of the various mutants; dots stand for measurement times, flanking thin lines are for ±1 SD. ( b ) Total adhesion (last kinetic time point; Blue columns) and strong adhesion (left after removal of loosely adhering bacteria; Orange columns). n  = 12 for w.t. P. aeruginosa , n  = 6 for mutant; error bars stand for ±1 SD. Different capital letters ( b ) above the blue columns represent statistically different groups for total adhesion and different lower case letters above the orange columns represent statistically different groups for strong adhesion ( P  < 0.05 by one-way ANOVA with post-hoc). Asterisks above blue-orange columns indicate a statistically significant difference between total adhesion and strong adhesion for the same mutant ( P  < 0.05 in one-tailed Welch’s t -test comparison within each mutant). Percentage above the columns stands for percentage of strong adhesion of total adhesion.

Pili mutant (Δ pil A) adhesion is similar to that of the wild-type, for both total and strong adhesion, in agreement with the results of [ 25 ], showing that pili are not used in tissue adhesion but rather facilitate bacterial binding to hydrophobic surfaces. Both QS mutants (Δ las I and Δ pqs A) showed statistically higher adherence to the cells ( Fig. 4b ), in agreement with the published results [ 26 ]. Nevertheless, the Δ las I shows higher total adherence combined with lesser strong adherence, when compared to the Δ pqs A mutant (87% and 72% respectively), a phenomenon worthy of further exploration.

Adherence of bacteria to host cells is an important step in both pathogenic and probiotic interactions. Here we present a method for simple quantification of the kinetics of bacterial adherence to tissue culture, using HEK293 and P. aeruginosa as a model. This method can be extended to any surface adhering cell lines (e.g. most epithelial cells as well as many nonmammalian cells) and to many other bacteria [ 8 ], as long as they can be fluorescently labeled, by expressing a fluorescence protein, a metabolic dye, or otherwise. Furthermore, this approach relies on commercially available multi-purpose equipment (i.e. bottom fluorescence plate reader, fluorescence-tagged bacteria, and simple dye tuned for the fluorescence tag). This approach can be easily modified for other microorganism–cell pairs (and even other motile cells), adding to our toolset for studying the adherence of microorganisms to tissue. The experiments provide consistent results, in agreement with published literature, making for a good bacterial adhesion kinetics measurement in a high-throughput setting at low labor and material cost. We hope this method drives research into specific tissue adhesion mechanisms and preclinical testing.

We would like to thank Professor Martin Welch of Cambridge University for making available the Δ pil A, Δ fli C P. aeruginosa PAO1 mutants; Professor Miguel Camara of Nottingham University for the Δ las I and Δ pqs A mutants; Professor Ehud Banin of the Bar Ilan University for the w.t PAO1 and the pMRP9-1 plasmid; Larisa Tamar Shteindel for generating the illustration presented in Fig. 1 .

Nimrod Shteindel (Conceptualization [lead], Investigation [lead], Methodology [lead], Resources [equal], Writing – original draft [equal], Writing – review and editing [supporting]), Danielle Gutman (Methodology [supporting], Resources [supporting]), Gil Atzmon (Conceptualization [supporting], Formal analysis [supporting], Resources [supporting], Writing – original draft [supporting], Writing – review and editing [supporting]), and Yoram Gerchman (Conceptualization [equal], Formal analysis [equal], Methodology [supporting], Supervision [lead], Writing – review and editing [equal]).

None declared.

Data are incorporated into the article. raw data will be shared on reasonable request to the corresponding author.

Stones DH , Krachler AM.   Against the tide: the role of bacterial adhesion in host colonization . Biochem Soc Trans   2016 ; 44 : 1571 – 80 .

Google Scholar

Bucior I , Mostov K , Engel JN.   Pseudomonas aeruginosa-mediated damage requires distinct receptors at the apical and basolateral surfaces of the polarized epithelium . Infect Immun   2010 ; 78 : 939 – 53 .

Dai CH , Gan LN , Qin WU  et al.    Use of fluorescence quantitative polymerase chain reaction (PCR) for the detection of Escherichia coli adhesion to pig intestinal epithelial cells . Pol J Vet Sci   2016 ; 19 : 619 – 25 .

Plotkowski MC , Chevillard M , Pierrot D  et al.    Differential adhesion of Pseudomonas aeruginosa to human respiratory epithelial cells in primary culture . J Clin Invest   1991 ; 87 : 2018 – 28 .

Martinez-Perez M , Perez-Jorge C , Lozano D  et al.    Evaluation of bacterial adherence of clinical isolates of Staphylococcus sp . using a competitive model: an in vitro approach to the “race for the surface” theory . Bone Joint Res   2017 ; 6 : 315 – 22 .

Van Ewijk BE , Wolfs TF , Aerts PC  et al.    RSV mediates Pseudomonas aeruginosa binding to cystic fibrosis and normal epithelial cells . Pediatr Res   2007 ; 61 : 398 – 403 .

Hendricks MR , Lashua LP , Fischer DK  et al.    Respiratory syncytial virus infection enhances Pseudomonas aeruginosa biofilm growth through dysregulation of nutritional immunity . Proc Natl Acad Sci U S A   2016 ; 113 : 1642 – 7 .

Shteindel N , Yankelev D , Gerchman Y.   High-throughput quantitative measurement of bacterial attachment kinetics on seconds time scale . Microb Ecol   2019 ; 77 : 726 – 35 .

Hu J , Han H , Li X , Zhang  et al.    Human embryonic kidney 293 cells: a vehicle for biopharmaceutical manufacturing, structural biology, and electrophysiology . Cells Tissues Organs   2018 ; 205 : 1 – 8 .

Newman JW , Floyd RV , Fothergill JL.   The contribution of Pseudomonas aeruginosa virulence factors and host factors in the establishment of urinary tract infections . FEMS Microbiology Letters   2017 ; 364 ( 15 ):fnx124. https://doi.org/10.1093/femsle/fnx124 .

Gardner J , Webster A.   Accelerating innovation in the creation of biovalue: the cell and gene therapy catapult . Sci Technol Human Values   2017 ; 42 : 925 – 46 .

Moradali MF , Ghods S , Rehm BH.   Pseudomonas aeruginosa lifestyle: a paradigm for adaptation, survival, and persistence . Front Cell Infect Microbiol   2017 ; 7 : 39 .

Rossi E , La Rosa R , Bartell JA  et al.    Pseudomonas aeruginosa adaptation and evolution in patients with cystic fibrosis . Nat Rev Microbiol   2021 ; 19 : 331 – 42 .

Hasannejad-Bibalan M , Jafari A , Sabati H  et al.    Risk of type III secretion systems in burn patients with Pseudomonas aeruginosa wound infection: a systematic review and meta-analysis . Burns   2021 ; 47 : 538 – 44 .

Khan ID , Basu A , Kiran S  et al.    Device-Associated Healthcare-Associated Infections (DA-HAI) and the caveat of multiresistance in a multidisciplinary intensive care unit . Med J Armed Forces India   2017 ; 73 : 222 – 31 .

Badal D , Jayarani AV , Kollaran MA  et al.    Pseudomonas aeruginosa biofilm formation on endotracheal tubes requires multiple two-component systems . J Med Microbiol   2020 ; 69 : 906 – 19 .

Thi MTT , Wibowo D , Rehm BHA.   Pseudomonas aeruginosa biofilms . IJMS   2020 ; 21 : 8671 .

Held K , Ramage E , Jacobs M  et al.    Sequence-verified two-allele transposon mutant library for Pseudomonas aeruginosa PAO1 . J Bacteriol   2012 ; 194 : 6387 – 9 .

Ortori CA , Dubern JF , Chhabra SR  et al.    Simultaneous quantitative profiling of N-acyl-L-homoserine lactone and 2-alkyl-4 (1 H)-quinolone families of quorum-sensing signaling molecules using LC-MS/MS . Anal Bioanal Chem   2011 ; 399 : 839 – 50 .

Monych NK , Turner RJ.   Multiple compounds secreted by Pseudomonas aeruginosa increase the tolerance of Staphylococcus aureus to the antimicrobial metals copper and silver . Msystems   2020 ; 5 : e00746 - 20 .

Banin E , Vasil ML , Greenberg EP.   Iron and Pseudomonas aeruginosa biofilm formation . Proc Natl Acad Sci U S A   2005 ; 102 : 11076 – 81 .

Shteindel N , Gerchman Y.   Pseudomonas aeruginosa mobbing-like behavior against Acanthamoeba castellanii bacterivore and its rapid control by quorum sensing and environmental cues . Microbiol Spectr   2021 ; 9 : e00642 - 21 .

Ribet D , Cossart P.   How bacterial pathogens colonize their hosts and invade deeper tissues . Microbes Infect   2015 ; 17 : 173 – 83 .

Bucior I , Pielage JF , Engel JN.   Pseudomonas aeruginosa pili and flagella mediate distinct binding and signaling events at the apical and basolateral surface of airway epithelium . PLoS Pathog   2012 ; 8 : e1002616 .

Beaussart A , Baker AE , Kuchma SL  et al.    Nanoscale adhesion forces of Pseudomonas aeruginosa type IV pili . ACS Nano   2014 ; 8 : 10723 – 33 .

Curutiu C , Iordache F , Lazar V  et al.    Impact of Pseudomonas aeruginosa quorum sensing signaling molecules on adhesion and inflammatory markers in endothelial cells . Beilstein J Org Chem   2018 ; 14 : 2580 – 8 .

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The membrane microenvironment regulates the sequential attachment of bacteria to host cells

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Pathogen attachment to host tissue is critical in the progress of many infections. Bacteria use adhesion in vivo to promote colonization and regulate the deployment of contact-dependent virulence traits. To specifically target host cells, they decorate themselves with adhesins, proteins that bind to mammalian cell surface receptors. One common assumption is that adhesin-receptor interactions entirely govern bacterial attachment. However, how adhesins engage with their receptors in an in vivo -like context remains unclear, in particular under the influence of a heterogeneous mechanical microenvironment. We here investigate the biophysical processes governing bacterial adhesion to host cells using a tunable adhesin-receptor system. By dynamically visualizing attachment, we found that bacterial adhesion to host cell surface, unlike adhesion to inert surfaces, involves two consecutive steps. Bacteria initially attach to their host without engaging adhesins. This step lasts about one minute during which bacteria can easily detach. We found that at this stage, the glycocalyx, a layer of glycosylated proteins and lipids, shields the host cell by keeping adhesin away from their receptor ligand. In a second step, adhesins engage with their target receptors to strengthen attachment for minutes to hours. The active properties of the membrane, endowed by the actin cytoskeleton, strengthen specific adhesion. Altogether, our results demonstrate that adhesin-ligand binding is not the sole regulator of bacterial adhesion. In fact, the host cell’s mechanical microenvironment relatively strongly mediated host-bacteria physical interactions, thereby playing an essential role in the onset of infection.

Introduction

In the wild, bacteria predominantly live associated with surfaces. Their sessile lifestyle confers fitness advantages such as protection from predation and improved access to nutrients 1 . In the context of host colonization, the transition between planktonic and sessile lifestyles plays a functional role in mediating host-microbe interactions. Indeed, attachment to host tissue, more specifically to cells, is often a critical first step towards infection or commensalism 2 , 3 . As a result, the dynamics of attachment of single bacteria to host cells can dramatically influence the outcome of infection or regulate host-microbiota homeostasis.

Bacterial adhesion to abiotic materials greatly contributes to biofouling and contamination of indwelling medical device. Multiple physicochemical properties of the surface mediate adhesion to an inert substrate, including charge, hydrophobicity and conditioning 4 . In addition, mechanical properties of the material such as stiffness and surrounding fluid flow regulate attachment strength and dynamics 5 – 7 . This understanding of adhesion to abiotic materials provides us with rudimentary insights on adhesion to biological tissue. However, the physical and biological complexity of biotic surfaces remains overlooked when making the analogy between living and inert materials. The surface of host mammalian cells is composed of a soft lipid bilayer densely packed with surface proteins 8 . In addition, it is an active surface, permanently rearranging itself under the action of force-generating structures such as the cytoskeleton. Finally, in contrast with abiotic adhesion, bacterial attachment to host cell involves specific molecular interactions 3 . As a result, the analogy between biotic and abiotic adhesion may overlook critical physical and biological regulators.

Pathogens and commensals alike express at their surfaces proteins that specifically bind to host membrane ligands. These cell type-specific adhesins promote tissue tropism during infection or colonization 9 . These can be classified in categories that reflect their structure and molecular mechanism of display. Adhesins from the autotransporter family are exposed immediately near the bacterial cell envelope 10 . Their structure includes an outer-membrane beta-barrel scaffold and an inner alpha helix that holds a passenger domain. This domain often includes its ligand-binding domain 11 . Intimin is an autotransporter adhesin from enteropathogenic and enterohemorrhagic Escherichia coli that mediates attachment to gut epithelial cells. Intimin binds to Tir host membrane receptors that have been preemptively translocated by the bacterium 12 . Similarly, Yersinia pseudotuberculosis uses invasin, which binds to beta integrins present at the host cell membrane, to initiate host cell entry during infection 13 .

Little is known about how the host microenvironment mediates the interaction between adhesins and their receptors. Measurement of bacterial internalization suggest that the membrane fluidity of host cells slightly improves bacterial adhesion 14 . At the molecular level of single adhesins, force spectroscopy measurements have helped characterize bond mechanics both on abiotic materials and on live cells 15 . These have helped precisely identify exotic adhesin behavior such as the formation of catch bonds, which strengthen under an applied tensile force. The fimbriae tip adhesin FimH notoriously forms catch bond, allowing uropathogenic E. coli to strengthen adhesion in the urinary tract under flow 16 – 18 . Studies of adhesion, including catch bonds, have vastly focused on detachment of bacteria, where adhesion force balances externally applied mechanical load 19 . However, by focusing on the behavior of adhesins, most studies have overlooked the dynamic and physical regulation of bacterial attachment to mammalian cell surfaces.

The structure and biochemistry of adhesin-receptor interactions has been intensively characterized for a subset of adhesins 2 , 3 , 20 . One common resulting hypothesis is that the attachment behavior of single bacteria to their target host cell entirely reflects the molecular adhesin-receptor kinetics and affinity 2 , 9 . Does this assumption hold true when considering that adhesin and receptor must come together in a complex biophysical microenvironment? To answer this question, we combined synthetic and biophysical approaches to investigate bacterial adhesion to host cells. We engineered autotransporters for heterologous display of a synthetic adhesin on a non-pathogenic strain of E. coli 21 . We found that the specific attachment of bacteria to host cells occurs in two consecutive steps. A first step is unspecific, taking place within the first few seconds following contact. This is followed by the onset of specific adhesion yielding near irreversible attachment on longer timescale. We found that biomechanical properties of the host cell, including membrane rearrangement, flow and glycocalyx, regulate each of the adhesion steps. Overall, we show that the biomechanical microenvironment of host tissues strongly regulates the adhesion behavior of bacteria to their target cells, implicating that this process cannot be solely reduced to adhesin-receptor interactions.

Synthetic adhesion to characterize bacterial attachment to host cells

To systematically probe bacterial adhesion to host cells without relying on virulence factors, we engineered an exogenous adhesin in E. coli and cognate receptor in HeLa cells ( Fig. 1A ). As adhesin, we display an anti-GFP nanobody (camelid single-domain variable heavy chain, VHH) using a truncated intimin scaffold 21 , 22 . The N-terminal domain consists in a beta-barrel associated with the bacterial outer membrane, through which spans an alpha helix displaying the synthetic passenger domain (Fig. S1A). Two out of three immunoglobulin-like structures of the passenger domain of wild-type intimin are replaced with an HA tag and VHH domain. We placed the construct under a tetracycline-inducible promoter. By staining with recombinant GFP and quantifying the fluorescence signal at the surface of single bacteria induced with increasing tetracycline concentrations, we generated titration curves allowing us to fine-tune the density of displayed VHH (Fig. S1D,E). To display receptor GFP ligand for the synthetic adhesin at the surface of HeLa, we displayed a doxycycline-inducible GFP fusion to a CD80 receptor anchored in the plasma membrane (Fig. S1B) 23 . Direct visualization of the fluorescence signal localized at the cell plasma membrane can confirm and help quantify receptor density (Fig. S1F).

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(A) Schematic of the synthetic adhesin-receptor system. E. coli cells display nanobody targeting GFP (VHH) fused to a truncated intimin autotransporter scaffold. HeLa display GFP receptors by fusion with the membrane-anchored CD80 scaffold. (B) In a mixed population of GFP+ (green) and GFP-(purple) HeLa cells, E. coli (orange, indicated with white arrows) specifically binds to GFP+ cells. Actin stained with phalloidin (purple). Scale bars: 10 μm (main) and 5 μm (inset). (C) Bacterial count per HeLa cell increases with E. coli nanobody density. E. coli expressing VHH at low density, or expressing VHH at high density but preincubated with soluble GFP only rarely bind to HeLa displaying GFP. (D) Dynamic visualizations of bacterial adhesion to HeLa cells under flow under flow allows to simultaneously monitor attachment and detachment events at multiple timescales. (E) Bacterial attachment efficiency is independent of VHH density and GFP display. High speed confocal imaging at 1 frame per second highlights bacterial populations that detach rapidly after contact. We considered bacteria as attached if they stayed on the HeLa surface for more than 2 s. Scale bar 2 μm. (F) We constructed residence time distributions using long timescale tracking of attached bacteria (1 h). Bare E. coli and E. coli displaying low and high VHH levels have largely different residence time distributions. We fit these distributions using the sum of two exponentials highlight two characteristic timescales τ transient and τ res (right illustrative graph). The single exponentials are shown in dashed green and blue and their sum is the continuous red line. (G) The model parameter τ transient is independent of the adhesin displayed. (H) In contrast, the characteristic residence time τ res increases with nanobody density. Statistical tests: one-way ANOVA followed by Dunnett’s post hoc test (**** P<10 −4 , * P<0.05).

We transiently transfected HeLa displaying CD80-anchored GFP, leading to a heterogeneous population of GFP positive and negative cells. We then mixed in E. coli with a high surface density of VHH ( E. coli VHH) with HeLa GFP whose respective adhesin and receptor where induced separately. After washing, we visualized the co-culture by confocal microscopy. We observed that bacteria bound to GFP-positive HeLa, but not to GFP-negative cells ( Fig. 1B ). This indicated that the synthetic system is specific, validating it as a model of bacterial adhesion. As a result, we generated a stable and clonal doxycycline-inducible HeLa GFP-display cell line (HeLa GFP) and grew cultures of this line in microchannels to investigate adhesion in flow conditions. We diluted bacteria in mammalian cell culture medium and loaded them on a syringe pump for flow control. We injected the bacterial suspension in the microchannel covered with Hela GFP. After one hour under moderate flow, we imaged cells in the channel by confocal microscopy and quantified the number of bacteria per mammalian cell. The bacterial counts per HeLa GFP cell was larger when both constructs were induced compared to uninduced conditions ( Fig. 1C ). Pre-incubation of E. coli VHH with recombinant soluble GFP decreased the bacterial count per HeLa GFP back to the non-induced condition ( Fig. 1C ). Therefore, this system yields selective and dose-dependent bacterial adhesion of VHH-displaying bacteria to GFP-displaying HeLa both in static and flow conditions. Our initial characterization overall demonstrates that in tandem, E. coli -VHH and HeLa-GFP represent a realistic, tunable model for specific microbial adhesion to host mammalian cells.

Bacterial attach to host cells in two successive steps

Our initial results showed that the number of bacteria attached to host cells depends on the induction levels of both VHH adhesin and GFP receptor ( Fig. 1B,C ). We wondered whether this was due to changes in the number of bacteria attaching or detaching from the host cell surface ( Fig. 1D ). This question motivated us to inspect the dynamics of attachment to HeLa cells at the single bacterium level. We tracked attachment and detachment of single bacteria over the course of 1 h (Movie S1). These visualizations helped us identify two classes of attachment behaviors. First, a large proportion of bacteria were only visible on single frames, indicating that they were in contact with the membrane for a short time. Another population of cells stayed attached for much longer times. We were intrigued by this dichotomy in adhesion behaviors and performed multiscale imaging to characterize each step.

To inspect short timescale attachment events, we performed fast confocal imaging of attachment (1 frame per second). We found that a large proportion of bacteria only stayed on the membrane for about two seconds (one or two frames, Movie S2). We then quantified the proportion of bacteria that attached to the host surface for more than two seconds relative to the total number of contacts, which we call attachment efficiency ( Fig. 1E ). We found that the attachment efficiency was in average only 7% when both VHH and GFP were induced. We then compared this attachment efficiency between adhesin-receptor conditions. Surprisingly, we found that neither the presence of VHH adhesins nor of GFP receptors influenced the attachment efficiency ( Fig. 1E ). This suggests that this early stage is not specific.

We thus speculated that the adhesin-receptor interactions regulate bacterial attachment on a longer timescale. To test this hypothesis, we timed single bacteria residing the surface of host cells during a 1 hour-long movie (Movie S1). We thus built inverse cumulative residence time distributions ( Fig. 1F ). We found that these distributions had exponential-like decays, which we could fit to the sum of two exponential functions ( Fig. 1F and material and methods). This highlighted two characteristic timescales over which bacteria detached from the surface. The shortest timescale is on the order of 100 seconds, and was nearly identical between conditions ( Fig. 1G ). The longest timescale τ res , associated with the second exponential, showed large variations between VHH or GFP configurations ( Fig. 1H ). We measured a 10-fold increase in τ res when bacteria displayed a high VHH density compared to bacteria displaying an empty intimin scaffold (no VHH). In addition, we measured a 3.5-fold decrease when we did not induce GFP on HeLa cells. These results implicate that adhesin-receptor interactions only materialize over minutes. As a comparison to typical association rates, we estimated the on- and off-rates of adhesin-ligand based on known kinetics constants of VHH-GFP 24 . Interestingly, the off-rate of VHH reflects a characteristic time of 6900 s, which is of the same order of magnitude as our τ res measurements. For an arbitrary GFP concentration of 1 μM, the on-rate yields a reaction time on the order of 1 s, two order of magnitude shorter than our measurements. This suggests that other factors mediate the first adhesion step, before adhesins engage with their ligand. In summary, we highlighted that bacteria specifically attach to host cells by going through an initial non-specific attachment followed by adhesin-receptor docking, thereby promoting long lasting physical contact.

We then tested the contributions of biochemical properties of the adhesin in regulating attachment. We swapped the adhesin to two other VHH sequences coding for anti-GFP nanobodies of different affinities ( K D ) and kinetic rates (k on and k off ) 25 . We checked that their expression levels were unaffected using anti-HA FITC-labeled antibodies (Fig. S2i). We first verified that the fusion to intimin did not affect K D . Titrating these alternate VHH forms on E. coli with GFP yielded K D matching their in vitro measurements performed with soluble recombinant proteins (Fig. S2ii,iii) 24 , 25 . We thus performed adhesion experiments on HeLa GFP under flow with E. coli expressing the alternate VHH forms. We observed a slight positive correlation between bacterial load per HeLa and VHH affinity across three orders of magnitude of K D and two order of magnitude of k off (Fig. S2 and S3A). Consistent with its non-specific nature, the attachment efficiency was independent of the affinity of the nanobody to GFP (Fig. S3B). On the longer timescale, we measured higher τ res and a statistically significant increase in the pre-exponential factor C res at higher affinities (Fig. S3C,D), explaining the differences in the bacterial load. Altogether, the dependence of the specific adhesion step on adhesin biochemistry was surprisingly weak compared to the changes induced by adhesin expression levels ( Fig. 1H and Fig. S3C).

Bacteria attach to abiotic surfaces in a single specific step

We suspected that the complex of physical microenvironment the host cell membrane plays a role in either of the two successive steps of attachment. To provide additional insights on these factors, we compared the specific adhesion of E. coli to the surface of an abiotic material with the one on mammalian cells ( Fig. 2A ). We engineered specific adhesion to glass by conjugating receptors to a coverslip substrate. We conjugated N-terminally His-tagged recombinant GFP to nitrilotriacetic acid (Ni-NTA) functionalized glass, on which we bonded elastomeric microfluidic channels (see material and methods). We monitored the dynamics of specific adhesion to abiotic surface by flowing a bacterial suspension in the GFP-coated microchannel. We observed bacteria almost exclusively attaching to the GFP-coated areas, thereby validating adhesion specificity (Fig. S4A-C and Movie S3). These experiments highlighted a blatant difference with mammalian cells: there were ten times more bacteria attached to the GFP-coated glass surface than on HeLa cells ( Fig. 2B ). This difference was strictly dependent on VHH-GFP interactions as bacteria only sparsely attached to untreated glass, or to glass coated with mKate2, a red fluorescent protein that does not bind VHH (Fig. S4D).

(A) (Top) Controlled GFP-functionalized coverslips permits visualization of specific adhesion to hard, abiotic surface and quantitative comparison with adhesion to mammalian cells. (Bottom) Representative confocal microscopy images of bacterial binding to GFP-coated coverslips (left) and HeLa-GFP (right). Scale bar: 10 μm. (B) Final bacterial count per cell area is about 10-fold larger on GFP-coated coverslips than HeLa in the presence of VHH. (C) Bacterial attachment efficiency is higher on GFP-coated coverslips than HeLa in the presence of VHH. (D) The characteristic residence time τ res shows the VHH-dependent binding to coverslips is stronger than to HeLa. (E) Relative contribution of short and long timescale exponential fits shows that 95% of E. coli VHH strongly bind to GFP-coated coverslips. Statistical tests: two-way ANOVA and Sidak post-hoc test (**** P<10 −4 , *** P<0.001, ** P<0.01, * P<0.05).

To further characterize the pronounced difference in adhesion between abiotic and biotic surfaces, we focused on attachment/detachment dynamics. We compared the early attachment efficiencies and residence times of bacteria on glass with the ones on HeLa cells. First, we found that about 50% of E. coli VHH stayed attached to the GFP-coated glass surface upon initial contact, in contrast with the 7% of bacteria remaining on HeLa GFP cells ( Fig. 2C ). This largely contributed to the differences in bacterial accumulation at the end of the experiment. In addition, the characteristic residence time of E. coli VHH on glass was more than eight times longer than on HeLa ( Fig. 2D ). This characteristic time was also much longer than the duration of our visualizations so that most bacteria can be considered irreversibly attached to glass. Finally, on the longer timescale, very few bacteria transiently bound to coverslips, as highlighted by the relative contribution of τ transient ( Fig. 2E ). This further supports a scenario where adhesin and receptor engage rapidly and efficiently when an abiotic surface supports the receptors.

In summary, specific adhesion to an abiotic surface is controlled by early attachment events within the first few seconds of surface encounter, consistent with in vitro reaction rates. Successful attachment beyond this step leads to nearly irreversible surface association. Thus, a single specific step mediates attachment on abiotic surfaces, while phenomena at both short and long timescales regulate specific attachment to host cells.

Host cell membrane mechanics regulate bacterial adhesion

Given the differences in material properties between inert and living substrates, we hypothesized that the mechanical microenvironment of host cells may play a key role in regulating attachment. Following this intuition, we investigated the role of cells mechanics in the process of adhesion to host cells. Host cell mechanics depend on the intrinsic membrane bilayer properties but also on emergent properties provided by the actin cytoskeleton.

We observed that bacteria attached to HeLa accumulate GFP at their surface, as if they were embedded into membrane invaginations ( Fig. 3Ai-ii ). Given the role of the cytoskeleton in the shape and mechanics of eukaryotic cell membranes, we hypothesized that actin could play a role in bacterial attachment. To first explore this possibility, we visualized the actin cytoskeleton of bacteria-bound cells using fluorescent phalloidin staining. The actin density increased around individual attached bacteria, indicating a potential morphological remodeling of the membrane upon attachment ( Fig. 3Aiii-iv ). Our GFP display construct is based on a truncation of the CD80 receptor that is overexpressed in macrophages with notoriously increased actin remodeling. To exclude the possibility that remodeling is an artefact of the C-terminal CD80 anchor, we fused GFP to a glycosylphosphatidylinositol (GPI) membrane anchor devoid of cytosolic signaling components (Fig. S1C) 26 . There, we could also observe a similar actin remodeling and membrane surrounding bacteria ( Fig. 3B and Fig S5A,B). The membrane remodeling occurred within minutes, on a similar timescale as the GFP uptake (Movie S4,5 Fig. S5C,D). Actin-dependent membrane remodeling could thus increase the contact area between bacteria and host cell, stimulating adhesion-receptor interactions and consequently increasing adhesion strength.

(A) Actin rearranges around attached bacteria. After static incubation with E. coli VHH (orange), HeLa displaying GFP with a CD80 anchor (green) were stained for actin (purple). Scale bar: 5 μm. (B) Bacteria promote actin embeddings in the absence of any cytosolic component in the mammalian cell. After static co-culture with E. coli VHH (red), HeLa displaying GFP with a glycosylphosphatidylinositol (GPI), which does not harbor any cytosolic signaling domain, also shows strong actin remodeling around attached bacteria. (C) HeLa treatment with the actin polymerization inhibitor cytochalasin D (cytoD) reduces the bacterial count per HeLa cells. (D) Bacterial attachment efficiency is independent of actin polymerization. (E) The characteristic residence time τ res decreases in the presence of cytochalasin D at high VHH density. Statistical tests: two-way ANOVA and Sidak post-hoc test (**** P<10 −4 , ** P<0.01).

We further tested the role of membrane remodeling in bacterial attachment by employing cytochalasin D, a drug inhibiting actin polymerization 27 . We measured an 8-fold reduction in E. coli VHH attachment on treated cells compared to the untreated control ( Fig. 3C ). Inhibiting actin polymerization did not decrease the attachment efficiency of bacteria at early timescales ( Fig. 3D ). However, bacterial residence time was decreased in presence of the drug ( Fig. 3E ). This difference was most dramatic for higher VHH densities. This suggests that membrane remodeling upon attachment takes place on the minute timescale, thereby stabilizing adhesin-receptor interactions.

Following the dependence of attachment on cytoskeletal density, which could affect membrane stiffness at the microscale, we investigated the contributions of intrinsic membrane mechanics on attachment 14 . We used a chemical approach where we modified membrane composition by growing HeLa cells in the presence of lipids known to modulate fluidity and stiffness. We first incubated cells with saturated fatty acids (margaric acid, MA) or with polyunsaturated fatty acids (eicosapentaenoic acid, EPA), which respectively increase and decrease membrane bending stiffness 28 . We could not detect changes in any of the attachment/detachment metrics compared to a negative control (Fig. S6A-D). We separately manipulated membrane fluidity by controlling HeLa’s membrane cholesterol content. We enriched the membrane with cholesterol or depleted cholesterol with methyl-β-cyclodextrin 29 . Here we measured a slight increase of bacterial load with decreasing fluidity (Fig. S6E). However, neither attachment efficiency nor residence times where affected by fluidity (Fig. S6F-H). Altogether, our results highlight that the intrinsic membrane mechanical properties such as bending stiffness and fluidity only slightly influence adhesion, while the host cytoskeleton plays an important active function in reinforcing specific bacterial attachment.

The glycocalyx shields the host from receptor-specific bacterial adhesion

Membrane mechanics regulate how bacteria engage in specific adhesion to hosts cell on timescale of minutes. Still, membrane mechanical properties had little effect on the non-specific adhesion step, which differed so much between glass and cells, as the attachment efficiencies upon membrane and cytoskeletal perturbations remained below 10% ( Fig. 3D and Fig. S6B,F). We thus still wondered why such a small proportion of bacteria could commit to specific adhesion upon encountering the host cell surface.

We reasoned that other mechanical components of the host cell surface could play a role in limiting bacterial adhesion. We thus hypothesized that the glycocalyx, a dense layer of glycoproteins and glycolipids that decorates the surface of most mammalian cells, could limit attachment. To test this, we investigated the role of the host glycocalyx in the dynamics of bacterial adhesion. We cultured HeLa cells with a deglycosylating mix of enzymes, thereby promoting its degradation ( Fig. 4A ) 30 , 31 . We confirmed specific enzymatic activity in mammalian medium by digesting fetuin, a N- and O-glycosylated control protein (Fig. S7). We then tracked bacterial adhesion dynamics at the surface of deglycosylated HeLa cells, which showed a dramatic effect. First, there was six times more bacteria attached to deglycosylated cells compared to their native, untreated state ( Fig. 4B ). The bacterial density on deglycosylated cells reached values close to the ones measured on glass ( Fig. 2B ). We further examined the specific contributions of the glycocalyx in attachment dynamics by comparing attachment efficiency and residence time distributions to the native state. Consistent with our hypothesis, we found that bacteria remained attached twice as efficiently to deglycosylated cells compared to untreated cells in a VHH-dependent manner ( Fig. 4C ). Deglycosylation only slightly increased the characteristic residence time, both in the presence and absence of VHH ( Fig. 4D ). Altogether, our data indicates that the mammalian glycocalyx shields the host cell membrane from direct engagement of bacterial adhesins to target receptors, thereby non-specifically limiting bacterial attachment.

(A) Enzymatic deglycosylation of HeLa surface proteins increases bacterial binding. The right image shows two deglycosylated HeLa cells covered by E. coli VHH while the negative control in otherwise identical conditions has low bacterial count. Scale bar: 10 μm. (B-D) Comparison of bacterial adhesion dynamics between untreated cells (native) and deglycosylated cells (deglyco). (B) Final E. coli VHH count per HeLa cell is higher in cells lacking glycocalyx. (C) Glycocalyx removal increases the attachment efficiency of E. coli VHH. (D) Comparison of the characteristic residence time τ res with or without deglycosylation mix. Statistical tests: two-way ANOVA and Sidak post-hoc test. (** P<0.01).

Flagella and flow counteract the glycocalyx shield

Beyond simple short-range adhesins such as the ones belonging to the class of autotransporters, bacteria often display surface extensions such as flagella and fimbriae, sometimes capped with adhesins. As a result, we wondered whether such structures could help overcome the physical glycocalyx barrier by reaching through, thereby promoting the first step of adhesion. We thus explored how surface filaments could play a role in the early adhesion step. We first compared the binding of flagellated and non-flagellated bacteria to glycosylated HeLa GFP. We could not distinguish the bacterial numbers between flagellated and non flagellated strains at the end of the experiments (Fig. S8A). However, the details of attachment dynamics revealed that the flagellum mediates a tradeoff between unspecific and specific adhesion. On the one hand, we observed that flagellated E. coli have higher attachment efficiency ( Fig. 5A ). This shows that flagella promote short timescale unspecific attachment. On the other hand, the characteristic residence time of flagellated E. coli was more than twice shorter than its non flagellated counterpart ( Fig. 5B ). Consistent with this, the transient characteristic residence time was similar between conditions but the pre-exponent factor C res had significantly decreased weight in our exponential fits, reflecting a high number of bacteria transiently binding and fewer bacteria strongly binding ( Fig. 5C and Fig. S8B and Movie S6). Altogether, flagella mediate a tradeoff in adhesion, increasing early commitment while decreasing subsequent specific attachment.

(A) Schematic of the experimental setup. Flagellated E. coli VHH (blue) were compared to non-flagellated E. coli VHH. E. coli VHH attachment efficiency is increased in the presence of flagellum in flow. (B) The presence of flagella decreases the characteristic residence time in flow. (C) Comparison of the pre-exponential factor of the characteristic transient binding time τ transient in the presence or absence of flagella shows that the proportion of bacteria strongly binding to HeLa GFP is lower with flagella. (D) We measured the attachment dynamics of E. coli VHH in low, intermediate (int) and high flow (respective shear stress: 0.05, 0.15 and 0.5 Pa). Bacterial attachment efficiency increases with flow intensity. (E) The characteristic residence time τ res increases with flow intensity. In (A-C), statistical significance was calculated by two-tailed unpaired t-test (** P<0.01, * P<0.05). In (D,E) significance was calculated by one-way ANOVA followed by Dunnett’s post hoc test (* P<0.05, **** P<10 −4 ).

Upon contact of a bacterium with a host cell, the glycocalyx blocks attachment by sterically shielding the membrane. This short timescale interaction does not involve short-range adhesins nor mammalian membrane receptors. Strong shear forces and bacterial flagellum can increase the transient binding efficiency, in part by attenuating the glycocalyx shield. The bacterium subsequently binds engages adhesins onto host receptors to promote specific adhesion. This increased adhesin density, affinity to the receptor ligand, flow, and actin polymerization promote the specific adhesion step, while the flagella and soluble antigen repress it promoting bacterial detachment.

Finally, we wondered whether fluid flow could balance the effect of the glycocalyx. Typically, hydrodynamic forces positively select for single bacteria whose adhesion force exceeds shear force. In the context of adhesion to host cells and based on molecular dynamic simulations, we suspected that flow could generate shear force that deform the ~100 nm thick glycoprotein layer, thereby reducing shielding 32 . Given these two flow-induced effects are antagonistic, we wondered how their combined contributions would ultimately affect bacterial attachment. We thus performed adhesion experiments of E. coli VHH to HeLa GFP at three different flow regimes. We applied flow rates that generated shear stress of 0.05, 0.15 and 0.5 Pa at the channel centerline. These stresses respectively generate 0.1, 0,3 and 1 pN hydrodynamic forces on single bacteria (assuming a bacterium is 2 μm long, 1 μm wide) 33 . We measured attachment efficiency and residence times, which are normalized metrics, that is they do not depend on the influx of bacteria in the channel.

The attachment efficiencies increased with shear stress, from 7% at low shear up to 31% at high shear. This indicates that flow promotes the unspecific adhesion within the few seconds after contact ( Fig. 5D ). On the timescale of minutes where adhesins engage to their GFP receptors, the characteristic residence times of bacteria increased strongly with shear stress, up to two orders of magnitude ( Fig. 5E ). Despite longer residence time and higher attachment efficiency in strong flow, we could not measure clear changes in absolute bacterial load per HeLa cell compared to weaker flows. We could attribute this to an unexpected decrease in the absolute number of bacterial contacts per mammalian cells with increasing flows, indicating that bacteria are less likely to encounter the host cells membrane under strong shear (Fig. S8C,D). Altogether, our results suggest that higher flows improve bacterial attachment in two ways. First, stronger flow promotes early attachment by counteracting the glycocalyx. Second, increased flow further engages adhesins to their receptors.

To infect or stably colonize their hosts, bacterial pathogens and commensals attach to the surface of biological tissues 34 . Adhesins are the major ingredient of bacterial adhesion to host cells. By binding to target receptor moieties at the surface of host cells, they confer adhesion specificity. We investigated how bacteria adhere to host cells by leveraging a tunable synthetic system comprising an adhesin (VHH) and a receptor (GFP). This system had been engineered for therapeutic VHH library screening and has been applied to the study of multicellular self-organization of bacterial populations 21 , 22 . We here repurposed it to investigate bacterial attachment to host cells while controlling adhesin expression and binding strength without affecting host viability.

We leveraged the versatility of the VHH-GFP system to perform a careful investigation of the dynamics adhesion. We first identified an overlooked temporal aspect of bacterial attachment to host cells, where a two-step sequence leads to specific attachment. After contact, bacteria attach non-specifically to host cells for not more than a minute. Bacteria subsequently engage adhesins to their receptors on a timescale consistent with adhesin-ligand rupture kinetics, in our case for minutes to hours. Sequential adhesion to host cells contrasts with the single specific step governing adhesion to abiotic surfaces.

Then, the VHH display system allowed us to test the contributions of adhesin density and binding kinetics on attachment. While VHH density promoted specific attachment, the adhesin affinity and reaction rates ended up being a surprisingly weak regulator of attachment and detachment. This could be explained by the fact after engaging several adhesins of relatively high affinity, bacterial overall avidity rapidly predominates over the affinity of individual adhesins 35 . In contrast, we found that mechanical factors of the host environment strongly regulate each of the stages of adhesion. The host glycocalyx, a layer of glycans bound to glycolipids and surface glycoproteins, inhibits the first adhesion step by physically shielding the host membrane surface. Then, we found that the host cell actin cytoskeleton shapes the membrane around attached bacteria, thereby improving specific adhesion. Membrane embedded bacteria could thus engage VHH to additional GFP receptors, thus increasing overall adhesion strength. We propose that a passive ratchet mechanism triggers the actin-dependent membrane encapsulation of bacteria 36 , 37 . The limited effect of membrane fluidity and bending stiffness on attachment might be explained by the fact that at the scale of a bacterium, cell stiffness is mainly driven by the underlying cytoskeleton. Overall, cell plasticity, but not intrinsic membrane stiffness, are important regulators of bacterial adhesion.

Surprisingly, we found that fluid flow improved attachment of E. coli VHH to HeLa GFP, both during unspecific and specific stages of adhesion. This was unexpected because fluid flow, by virtue of the shear force it generates, tends to remove bacteria from their attachment surface 33 . By shearing the glycocalyx, flow could improve the access of the bacterium to the cell membrane, thereby increasing unspecific attachment efficiency 32 . Concerning the subsequent specific step, our observations are reminiscent of flow-enhanced adhesion as a result of the formation of catch bonds 38 . However, VHH-GFP do not form catch bonds 39 . We hypothesize that flow improves specific adhesion via an indirect mechanism. For example, shearing of a bound bacterium generates tension onto the membrane, thereby stimulating actin recruitment 40 . This in turn engages more receptors, ultimately strengthening attachment. We finally note that as shear stress increases, more stringent selection for strongly attached cells could lead to the observed enhanced attachment. As a results, we cannot rule out that shear removes loosely attached bacteria at a rate that is faster than the temporal resolution of our imaging. All things considered, we demonstrated that the dependence of bacterial attachment on flow-generated forces cannot be simply extrapolated from a physically simplified behavior of a bacterium attached to a hard inert surface.

Placing our results in the context of infection showcases the breadth of strategies bacteria may have evolve to speed up and strengthen attachment to host tissue. For examples, a subset of pathogens have evolved strategies to overcome the glycoprotein shield. Salmonella Typhimurium actively degrades the glycocalyx during infection, a strategy suspected to promote long lasting attachment 41 . Interestingly, bacterial adhesins in some instances target surface glycans directly rather than proteins, transforming the glycocalyx shield into the target itself 42 . We have shown flagella also favor bacterial release, potentially providing an explanation for flagellum shedding it upon attachment 43 . Also, many adhesins are associated to the tips of slender filaments displayed at bacterium surface, allowing them to reach through the glycocalyx. For example, uropathogenic E. coli binds to mannosylated proteins at the surface of uroepithelial cells displays using the lectin FimH 18 . We expect the early attachment of bacteria expressing these long-range adhesins to be more efficient than autotransporter-based adhesins. Consistent with this, we found that flagella had a positive effect on the early attachment efficiency, even though it does not express specific adhesins.

During the process of infection, bacteria use an arsenal of virulence factors. These are deployed in a timely fashion in response to relevant signals. Synchronizing expression of virulence factors with host cell contact could promote timely deployment 33 . For example, enteropathogenic E. coli transfer the intimin adhesin receptors Tir to gut epithelial cells upon contact 2 . The unspecific first step of adhesion thus offers a window of opportunity to deploy these systems within minutes.

Altogether, we have demonstrated that bacterial attachment to host cells vastly differs from the expected behavior of simple adhesin-receptor interactions. Adhesin biochemistry and the physics of adhesion to inert materials only poorly predict adhesion to mammalian cells. This has therefore important implication in our view of infection. In the current context of a concerning rise in multidrug resistant pathogens, our work provides new insights that could inspire us in developing anti-adhesive therapeutics 3 .

Material and methods

Plasmid cloning strategy and primers sequences are described in supplementary tables S1 and S2, respectively. Cloning was performed by restriction enzymes (NEB) and ligation with T4 ligase (Bioconcept) or by Hi-Fi Gibson assembly (NEB). PCR were performed using Phusion polymerase (Life Technologies) and DNA purification with commercially available kits. Chemically competent XL10Gold (Agilent) were used for transformation.

Cell culture, engineering and induction

HeLa cells were cultured in DMEM (Thermofisher) supplemented with 10% FBS (Life Technologies) at 37°C and 5% CO 2 . Prior to experiments, cells were trypsinized and resuspended in FluoroBrite (Life Technologies) supplemented with 10% FBS and 1% Glutamax (Life Technologies). Cells were seeded at 100,000 cells/mL in 96 well plates or 400,000 cells/mL microchannels (Ibidi μ-Slide VI 0.4) one day prior to experiments. In microchannels, first 30 μL of cell suspension were added. Cells were left to adhere for 5-6 hours, and then reservoirs were filled with 120 μL medium.

Unless stated otherwise, we used HeLa cells displaying a doxycycline-inducible CD80-anchored GFP. To generate a stable cell line, we produced lentiviruses in HEK293T cells. Cells at 50% confluence were co-transfected with pMD2G (Addgene 12259), pCMVR8.74 (Addgene 22036) and pXP340 (Table S1) using Lipofectamine3000 (Life Technologies). Medium was changed at day 1 and lentiviruses were collected at day 2 and 3, separated from cell debris by centrifugation, sterile filtered and added to HeLa cells. Cells were selected with G418 (Chemie Brunschwig) at 300 μg/mL and resistant clones were obtained by limiting dilution in 96 well-plates. The resulting monoclonal cell line (HeLa GFP) were induced overnight with doxycycline (HiMedia) at 300 ng/mL.

HeLa cells transiently expressing GPI-anchored were obtained by lipofection of the plasmid PeGFP_GPI.

Bacterial culture, engineering and induction

E.coli K12 (BW25113) were cultured in LB at 37°C. Bacteria were stably engineered to express cytoplasmic mScarlet using pZA002 for Tn7 insertion 44 . pZA002 consists in a synthetic constitutive promoter upstream of mScarlet ligated into pGRG36 for chromosomal integration. Deletion of the flagellum was performed using the lambda red system and the PCR product using oXP851 oXP852 on E.coli genomic DNA to delete the FliCDST operon 45 (table S2). Flagellated and aflagellated fluorescent E.coli were then electroporated with tetracycline-inducible intimin-based display constructs. pXP383 coding for the display of VHH of medium affinity was used in this study in aflagellated E.coli unless stated otherwise. pXP384 and pXP388 display the VHH of lower and higher affinities, pDSG323 the empty scaffold and selected with kanamycin (Sigma) at 50 μg/mL 22 . To prepare adhesion experiments, early stationary pre-cultures were diluted 1:3000 and induced with sublethal doses of tetracycline (Sigma, 50 or 250 ng/mL) overnight under shaking conditions.

Cell membrane and cytoskeletal perturbation

HeLa were cultured overnight with eicosapentaenoic acid (Cayman) or margaric acid (Sigma) at 150 μM. Water-soluble cholesterol (Sigma) at 1 mg/mL or cholesterol-depleting methyl-β-cyclodextrin (Sigma) at 20 mg/mL was added for 1 hour prior to the experiment. Cytochalasin D (Sigma) at 1 μM was added 5 minutes prior to- and during the experiment.

Attachment with soluble GFP

Soluble recombinant GFP was added to the bacterial suspension at 10 μg/mL 5 min prior to the experiments.

Generation of a Ni-NTA functionalized glass surface for selective protein immobilization

Addition of the Ni-NTA functionality to a glass surface was inspired by existing protocols 46 , 47 . Glass coverslips (#1.5) were placed in a holder and sonicated in acetone for 30 min. The coverslips were then rinsed with MilliQ water, dried with a stream of nitrogen gas and plasma treated for 10 minutes at maximal power (Zepto, Diener electronic). The plasma-treated coverslips were then transferred into 150 mL of 1% (v/v) (3-Aminopropyl)triethoxysilane (APTES) (Sigma-Aldrich) in toluene (Sigma-Aldrich) and stirred for 30 min. The cover slips were then rinsed in 150 mL of toluene for 10 minutes, dried by a stream of nitrogen gas, then baked at 80°C for 45 min. The coverslips were then cooled down with a stream of nitrogen gas and transferred into a 150 mL stirred solution of 2 mg/ mL p-Phenylene diisothiocyanate (PDITC) (Sigma-Aldrich) in 10% (v/v) anhydrous pyridine (Sigma-Aldrich) and 90% (v/v) N,N-dimethylformamide (DMF) (Sigma-Aldrich) for 2 h in darkness. The cover slips were then flushed with 1 volume of absolute ethanol, followed by a wash in acetone for 10 min and drying with a stream of nitrogen gas. Then half the cover slips were laid on a flat surface. We then prepared a solution of 457 mM N,N-Bis(carboxymethyl)-L-Lysine-hydrate (Sigma-Aldrich) in 1 M NaHCO3 (Sigma-Aldrich). 90 μL of the N,N-Bis(carboxymethyl)-L-Lysine-hydrate solution were deposited onto the cover slips, then sandwiched with another coverslip on top, These were incubated overnight at room temperature. The unreacted PDITC was then blocked by immersing the coverslips into a solution of 5mg/ml BSA + 5% ethanolamine in PBS for 30 min. The slides were then washed in 1x PBS for 10 minutes under constant stirring, and transferred into a solution of 1% (w/v) solution of nickel sulfate (NiSO 4 ) for 1 hour under stirring, then washed in 1x PBS for 10 min followed by a second wash in 0.1x PBS for 10 minutes and dried under a stream of nitrogen gas. 50 μL of recombinant GFP protein at 1 mg/mL were deposited onto each coverslip and incubated over 2 days in the dark at 4°C. The slides were again flushed in 1x PBS for 10 minutes followed by a second wash in 0.1x PBS for 10 minutes then dried with a stream of nitrogen.

Visualization

For widefield visualizations, we used a Nikon TiE epifluorescence microscope equipped with a Hamamatsu ORCA Flash 4 camera and an oil immersion 100x Plan APO N.A. 1.45 objective.

For all time-lapses and mammalian cell visualizations, we used a Nikon Eclipse Ti2-E inverted microscope coupled with a Yokogawa CSU W2 confocal spinning disk unit and equipped with a Prime 95B sCMOS camera (Photometrics). For time-lapses, we used a 40x objective with N.A. of 1.15 to acquire z-stacks with 2 μm intervals over 6 μm. Each plane was acquired at low laser power for 200 ms allowing to threshold out free bacteria in flow from bound bacteria. For stained mammalian cell visualizations, we used a 100x oil immersion objective with N.A. of 1.45 to acquire z-stacks with 0.5 μm intervals.

We used NIS Elements (Nikon) for three-dimensional rendering of z-stack pictures.

Flow experiments and data acquisition

Bacteria induced overnight were diluted 1:10 in Fluorobrite 10% FBS 1% Glutamax and loaded in syringes. 3 mm/s mean flow (unless stated otherwise) was applied using syringe pumps connected to microchannels seeded with induced HeLa at 50-80% confluency. Z-stacks for bacterial attachment efficiency was generated by confocal microscopy every second. Three different fields of view were sequentially imaged for 5 min per biological replicate. Data to model residence time was generated by confocal microscopy of z-stacks every 10 s. Three different fields of view were simultaneously imaged for 60 min per biological replicate. Cell surface area was acquired once in the green channel at the start of the experiment. Number of HeLa cells was then approximated based on their average size as manually determined with 5 biological replicates of 3 frames each.

Illustrative confocal time-lapse with both channels for GFP and mScarlet were acquired at either 2 or 6 stacks per minute at 100x magnification.

Bacteria tracking

We use the maximum intensity projection of full stacks to detect attaching bacteria. We used the Fiji plugin Trackmate with LoG detector 48 . Threshold was set so that >95% of bacteria are detected on the final frame and <5% of the tracks were false positive (two different bacteria slowing down in the same area on consecutive frames). The LAP tracker was used with 5 μm maximal inter-frame distance and gap closing, track splitting and closing with a maximal distance of 3 μm. Final number of spots, tracks and spots statistics were exported for data analysis.

Data analysis and modeling

Data generated by trackmate was analyzed using Matlab. In brief, attachment efficiency was defined as the number of tracks strictly longer than 2 frames, divided by the total number of contacts (bacterium appearing on one frame or more). Bacteria present from the first frame were removed from the analysis to exclude bacteria that attached during handling time.

Static co-culture and mammalian cell staining

Mammalian cells were co-incubated with bacteria for 5h 30min at a multiplicity of infection (MOI) of 50 ( Fig. 1C ) or for 1h at a MOI of 200 ( Fig. 3A,B and S5A,B). Wells were washed once with PBS, fixed in 4% paraformaldehyde for 20 minutes, permeabilized with 0.1% Triton X-100 for 5 min and washed twice with PBS. Phalloidin-Atto 655 (Sigma) was used to stain actin at 500 nM for 15 min. DAPI was used for nuclei counterstain at 1 μM for 5 min. Cells were washed twice with PBS and imaged by confocal microscope at 100x magnification.

Bacterial staining, titration and quantification

Bacteria displaying VHH were washed with PBS and stained with recombinant GFP at 100 μg/mL for 10 minutes prior to two PBS washes and imaging under a 1% agarose PBS pad. Wide field fluorescent pictures were taken at 100x and 1.5x lens magnification.

Production of recombinant proteins

eGFP sequence (Genbank accession 8382257) was cloned into pET28a (Novagen) in frame with a N-terminal 6xHis tag and the resulting pXP226 was retransformed into BL21 strain. Production was induced with 1 mM IPTG (Fisher bioreagents) at 20°C overnight. Bacteria were pelleted and lysed by sonication in lysis buffer (Tris 100mM, NaCl 0.5M, glycerol 5%) and eGFP was purified using fast flow His-affinity columns (GE Healthcare) and eluted with 500 mM imidazole. Buffer was exchanged to PBS using 30kDa ultracentrigation spin columns (Merck) and aliquots at 1 mg/mL were snap frozen for further use. mKate2 was produced using the same protocol using the plasmid SpyTag003-mKate2.

Acknowledgements

We thank Dr. Bruno Correia and Stephane Rosset at Ecole Polytechnique Fédérale de Lausanne (EPFL) for the production and purification of recombinant proteins, Dr. Ingmar Riedel-Kruse (Stanford University) for the tetracycline-inducible nanobody display constructs, Dr. Gisou Van der Goot (EPFL) for the GPI-anchored GFP construct, Dr. Didier Trono (EPFL) for the HEK293T cells and lentivectors and Dr. Joerg Huelsken (EPFL) for the CD80-GFP construct and HeLa cells. We are grateful for the fundings from the Gebert Rüf Foundation, project number GRS-057/16, the EPFL School of Life Science interdisciplinary PhD program and the Swiss National Science Foundation, project number 514495.

• 1. ↵ Nadell , C. D. , Drescher , K. & Foster , K. R. Spatial structure, cooperation and competition in biofilms . Nat. Rev. Microbiol. 14 , 589 – 600 ( 2016 ). OpenUrl CrossRef PubMed
• 2. ↵ Pizarro-Cerdá , J. & Cossart , P. Bacterial adhesion and entry into host cells . Cell 124 , 715 – 727 ( 2006 ). OpenUrl CrossRef PubMed Web of Science
• 3. ↵ Viela , F. , Mathelié-Guinlet , M. , Viljoen , A. & Dufrêne , Y. F. What makes bacterial pathogens so sticky? Mol. Microbiol. 113 , 683 – 690 ( 2020 ). OpenUrl
• 4. ↵ Berne , C. , Ellison , C. K. , Ducret , A. & Brun , Y. V. Bacterial adhesion at the single-cell level . Nat. Rev. Microbiol. 16 , 616 – 627 ( 2018 ). OpenUrl CrossRef
• 5. ↵ Thomas , W. E. , Trintchina , E. , Forero , M. , Vogel , V. & Sokurenko , E. V. Bacterial Adhesion to Target Cells Enhanced by Shear Force . Cell 109 , 913 – 923 ( 2002 ). OpenUrl CrossRef PubMed Web of Science
• 6. Kolewe , K. W. , Zhu , J. , Mako , N. R. , Nonnenmann , S. S. & Schiffman , J. D. Bacterial Adhesion Is Affected by the Thickness and Stiffness of Poly(ethylene glycol) Hydrogels . ACS Appl. Mater. Interfaces 10 , 2275 – 2281 ( 2018 ). OpenUrl CrossRef
• 7. ↵ Lecuyer , S. et al. Shear stress increases the residence time of adhesion of Pseudomonas aeruginosa . Biophys. J. 100 , 341 – 350 ( 2011 ). OpenUrl CrossRef PubMed
• 8. ↵ de la Serna , J. B. , Schütz , G. J. , Eggeling , C. & Cebecauer , M. There is no simple model of the plasma membrane organization . Front. Cell Dev. Biol. 4 , 1 – 17 ( 2016 ). OpenUrl
• 9. ↵ Ribet , D. & Cossart , P. How bacterial pathogens colonize their hosts and invade deeper tissues . Microbes Infect. 17 , 173 – 183 ( 2015 ). OpenUrl CrossRef PubMed
• 10. ↵ Girard , V. & Mourez , M. Adhesion mediated by autotransporters of Gram-negative bacteria: Structural and functional features . Res. Microbiol. 157 , 407 – 416 ( 2006 ). OpenUrl CrossRef PubMed Web of Science
• 11. ↵ Nicolay , T. , Vanderleyden , J. & Spaepen , S. Autotransporter-based cell surface display in Gram-negative bacteria . Crit. Rev. Microbiol. 41 , 109 – 123 ( 2015 ). OpenUrl CrossRef PubMed
• 12. ↵ Leo , J. C. , Oberhettinger , P. , Schütz , M. & Linke , D. The inverse autotransporter family: Intimin, invasin and related proteins . Int. J. Med. Microbiol. 305 , 276 – 282 ( 2015 ). OpenUrl CrossRef PubMed
• 13. ↵ Schulte , R. et al. Translocation of Yersinia enterocolitica across reconstituted intestinal epithelial monolayers is triggered by Yersinia invasin binding to β1 integrins apically expressed on M-like cells . Cell. Microbiol. 2 , 173 – 185 ( 2000 ). OpenUrl CrossRef PubMed Web of Science
• 14. ↵ Ismaili , A. , Meddings , J. B. , Ratnam , S. & Sherman , P. M. Modulation of host cell membrane fluidity: A novel mechanism for preventing bacterial adhesion . Am. J. Physiol. -Gastrointest. Liver Physiol. 277 , 201 – 208 ( 1999 ). OpenUrl
• 15. ↵ Dufrêne , Y. F. Sticky microbes: Forces in microbial cell adhesion . Trends Microbiol. 23 , 376 – 382 ( 2015 ). OpenUrl CrossRef PubMed
• 16. ↵ Sauer , M. M. et al. Binding of the Bacterial Adhesin FimH to Its Natural, Multivalent High-Mannose Type Glycan Targets . J. Am. Chem. Soc. 141 , 936 – 944 ( 2019 ). OpenUrl CrossRef PubMed
• 17. Nilsson , L. M. , Thomas , W. E. , Trintchina , E. , Vogel , V. & Sokurenko , E. V. Catch bond-mediated adhesion without a shear threshold: Trimannose versus monomannose interactions with the FimH adhesin of Escherichia coli . J. Biol. Chem. 281 , 16656 – 16663 ( 2006 ). OpenUrl Abstract / FREE Full Text
• 18. ↵ Sauer , M. M. et al. Catch-bond mechanism of the bacterial adhesin FimH . Nat. Commun. 7 , 1 – 13 ( 2016 ). OpenUrl CrossRef PubMed
• 19. ↵ Bell , G. I. Models for the specific adhesion of cells to cells . Science. 200 , 618 – 627 ( 1978 ). OpenUrl Abstract / FREE Full Text
• 20. ↵ Patel , S. , Mathivanan , N. & Goyal , A. Bacterial adhesins, the pathogenic weapons to trick host defense arsenal . Biomed. Pharmacother. 93 , 763 – 771 ( 2017 ). OpenUrl CrossRef PubMed
• 21. ↵ Salema , V. et al. Selection of Single Domain Antibodies from Immune Libraries Displayed on the Surface of E. coli Cells with Two β-Domains of Opposite Topologies . PLoS One 8 , 1 – 18 ( 2013 ). OpenUrl CrossRef PubMed
• 22. ↵ Glass , D. S. & Riedel-Kruse , I. H. A Synthetic Bacterial Cell-Cell Adhesion Toolbox for Programming Multicellular Morphologies and Patterns . Cell 174 , 649 – 658.e16 ( 2018 ). OpenUrl CrossRef
• 23. ↵ Sabzevari , H. et al. Acquisition of CD80 (B7-1) by T Cells . J. Immunol. 166 , 2505 – 2513 ( 2001 ). OpenUrl Abstract / FREE Full Text
• 24. ↵ Kubala , M. H. , Kovtun , O. , Alexandrov , K. & Collins , B. M. Structural and thermodynamic analysis of the GFP:GFP-nanobody complex . Protein Sci. 19 , 2389 – 2401 ( 2010 ). OpenUrl CrossRef PubMed
• 25. ↵ Fridy , P. C. et al. A robust pipeline for rapid production of versatile nanobody repertoires . Nat. Methods 11 , 1253 – 1260 ( 2014 ). OpenUrl CrossRef PubMed
• 26. ↵ Ricci , V. et al. High cell sensitivity to Helicobacter pylori VacA toxin depends on a GPI-anchored protein and is not blocked by inhibition of the clathrin-mediated pathway of endocytosis . Mol. Biol. Cell 11 , 3897 – 3909 ( 2000 ). OpenUrl Abstract / FREE Full Text
• 27. ↵ D W Goddette and C Frieden . Actin polymerization. The mechanism of action of cytochalasin D . J. Biol. Chem. 261 , 15974 – 15980 ( 1986 ). OpenUrl Abstract / FREE Full Text
• 28. ↵ Romero , L. O. et al. Dietary fatty acids fine-tune Piezo1 mechanical response . Nat. Commun. 10 , 1 – 14 ( 2019 ). OpenUrl CrossRef PubMed
• 29. ↵ Fernández-Pérez , E. J. et al. Effect of cholesterol on membrane fluidity and association of Aβ oligomers and subsequent neuronal damage: A Double-Edged Sword . Front. Aging Neurosci. 10 , 1 – 14 ( 2018 ). OpenUrl CrossRef
• 30. ↵ Nishida-Aoki , N. , Tominaga , N. , Kosaka , N. & Ochiya , T. Altered biodistribution of deglycosylated extracellular vesicles through enhanced cellular uptake . J. Extracell. Vesicles 9 , ( 2020 ).
• 31. ↵ Howlader , M. A. et al. Neuraminidase-3 Is a Negative Regulator of LFA-1 Adhesion . Front. Chem. 7 , 1 – 15 ( 2019 ). OpenUrl
• 32. ↵ Cruz-Chu , E. R. , Malafeev , A. , Pajarskas , T. , Pivkin , I. V. & Koumoutsakos , P. Structure and response to flow of the glycocalyx layer . Biophys. J. 106 , 232 – 243 ( 2014 ). OpenUrl CrossRef PubMed
• 33. ↵ Dufrêne , Y. F. & Persat , A. Mechanomicrobiology: how bacteria sense and respond to forces . Nat. Rev. Microbiol. 18 , 227 – 240 ( 2020 ). OpenUrl CrossRef
• 34. ↵ Tripathi , P. et al. Adhesion and nanomechanics of pili from the probiotic Lactobacillus rhamnosus GG . ACS Nano 7 , 3685 – 3697 ( 2013 ). OpenUrl CrossRef PubMed
• 35. ↵ McKenzie , M. , Ha , S. M. , Rammohan , A. , Radhakrishnan , R. & Ramakrishnan , N. Multivalent Binding of a Ligand-Coated Particle: Role of Shape, Size, and Ligand Heterogeneity . Biophys. J. 114 , 1830 – 1846 ( 2018 ). OpenUrl
• 36. ↵ Tollis , S. , Dart , A. E. , Tzircotis , G. & Endres , R. G. The zipper mechanism in phagocytosis: Energetic requirements and variability in phagocytic cup shape . BMC Syst. Biol. 4 , ( 2010 ).
• 37. ↵ Romero , S. , Le Clainche , C. & Gautreau , A. M. Actin polymerization downstream of integrins: Signaling pathways and mechanotransduction . Biochem. J. 477 , 1 – 21 ( 2020 ). OpenUrl
• 38. ↵ González , C. et al. Nanobody-CD16 Catch Bond Reveals NK Cell Mechanosensitivity . Biophys. J. 116 , 1516 – 1526 ( 2019 ). OpenUrl
• 39. ↵ Klamecka , K. , Severin , P. M. , Milles , L. F. , Gaub , H. E. & Leonhardt , H. Energy profile of nanobody-GFP complex under force . Phys. Biol. 12 , 56009 ( 2015 ). OpenUrl
• 40. ↵ Kurdi , R. Al , Vallade , M. & Riveline , D. Membrane and acto-myosin tension promote clustering of adhesion proteins . Pnas 101 , 2229 – 2234 ( 2004 ). OpenUrl Abstract / FREE Full Text
• 41. ↵ Arabyan , N. et al. Salmonella Degrades the Host Glycocalyx Leading to Altered Infection and Glycan Remodeling . Sci. Rep. 6 , 1 – 11 ( 2016 ). OpenUrl CrossRef PubMed
• 42. ↵ Etienne-Mesmin , L. et al. Experimental models to study intestinal microbes–mucus interactions in health and disease . FEMS Microbiol. Rev. 43 , 457 – 489 ( 2019 ). OpenUrl
• 43. ↵ Berne , C. et al. Physiochemical properties of Caulobacter crescentus holdfast: A localized bacterial adhesive . J. Phys. Chem. B 117 , 10492 – 10503 ( 2013 ). OpenUrl CrossRef
• 44. ↵ Mckenzie , G. J. & Craig , N. L. Fast, easy and efficient : site-specific insertion of transgenes into Enterobacterial chromosomes using Tn 7 without need for selection of the insertion event . 7 , 1 – 7 ( 2006 ). OpenUrl
• 45. ↵ Datsenko , K. A. & Wanner , B. L. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products . Proc. Natl. Acad. Sci. U. S. A. 97 , 6640 – 5 ( 2000 ). OpenUrl Abstract / FREE Full Text
• 46. ↵ Chevalier , S. et al. Creating biomimetic surfaces through covalent and oriented binding of proteins . Langmuir 26 , 14707 – 14715 ( 2010 ). OpenUrl CrossRef PubMed
• 47. ↵ Bañuls , M. J. , Puchades , R. & Maquieira , Á. Chemical surface modifications for the development of silicon-based label-free integrated optical (IO) biosensors: A review . Anal. Chim. Acta 777 , 1 – 16 ( 2013 ). OpenUrl CrossRef PubMed
• 48. ↵ Tinevez , J. Y. et al. TrackMate: An open and extensible platform for single-particle tracking . Methods 115 , 80 – 90 ( 2017 ). OpenUrl CrossRef PubMed

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MINI REVIEW article

Modeling bacterial adhesion to unconditioned abiotic surfaces.

• 1 Experimental Physics, Center for Biophysics, Saarland University, Saarbrücken, Germany
• 2 Theoretical Physics, Center for Biophysics, Saarland University, Saarbrücken, Germany
• 3 Max Planck School Matter to Life, Heidelberg, Germany

Understanding bacterial adhesion as a first step toward biofilm formation is of fundamental interests in many applications. While adhesion to abiotic surfaces is directly relevant for some applications, it also provides a controlled reference setting to study details of the adhesion process in general. This review describes the traditional approaches from contact mechanics and colloidal science, which treat the bacterium–substratum interaction in a continuous manner. We will discuss its shortcomings and provide an introduction to different approaches, which understand the adhesion process as a result of individual stochastic interactions of many macromolecules with the substratum.

1. Introduction

Bacterial biofilms are complex consortia of bacterial cells and extracellular substances that can form on various interfaces ( Dunne, 2002 ). The presence of such biofilms on solid, abiotic surfaces can cause problems in many applications: Formed on ship hulls, they increase hydrodynamic friction and therewith fuel consumption ( De Carvalho, 2007 ), biofilms formed inside pipes reduce the pipes' diameter and therewith flow rates of fluids ( Schwermer et al., 2008 ), biocorrosion caused by biofilms reduces efficiency of cooling water systems in the processing industry ( Narenkumar et al., 2019 ). On medical equipment, such as catheters, implants, protheses, and pacemakers, biofilms are responsible for device-related infections, which can lead to severe diseases and hence are an important health care problem ( Magill et al., 2014 ; Römling et al., 2014 ; Jamal et al., 2018 ).

One of the first steps in biofilm formation is the adhesion of single cells to a surface. Therefore, to manage or prevent biofilm formation, a profound understanding of bacterial adhesion to solid surfaces is necessary. In order to gain experimental access to the basic mechanisms of adhesion, the parameters of the system must be kept as controlled as possible. Hence, the presented studies explore bacterial adhesion to abiotic, unconditioned surfaces, i.e., surfaces that are not covered by other biomacromolecules.

First, the approaches of understanding bacterial adhesion on the whole cell level, namely in the framework of colloidal science, i.e., surface thermodynamics and DLVO 1 theory, and contact mechanics are briefly presented. We discuss the prospects and limitations of those models and describe the efforts made outside these frameworks in describing bacterial adhesion mediated by cell wall macromolecules.

2. Experimental Setups

To understand how experimental results led to the creation of different models for bacterial adhesion, the principle experimental approaches that have been used are briefly explained (see also, e.g., Tandogan et al., 2017 ). There are predominately two principle types of experimental setups: On the one hand, experiments with a rather high number of planktonic cells that freely adsorb to an interface and eventually desorb again; on the other hand, experiments with single cells that are actively manipulated by external forces to precisely measure their behavior during adhesion and detachment.

For the first setup type, flow chambers are commonly used in which a bacterial solution is flushed over a surface of interest by a laminar flow profile that allows to estimate the forces parallel to the surfaces. Using optical microscopy, quartz crystal micro-balance, or surface plasmon resonance, the number of attached cells in a certain area can be recorded over time ( Filion-Côté et al., 2017 ; Keskin et al., 2018 ; Alexander et al., 2019 ). With the help of high-resolution optical techniques, not only the number of cells but also their motion at or above the surface can be quantified ( van der Westen et al., 2018 ; Vissers et al., 2018 ). These methods can collect data of large numbers of cells simultaneously under controlled (with or without shear flow) conditions tangential to the surface. However, the forces acting during approach of the cells normal to the surface cannot be controlled. In addition, repeating the experiment and the cellular response for one individual cell is hardly possible.

To repeatedly probe single cells and achieve a high force control, optical tweezers ( Fällman et al., 2004 ; Zhang and Liu, 2008 ) or atomic force microscopy (AFM) ( Hinterdorfer and Dufrêne, 2006 ; Dufrêne and Pelling, 2013 ; Thewes et al., 2015a ; Krieg et al., 2019 ) are used. While both methods have essentially the same advantages in terms of precise force and position control, the latter places fewer demands on the system itself. Therefore, AFM-based force spectroscopy with individual bacterial probes, termed single cell force spectroscopy (SCFS), is the method of choice for many researchers investigating adhesion properties of bacteria ( Berne et al., 2018 ; Alam et al., 2019 ). The cells are immobilized at an AFM cantilever and moved toward and then away from a surface. By measuring the deflection of the cantilever as a function of its motion, force-separation curves, such as one schematically shown in Figure 1A can be recorded. SCFS allows to study the adhesion process almost natively by using very small force triggers, i.e., the force threshold at which the cantilever retraction starts 2 . From these curves, many quantities, such as the adhesion force can be determined. Of note, many experimental force-separation curves recorded with bacterial cells show a very characteristic feature: Before the cells reaches the substratum, a sudden change in the cantilever's deflection and a decrease in the distance between cell and substratum is observed, which is referred to as “snap-in” ( Bhushan, 2017 ). In addition, approach and retraction curves do not necessarily overlap; this is sometimes termed hysteresis. While investigating a significant number of individual cells requires a lot of time, the nature of these experiments allows the repetition of approach and retraction curves with one and the same bacterial cell. This allows to study the role of stochasticity in the adhesion process and to distinguish it from population heterogeneity.

Figure 1. (A) Schematic force–distance curve. (B) Sketch of the model: During approach (upper row), the most extend macromolecules start interacting with the surface and pull the bacterium closer to it, which allows more and more molecules to bind. Upon further approach, some molecules may be compressed. During retraction (lower row), the molecules are decompressed and then stretched before the detaching individually. (C) Gibb's free energy in dependence of the relative displacement of Streptococcus salivarius cells with (top) and without (bottom) fibrillar surface tethers ( van der Westen et al., 2018 ). Image taken from https://pubs.acs.org/doi/10.1021/acs.langmuir.7b04331 . For futher information and permissions refer to ACS. (D) Partitioned force–distance curves (from experiment and simulations) with varying force trigger. A smaller negative (larger absolute value) force trigger means that the retraction starts later ( Thewes et al., 2015b ). Blue curves are individual approach curves, while red curves are individual retraction curves with the same cell. Note the bistable behavior of the retraction curves for intermediate force triggers, where either one or the other type of curve is observed for the same cell. The pulling regime is indicated by the green dotted circle. (E) Model details for Monte Carlo simulations by Thewes et al. (2015b) : Sphere of radius R decorated with springs of length li connected at fixed height Δ d i from bottom of the sphere. The other end is dz i above the surface and interacts with the potential. In each step, the cantilever's height z c is changed and a new equilibrium position d for the sphere computed.

3. Bacterial Adhesion on a Whole-Cell Level

In contact mechanics exist many models, which extend the Hertz model to include the coupling of adhesion and deformation forces: Very simplified cases that included adhesion are the JKR and DMT model ( Johnson et al., 1971 ; Derjaguin et al., 1975 ), based on which more accurate models were constructed that account for deformations and longer ranging adhesion forces ( Muller et al., 1980 ; Maugis, 1992 ; Greenwood, 1997 ; Ciavarella et al., 2019 ). The models have also been extended to describe interactions of inhomogeneous objects ( Barthel and Perriot, 2007 ; Stan and Adams, 2016 ), making them suitable candidates for modeling the adhesion of bacterial cells that have an inhomogeneous surface structure with a lipid bilayer, cross-linked peptidoglycan layer, and eventual cellular appendages ( Chen et al., 2014 ; Loskill et al., 2014 ). A model including these heterogeneities has been constructed by Chen et al. (2012) , who considered a layered structure with different elastic properties along the radial direction. It turns out that this already reduces the extracted Young's modulus to 8–50 kPa, which is about a hundred times smaller than what would be extracted from the Hertz model.

Note that the heterogeneity is limited to the radial direction of the spherical cell. Inhomogeneities within the cell surface, such as clusters of adhesins, and different mechanical properties or lengths of single molecules in the cell wall are not considered. One reason for the fact that Chen et al. (2014) did not experimentally observe effects of these properties can be attributed to their way of preparing bacterial probes: The bacteria, already immobilized on the cantilever, were dried for 2 min, which is likely to alter the proteinaceous cell wall layer and change its original properties, such as heterogeneity ( Chen et al., 2012 , 2014 ). This might also explain why no cell-individual adhesion behavior was observed.

Colloidal approaches phrase the problem of bacterial adhesion as minimization of thermodynamic potentials, such as the Gibbs free energy. Thus, the theory does not take into account eventual strengthening of adhesive bonds. In the review article by Perni et al. (2014) , it is shown that the simple surface thermodynamics approach of considering only interfacial energies to minimize the Gibbs free energy works only in a few cases and is generally considered too simplistic. A different approach applies the DLVO theory to the bacterium-plane geometry considering electrostatic double layer and van der Waals forces that have shown to influence bacterial adhesion ( Van Oss et al., 1990 ; Boks et al., 2008 ; Loskill et al., 2012 ). Various publications use different approximations for these forces that can be quite evolved and in many instances not analytically solvable. However, qualitatively there are only few scenarios possible: If either of the interactions is attractive and the other repulsive, the free energy landscape displays a minimum close to the surface and eventually—depending on the exact relation between attractive and repulsive potentials—also a secondary minimum. Strong adhesion is achieved when the bacterium can overcome the barrier and weak adhesion is achieved inside the secondary minimum. On experimental time scales, weak adhesion manifests itself as reversibility of the adhesion process, not predicted by the surface thermodynamic approach. In DLVO theory, neglected interactions, such as acid–base interactions and steric effects due to the presence of polymers on the bacterium surface, have been incorporated in the so-called xDLVO theory ( Van Oss, 1995 ). These extensions, however, change the interaction potential quantitatively but do not alter the qualitative picture. The failure of these approaches has, according to Perni et al. (2014) , been attributed due to neglecting shear forces and the underlying assumption of a homogeneous bacterial surface composition. However, these models do not aim at describing a full approach and retraction cycle. If the derivative of the potential is considered as the force experience by a bacterium, no hysteresis can be observed since the derivative is unique.

To address these limitations, Jasevičius et al. (2015) extended the DMT model of classical adhesion: The snap-in is incorporated by the van der Waals force of sphere-plane geometry acting from the snap-in distance until direct contact of the surfaces. The magnitude, i.e., the snap-in force, as well as the snap-in distance are fitted from experimental data and are not obtained from the constitute equations of DLVO theory. Once the bacterium is in contact, the usual DMT forces in addition to repulsive electrostatic double layer forces and steric repulsion forces of polymer brushes are considered. This is complemented by an energy dissipation mechanism including plastic deformation to produce the adhesion hysteresis. 3 Phrased loosely, this model combines xDLVO theory with the Hertzian contact model, while also including an ad hoc snap-in mechanism and energy dissipation. Recently, this model has been extended to mimic flow chamber experiments and determine if a given bacterial strain will adhere to a given surface ( Jasevičius and Kruggel-Emden, 2017 ). Therefore, an initial velocity and viscous drag was included into the model and it was demonstrated that Staphylococcus aureus cells stick to a glass surface.

We point out that all three models assume continuous interactions of the entire bacterium with the surface while neglecting stochasticity and the responses of individual macromolecules in the adhesion process. However, the next section will show that non-continuous interactions are needed to describe certain aspects of bacterial adhesion.

4. Understanding Adhesion Through Individual Macromolecules

In a different set of studies, the displacement of different bacteria after settling in a flow chamber has been monitored by optical microscopy ( Sjollema et al., 2017 ). These experiments, combined with SCFS, demonstrated that the movement of the cells parallel to the surface decreases with increasing adhesion force. These experimental results combined with an in silico model led to the conclusion that the bacteria adhere via multiple reversibly binding tethers, which repeatedly detach from and attach to the surface without detaching all at the same time (see Figure 1B ). An extension of this study has determined if adhering bacteria also exhibit vibrations perpendicular to the surface using internal reflection microscopy ( van der Westen et al., 2018 ). For bacteria without cellular appendages, a comparison of the results with predictions from DLVO theory showed that the surface potential displays two minima with a potential barrier in between that was considered to be too high to be overcome by Brownian motion. The researchers observed on the hydrophobic substrata asymmetric fluctuations inside the secondary minimum with amplitudes fitting to the width of the minimum, independent of ionic strength of the solution. 4 In contrast, cells with fibrils showed symmetric fluctuations with five times smaller vibrational amplitudes, regardless of surface hydrophobicity and ionic strength of the solution (see Figure 1C ). This lead the authors to distinguish “tether-coupled” and “floating” adhesion where in the latter case adhesion is dominated by the thermal motion inside the secondary minimum predicted by DLVO theory, whereas in the first case the bacterium is bound to the surface by tethers, which penetrate through the potential barrier predicted from DLVO theory.

A different approach toward understanding the adhesion process was taken by analyzing approach curves of S. aureus on hydrophobic surfaces ( Thewes et al., 2015b ). It has been observed that bacterial contact begins at about 50 nm above the substratum ( Thewes et al., 2015b ), with the aforementioned snap-in. In buffer solution, attractive forces over such large distances cannot be explained by DLVO forces between the bacterium and the substratum. The snap-in was more detailed by analyzing approach and retraction curves with varying negative force triggers, i.e., retraction starts at a certain distance above the substratum before the cell is in direct contact, in experiment and simulation (see Figure 1D ). While for low and high absolute values of the force trigger the same rupture lengths were observed, the adhesion forces were larger for lower absolute values. In between, an unstable behavior with two types of retraction curves was observed. This stochasticity is not caused by difference of individual cells but—since the same cell is repeatedly used—reflects the internal stochasticity of the adhesion process. In particular, the curves with small force triggers displayed an initial attraction to the surface termed “pulling regime” even though the retraction already started.

To explain these observations, Thewes et al. (2015b) built a stochastic model that treats the bacterium as a hard, incompressible sphere decorated with elastic springs representing the cell wall macromolecules (see Figure 1E ). One end of the springs is fixed to the bacterium, while the other end fluctuates thermally and interacts with the surface via an interaction potential. In order to mimic SCFS experiments, this sphere is connected to a cantilever, modeled as a spring, which moves toward/away from the surface. After each cantilever step, determined from the step size of the experimental piezo motor, a prescribed number of Monte Carlo (MC) steps is performed in order to incorporate thermal fluctuations. Afterward, the acting force, computed from the length of the connected springs and the deflection of the cantilever, is computed. The separation d of the sphere to the surface is then moved into the mechanical equilibrium position, such that the restoring force of the cantilever F C and the pulling force of the macromolecules F M cancel. The pulling force is generated only by macromolecules, which are in range of the interaction potential. That way the binding of individual macromolecules and the macroscopic movement of the cell are combined in a single model, which reproduces the experimentally observed behavior, namely the adhesion hysteresis, the snap-in event, and the behavior of retraction curves with varying negative force trigger. The model shows that for generating a snap-in, the distribution of spring constants is important, while the form of the interaction potential is not ( Thewes et al., 2015b ).

The model was extended by replacing the Hookean response of cell wall macromolecules to stretching by the more realistic worm-like chains (WLC) response and by reproducing a high number of experimental force–distance curves from many cells on hydrophilic and hydrophobic surfaces by MC simulations, the adhesion process to abiotic surfaces could be understood in more detail ( Maikranz et al., 2020 ): On hydrophilic surfaces, cell wall macromolecules bind to the substratum (most likely by hydrogen bonds) after overcoming a potential barrier while on hydrophobic surfaces, the molecules tether via hydrophobic interactions without an energy barrier. This leads to rather strong adhesion via many molecules on hydrophobic surfaces and hence rather smooth force–distance curves (where WLC like signatures of single molecules detaching events define the rupture length as shown in the inset in Figure 2A ), and to very “spiky,” stochastically varying force–distance curves and rather low adhesion force on hydrophilic surfaces (see Figure 2A ) ( Thewes et al., 2014 ; Maikranz et al., 2020 ).

Figure 2. (A) Exemplary force–distance curves (upper row) and probability density function of adhesion forces (lower row) from SCFS with S. aureus cells ( Maikranz et al., 2020 ). (B) Adhesion energy (black symbols) and adhesion force (gray symbols) of S. aureus (left) and S. carnosus (right) in dependence of their contact radius squared. The reddish rectangle displays the size of the complete right graph ( Spengler et al., 2017 ). (C) (Left) Experimentally determined adhesion forces for 10 individual cells each on different rough substrata. (Right) Correlation of adhesion force and accessible surface area in dependence of the depth from the top of the rough surface ( Spengler et al., 2019 ).

Ostvar and Wood (2016) introduced a similar model with individual macromolecules and heterogeneous mechanical responses, but without thermal fluctuations. The flexibility of the cantilever was not considered, and a plane–plane geometry was used: The bacterial cell wall is considered to have a certain roughness (approximately determined by AFM to be about 10–20 nm) that accounts for differing lengths of surface molecules. In the model, the surface molecules are represented by polymers that can either behave like Hookean springs or WLCs. At the end of each polymer, a bead is located that can directly bind to the surface via a Lennard–Jones potential. Upon retraction, every single polymer can either unbind by the bead escaping the potential. Using this model, retraction parts of experimental force–distance curves obtained with Staphylococcus epidermidis cells on glass substrata could be reproduced ( Chen et al., 2011 ; Ostvar and Wood, 2016 ). In general, the model cannot produce a snap-in event due to the lack of a cantilever that allows the cell to suddenly approach the surface. Both models demonstrate that the adhesion process can be understood as the multi-scale interactions of heterogeneous macromolecules tethering to a surface.

For these models, the number of cell wall macromolecules that are able to bind to the substratum and also the exact knowledge of the cell wall area size that comes in contact with the surface is important. Spengler et al. (2017) investigated the size of this area, i.e., the area of the bacterial cell wall that contributes to the adhesion for S. aureus and S. carnosus cells: both strains have approximately the same (assumed to be circular) interaction area with radii of about 150–300 nm, although S. aureus cells adhere almost one order of magnitude stronger than S. carnosus cells. Even on the single species level, no correlation between the adhesion force and interaction area could be measured (see Figure 2B ). In addition, the study demonstrated that the increase of the contact area with the applied force differs for different individual cells proving that the adhesion cannot be described by the Hertzian contact model.

As mentioned before, the knowledge about the interaction area and the thermal fluctuations can be used to describe bacterial adhesion to non-ideal surfaces ( Spengler et al., 2019 ). It has been found that on nano-rough substrata, the adhesion force of S. aureus cells decreases with increasing roughness. The reduced adhesion forces can be directly linked to the decrease in accessible binding area for macromolecules that undergo thermal fluctuations of about 50 nm (see Figure 2C ). The study also shows that the thermal fluctuation and hence adhesion can be understood mostly as a passive process: Although cells were killed during SCFS on these spiky surfaces, their adhesion force was not affected ( Spengler et al., 2019 ).

5. Conclusion

Several approaches to describe and understand bacterial adhesion on unconditioned abiotic surfaces have been reviewed. Many studies demonstrate that traditional approaches to bacterial adhesion from colloid science and contact mechanics have limitations because adhesion, without external load, is primary mediated by the interaction of cell wall macromolecules with the substratum. The force response and stochastic length fluctuations of individual molecules determine the adhesive behavior. This leads to huge differences in adhesion forces of individual cells even within the same population. This mechanical heterogeneity inside a population can be important on the biofilm level, determining the colonization of small cavities, e.g., catheters.

A recent study has also shown that external factors, such as shear stresses, can change the molecules' force response and even “activate” adhesion ( Dufrêne and Viljoen, 2020 ). The complexity caused by these divers mechanical responses is enhanced through the organization of adhesive molecules into patches, which were needed to interpret our own results ( Spengler et al., 2021 ). These patches lead to a strong variation of the adhesion forces, depending on the contact area between patches and the substrate. In SCFS experiments, rotation of the bacteria is excluded, but typically not in the native setting. Reorientation of the bacteria could lead to more adherent areas coming into contact with the surface, which in principle could lead to stronger adhesion, especially on rough surfaces. Incorporating this and more detailed information, such as experimentally determined mechanical properties of cell wall macromolecules, their density and inhomogeneity (for example, in the division plane) are interesting directions for future research.

Author Contributions

CS and EM reviewed and selected the content of the paper and wrote the manuscript. LS and KJ supervised the process, discussed the drafts, and helped in writing the manuscript. All authors contributed to the article and approved the submitted version.

This work was supported by the German Research Foundation (DFG) within the collaborative research center SFB 1027 (Projects B1 and B2). CS and KJ were supported by the DFG-project number JA 905/6. KJ was also supported by the Max Planck School Matter to Life in Heidelberg, Germany.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors gratefully acknowledge funding from the above sources. The authors would also like to thank one of the reviewers for his or her detailed comments on contact mechanics, which helped to improve the manuscript.

1. ^ Named after B. Derjaguin, L. D. Landau, E. Verwey, T. Overbeek ( Derjaguin and Landau, 1941 ; Verwey, 1947 ).

2. ^ Even experiments with minimal force triggers do not fully mimic flow chamber experiments since the bacterium is pushed through eventual energy barriers a planktonic bacterium would encounter.

3. ^ Different deformation models from contact mechanics display hysteresis even without energy dissipation or plastic deformation ( Goryacheva and Makhovskaya, 2001 ).

4. ^ On hydrophilic surfaces, adhesion was too low to determine amplitudes.

Alam, F., Kumar, S., and Varadarajan, K. M. (2019). Quantification of adhesion force of bacteria on the surface of biomaterials: techniques and assays. ACS Biomater. Sci. Eng . 5, 2093–2110. doi: 10.1021/acsbiomaterials.9b00213

PubMed Abstract | CrossRef Full Text | Google Scholar

Alexander, T. E., Lozeau, L. D., and Camesano, T. A. (2019). QCM-D characterization of time-dependence of bacterial adhesion. Cell Surface 5:100024. doi: 10.1016/j.tcsw.2019.100024

Barthel, E., and Perriot, A. (2007). Adhesive contact to a coated elastic substrate. J. Phys. D Appl. Phys . 40:1059. doi: 10.1088/0022-3727/40/4/021

Berne, C., Ellison, C. K., Ducret, A., and Brun, Y. V. (2018). Bacterial adhesion at the single-cell level. Nat. Rev. Microbiol . 16, 616–627. doi: 10.1038/s41579-018-0057-5

Bhushan, B. (2017). Springer Handbook of Nanotechnology . Berlin; Heidelberg: Springer. doi: 10.1007/978-3-662-54357-3

CrossRef Full Text | Google Scholar

Boks, N. P., Norde, W., van der Mei, H. C., and Busscher, H. J. (2008). Forces involved in bacterial adhesion to hydrophilic and hydrophobic surfaces. Microbiology 154, 3122–3133. doi: 10.1099/mic.0.2008/018622-0

Chen, Y., Busscher, H. J., van der Mei, H. C., and Norde, W. (2011). Statistical analysis of long- and short-range forces involved in bacterial adhesion to substratum surfaces as measured using atomic force microscopy. Appl. Environ. Microbiol . 77, 5065–5070. doi: 10.1128/AEM.00502-11

Chen, Y., Harapanahalli, A. K., Busscher, H. J., Norde, W., and van der Mei, H. C. (2014). Nanoscale cell wall deformation impacts long-range bacterial adhesion forces on surfaces. Appl. Environ. Microbiol . 80, 637–643. doi: 10.1128/AEM.02745-13

Chen, Y., Norde, W., van der Mei, H. C., and Busscher, H. J. (2012). Bacterial cell surface deformation under external loading. mBio 3:e00378-12. doi: 10.1128/mBio.00378-12

Ciavarella, M., Joe, J., Papangelo, A., and Barber, J. R. (2019). The role of adhesion in contact mechanics. J. R. Soc. Interface 16:20180738. doi: 10.1098/rsif.2018.0738

De Carvalho, C. C. (2007). Biofilms: recent developments on an old battle. Recent Pat. Biotechnol . 1, 49–57. doi: 10.2174/187220807779813965

Derjaguin, B., Muller, V., and Toporov, Y. (1975). Effect of contact deformations on the adhesion of particles. J. Colloid Interface Sci . 53, 314–326. doi: 10.1016/0021-9797(75)90018-1

Derjaguin, B. V., and Landau, L. (1941). Theory of the stability of strongly charged lyophobic sols and of the adhesion of strongly charged particles in solutions of electrolytes. Acta Physicochim. URSS 14, 633–662.

Google Scholar

Dufrêne, Y. F., and Pelling, A. E. (2013). Force nanoscopy of cell mechanics and cell adhesion. Nanoscale 5, 4094–4104. doi: 10.1039/c3nr00340j

Dufrêne, Y. F., and Viljoen, A. (2020). Binding strength of gram-positive bacterial adhesins. Front. Microbiol . 11:1457. doi: 10.3389/fmicb.2020.01457

Dunne, W. M. (2002). Bacterial adhesion: seen any good biofilms lately? Clin. Microbiol. Rev . 15, 155–166. doi: 10.1128/CMR.15.2.155-166.2002

Fällman, E., Schedin, S., Jass, J., Andersson, M., Uhlin, B. E., and Axner, O. (2004). Optical tweezers based force measurement system for quantitating binding interactions: system design and application for the study of bacterial adhesion. Biosens. Bioelectron . 19, 1429–1437. doi: 10.1016/j.bios.2003.12.029

Filion-Côté, S., Melaine, F., Kirk, A. G., and Tabrizian, M. (2017). Monitoring of bacterial film formation and its breakdown with an angular-based surface plasmon resonance biosensor. Analyst 142, 2386–2394. doi: 10.1039/C7AN00068E

Goryacheva, I., and Makhovskaya, Y. Y. (2001). Adhesive interaction of elastic bodies. J. Appl. Math. Mech . 65, 273–282. doi: 10.1016/S0021-8928(01)00031-4

Greenwood, J. (1997). Adhesion of elastic spheres. Proc. R. Soc. Lond. A Math. Phys. Eng. Sci . 453, 1277–1297. doi: 10.1098/rspa.1997.0070

Hinterdorfer, P., and Dufrêne, Y. F. (2006). Detection and localization of single molecular recognition events using atomic force microscopy. Nat. Methods 3, 347–355. doi: 10.1038/nmeth871

Jamal, M., Ahmad, W., Andleeb, S., Jalil, F., Imran, M., Nawaz, M. A., et al. (2018). Bacterial biofilm and associated infections. J. Chin. Med. Assoc . 81, 7–11. doi: 10.1016/j.jcma.2017.07.012

Jasevičius, R., Baronas, R., and Kruggel-Emden, H. (2015). Numerical modelling of the normal adhesive elastic-plastic interaction of a bacterium. Adv. Powder Technol . 26, 742–752. doi: 10.1016/j.apt.2015.04.010

Jasevičius, R., and Kruggel-Emden, H. (2017). Numerical modelling of the sticking process of a S. aureus bacterium. Int. J. Adhes. Adhesiv . 77, 15–28. doi: 10.1016/j.ijadhadh.2017.03.015

Johnson, K. L., Kendall, K., and Roberts, A. (1971). Surface energy and the contact of elastic solids. Proc. R. Soc. Lond. A Math. Phys. Sci . 324, 301–313. doi: 10.1098/rspa.1971.0141

Keskin, D., Mergel, O., van der Mei, H. C., Busscher, H. J., and van Rijn, P. (2018). Inhibiting bacterial adhesion by mechanically modulated microgel coatings. Biomacromolecules 20, 243–253. doi: 10.1021/acs.biomac.8b01378

Krieg, M., Fläschner, G., Alsteens, D., Gaub, B. M., Roos, W. H., Wuite, G. J., et al. (2019). Atomic force microscopy-based mechanobiology. Nat. Rev. Phys . 1, 41–57. doi: 10.1038/s42254-018-0001-7

Loskill, P., Hähl, H., Thewes, N., Kreis, C. T., Bischoff, M., Herrmann, M., et al. (2012). Influence of the subsurface composition of a material on the adhesion of staphylococci. Langmuir 28, 7242–7248. doi: 10.1021/la3004323

Loskill, P., Pereira, P. M., Jung, P., Bischoff, M., Herrmann, M., Pinho, M. G., et al. (2014). Reduction of the peptidoglycan crosslinking causes a decrease in stiffness of the staphylococcus aureus cell envelope. Biophys. J . 107, 1082–1089. doi: 10.1016/j.bpj.2014.07.029

Magill, S. S., Edwards, J. R., Bamberg, W., Beldavs, Z. G., Dumyati, G., Kainer, M. A., et al. (2014). Multistate point-prevalence survey of health care-associated infections. N. Engl. J. Med . 370, 1198–1208. doi: 10.1056/NEJMoa1306801

Maikranz, E., Spengler, C., Thewes, N., Thewes, A., Nolle, F., Jung, P., et al. (2020). Different binding mechanisms of staphylococcus aureus to hydrophobic and hydrophilic surfaces. Nanoscale 12, 19267–19275. doi: 10.1039/D0NR03134H

Maugis, D. (1992). Adhesion of spheres: the JKR-DMT transition using a dugdale model. J. Colloid Interface Sci . 150, 243–269. doi: 10.1016/0021-9797(92)90285-T

Muller, V., Yushchenko, V., and Derjaguin, B. (1980). On the influence of molecular forces on the deformation of an elastic sphere and its sticking to a rigid plane. J. Colloid Interface Sci . 77, 91–101. doi: 10.1016/0021-9797(80)90419-1

Narenkumar, J., AlSalhi, M. S., Arul Prakash, A., Abilaji, S., Devanesan, S., Rajasekar, A., et al. (2019). Impact and role of bacterial communities on biocorrosion of metals used in the processing industry. ACS Omega 4, 21353–21360. doi: 10.1021/acsomega.9b02954

Ostvar, S., and Wood, B. D. (2016). Multiscale model describing bacterial adhesion and detachment. Langmuir 32, 5213–5222. doi: 10.1021/acs.langmuir.6b00882

Perni, S., Preedy, E. C., and Prokopovich, P. (2014). Success and failure of colloidal approaches in adhesion of microorganisms to surfaces. Adv. Colloid Interface Sci . 206, 265–274. Manuel G. Velarde. doi: 10.1016/j.cis.2013.11.008

Römling, U., Kjelleberg, S., Normark, S., Nyman, L., Uhlin, B. E., and Åkerlund, B. (2014). Microbial biofilm formation: a need to act. J. Intern. Med . 276, 98–110. doi: 10.1111/joim.12242

Schwermer, C. U., Lavik, G., Abed, R. M., Dunsmore, B., Ferdelman, T. G., Stoodley, P., et al. (2008). Impact of nitrate on the structure and function of bacterial biofilm communities in pipelines used for injection of seawater into oil fields. Appl. Environ. Microbiol . 74, 2841–2851. doi: 10.1128/AEM.02027-07

Sjollema, J., van der Mei, H. C., Hall, C. L., Peterson, B. W., de Vries, J., Song, L., et al. (2017). Detachment and successive re-attachment of multiple, reversibly-binding tethers result in irreversible bacterial adhesion to surfaces. Sci. Rep . 7:4369. doi: 10.1038/s41598-017-04703-8

Spengler, C., Glatz, B. A., Maikranz, E., Bischoff, M., Klatt, M. A., Santen, L., et al. (2021). The adhesion capability of S. aureus cells is heterogeneously distributed over the cell envelope. bioRxiv . doi: 10.1101/2021.01.05.425282

Spengler, C., Nolle, F., Mischo, J., Faidt, T., Grandthyll, S., Thewes, N., et al. (2019). Strength of bacterial adhesion on nanostructured surfaces quantified by substrate morphometry. Nanoscale 11, 19713–19722. doi: 10.1039/C9NR04375F

Spengler, C., Thewes, N., Jung, P., Bischoff, M., and Jacobs, K. (2017). Determination of the nano-scaled contact area of staphylococcal cells. Nanoscale 9, 10084–10093. doi: 10.1039/C7NR02297B

Stan, G., and Adams, G. G. (2016). Adhesive contact between a rigid spherical indenter and an elastic multi-layer coated substrate. Int. J. Solids Struct . 87, 1–10. doi: 10.1016/j.ijsolstr.2016.02.043

Tandogan, N., Abadian, P. N., Huo, B., and Goluch, E. D. (2017). “Characterization of bacterial adhesion and biofilm formation,” in Antimicrobial Coatings and Modifications on Medical Devices , eds Z. Zhang and V. E. Wagner (Cham: Springer), 67–95. doi: 10.1007/978-3-319-57494-3_3

Thewes, N., Loskill, P., Jung, P., Peisker, H., Bischoff, M., Herrmann, M., et al. (2014). Hydrophobic interaction governs unspecific adhesion of staphylococci: a single cell force spectroscopy study. Beilstein J. Nanotechnol . 5, 1501–1512. doi: 10.3762/bjnano.5.163

Thewes, N., Loskill, P., Spengler, C., Hümbert, S., Bischoff, M., and Jacobs, K. (2015a). A detailed guideline for the fabrication of single bacterial probes used for atomic force spectroscopy. Eur. Phys. J. E 38:140. doi: 10.1140/epje/i2015-15140-2

Thewes, N., Thewes, A., Loskill, P., Peisker, H., Bischoff, M., Herrmann, M., et al. (2015b). Stochastic binding of Staphylococcus aureus to hydrophobic surfaces. Soft Matter 11, 8913–9046. doi: 10.1039/C5SM00963D

van der Westen, R., Sjollema, J., Molenaar, R., Sharma, P. K., van der Mei, H. C., and Busscher, H. J. (2018). Floating and tether-coupled adhesion of bacteria to hydrophobic and hydrophilic surfaces. Langmuir 34, 4937–4944. doi: 10.1021/acs.langmuir.7b04331

Van Oss, C. (1995). Hydrophobicity of biosurfaces—origin, quantitative determination and interaction energies. Colloids Surf. B Biointerfaces 5, 91–110. doi: 10.1016/0927-7765(95)01217-7

Van Oss, C. J., Giese, R. F., and Costanzo, P. M. (1990). DLVO and non-DLVO interactions in hectorite. Clays Clay Miner . 38, 151–159. doi: 10.1346/CCMN.1990.0380206

Verwey, E. J. W. (1947). Theory of the Stability of Lyophobic Colloids. J. Phys. Chem. 51, 631–636.

PubMed Abstract | Google Scholar

Vissers, T., Brown, A. T., Koumakis, N., Dawson, A., Hermes, M., Schwarz-Linek, J., et al. (2018). Bacteria as living patchy colloids: phenotypic heterogeneity in surface adhesion. Sci. Adv . 4:eaao1170. doi: 10.1126/sciadv.aao1170

Zhang, H., and Liu, K. K. (2008). Optical tweezers for single cells. J. R. Soc. Interface 5, 671–690. doi: 10.1098/rsif.2008.0052

Keywords: bacterial adhesion, living colloids, (x)DLVO, tethering cell wall molecules, single-cell force spectroscopy, Monte Carlo simulation, Staphylococcus aureus

Citation: Spengler C, Maikranz E, Santen L and Jacobs K (2021) Modeling Bacterial Adhesion to Unconditioned Abiotic Surfaces. Front. Mech. Eng. 7:661370. doi: 10.3389/fmech.2021.661370

Received: 30 January 2021; Accepted: 10 March 2021; Published: 13 May 2021.

Reviewed by:

Copyright © 2021 Spengler, Maikranz, Santen and Jacobs. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ludger Santen, l.santen@mx.uni-saarland.de ; Karin Jacobs, k.jacobs@physik.uni-saarland.de

† These authors have contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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For information about this mission, see the Space Station Research Explorer on Bacterial Adhesion and Corrosion .

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In vitro assay of bacterial adhesion onto mammalian epithelial cells

Affiliation.

• 1 Groupe de Recherche sur les Maladies Infectieuses du Porc GREMIP, Faculte de medecine veterinaire, Universite de Montreal, QC, Canada.
• PMID: 21633326
• PMCID: PMC3197129
• DOI: 10.3791/2783

To cause infections, bacteria must colonize their host. Bacterial pathogens express various molecules or structures able to promote attachment to host cells(1). These adhesins rely on interactions with host cell surface receptors or soluble proteins acting as a bridge between bacteria and host. Adhesion is a critical first step prior to invasion and/or secretion of toxins, thus it is a key event to be studied in bacterial pathogenesis. Furthermore, adhered bacteria often induce exquisitely fine-tuned cellular responses, the studies of which have given birth to the field of 'cellular microbiology'(2). Robust assays for bacterial adhesion on host cells and their invasion therefore play key roles in bacterial pathogenesis studies and have long been used in many pioneer laboratories(3,4). These assays are now practiced by most laboratories working on bacterial pathogenesis. Here, we describe a standard adherence assay illustrating the contribution of a specific adhesin. We use the Escherichia coli strain 2787(5), a human pathogenic strain expressing the autotransporter Adhesin Involved in Diffuse Adherence (AIDA). As a control, we use a mutant strain lacking the aidA gene, 2787ΔaidA (F. Berthiaume and M. Mourez, unpublished), and a commercial laboratory strain of E. coli, C600 (New England Biolabs). The bacteria are left to adhere to the cells from the commonly used HEp-2 human epithelial cell line. This assay has been less extensively described before(6).

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• Enteropathogenic Escherichia coli (EPEC) adhesion to intestinal epithelial cells: role of bundle-forming pili (BFP), EspA filaments and intimin. Cleary J, Lai LC, Shaw RK, Straatman-Iwanowska A, Donnenberg MS, Frankel G, Knutton S. Cleary J, et al. Microbiology (Reading). 2004 Mar;150(Pt 3):527-538. doi: 10.1099/mic.0.26740-0. Microbiology (Reading). 2004. PMID: 14993302
• Evolution of host-microbe cell adherence by receptor domain shuffling. Baker EP, Sayegh R, Kohler KM, Borman W, Goodfellow CK, Brush ER, Barber MF. Baker EP, et al. Elife. 2022 Jan 25;11:e73330. doi: 10.7554/eLife.73330. Elife. 2022. PMID: 35076392 Free PMC article.
• Functional organization of the autotransporter adhesin involved in diffuse adherence. Charbonneau ME, Mourez M. Charbonneau ME, et al. J Bacteriol. 2007 Dec;189(24):9020-9. doi: 10.1128/JB.01238-07. Epub 2007 Oct 12. J Bacteriol. 2007. PMID: 17933890 Free PMC article.
• Autotransporter Adhesins in Escherichia coli Pathogenesis. Vo JL, Martínez Ortiz GC, Subedi P, Keerthikumar S, Mathivanan S, Paxman JJ, Heras B. Vo JL, et al. Proteomics. 2017 Dec;17(23-24). doi: 10.1002/pmic.201600431. Epub 2017 Oct 12. Proteomics. 2017. PMID: 28665015 Review.
• Adhesion and entry of uropathogenic Escherichia coli. Mulvey MA. Mulvey MA. Cell Microbiol. 2002 May;4(5):257-71. doi: 10.1046/j.1462-5822.2002.00193.x. Cell Microbiol. 2002. PMID: 12027955 Review.
• Genomic diversity of Campylobacter jejuni and Campylobacter coli isolated from the Ethiopian dairy supply chain. Admasie A, Wei X, Johnson B, Burns L, Pawar P, Aurand-Cravens A, Voloshchuk O, Dudley EG, Sisay Tessema T, Zewdu A, Kovac J. Admasie A, et al. PLoS One. 2024 Aug 19;19(8):e0305581. doi: 10.1371/journal.pone.0305581. eCollection 2024. PLoS One. 2024. PMID: 39159178 Free PMC article.
• Membrane lipid remodeling eradicates Helicobacter pylori by manipulating the cholesteryl 6'-acylglucoside biosynthesis. Ong LL, Jan HM, Le HT, Yang TC, Kuo CY, Feng AF, Mong KT, Lin CH. Ong LL, et al. J Biomed Sci. 2024 Apr 29;31(1):44. doi: 10.1186/s12929-024-01031-8. J Biomed Sci. 2024. PMID: 38685037 Free PMC article.
• Cell-Free Culture Supernatant of Lactobacillus acidophilus AG01 and Bifidobacterium animalis subsp. lactis AG02 Reduces the Pathogenicity of NetB-Positive Clostridium perfringens in a Chicken Intestinal Epithelial Cell Line. Kadekar D, Udrea AC, Bak SY, Christensen N, Gibbs K, Shen C, Bernardeau M. Kadekar D, et al. Microorganisms. 2024 Apr 22;12(4):839. doi: 10.3390/microorganisms12040839. Microorganisms. 2024. PMID: 38674783 Free PMC article.
• IgA-mediated control of host-microbial interaction during weaning reaction influences gut inflammation. Tang W, Wei Y, Ni Z, Hou K, Luo XM, Wang H. Tang W, et al. Gut Microbes. 2024 Jan-Dec;16(1):2323220. doi: 10.1080/19490976.2024.2323220. Epub 2024 Mar 4. Gut Microbes. 2024. PMID: 38439579 Free PMC article.
• In vitro safety and functional characterization of the novel Bacillus coagulans strain CGI314. Mazhar S, Simon A, Khokhlova E, Colom J, Leeuwendaal N, Deaton J, Rea K. Mazhar S, et al. Front Microbiol. 2024 Jan 10;14:1302480. doi: 10.3389/fmicb.2023.1302480. eCollection 2023. Front Microbiol. 2024. PMID: 38274758 Free PMC article.
• Kline KA. Bacterial adhesins in host-microbe interactions. Cell Host Microbe. 2009;5:580–580. - PubMed
• Cossart P, Boquet P, Normark S, Rappuoli R. Cellular microbiology emerging. Science. 1996;271:315–315. - PubMed
• Elsinghorst EA. Measurement of invasion by gentamicin resistance. Methods Enzymol. 1994;236:405–405. - PubMed
• Pizarro-Cerda J, Lecuit M, Cossart P. In: Molecular Cellular Microbiology. Sansonetti P, Zychlinsky A, editors. London: Academic Press; 2002. pp. 161–161. Vol. 31.
• Srivastava A, Isberg RR. In: Molecular Cellular Microbiology. Sansonetti P, Zychlinsky A, editors. London: Academic Press; 2002. pp. 179–179. Vol. 31.

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In vitro modulation of human foam cell formation and adhesion molecules expression by ginger extracts points to potential cardiovascular preventive agents.

1. Introduction

2.1. fractionation of raw ginger extract and thin-layer chromatography, 2.2. foam cell formation upon oxldl exposure, 2.3. the effect of ginger fractions on foam cell formation, 2.4. the effect of ginger fractions on scavenger receptors and cellular adhesion markers, 2.5. effect of ginger fractions on ldl oxidation, 2.6. the effect of ginger fractions on the inflammasome, 3. discussion, 4. materials and methods, 4.1. fractionation of raw ginger extract and high-performance thin-layer chromatography, 4.2. oxldl production, 4.3. cell culture, 4.4. assessment of foam cell formation under oxldl exposure by the oil red o assay, 4.5. effect of ginger fractions on foam cell formation according to the oil red o assay, 4.6. the effect of ginger fractions on scavenger receptors and cellular adhesion markers determined by indirect immunofluorescence, 4.7. the effect of ginger fractions on the electronegativity of ldl, 4.8. the effect of ginger fractions on the inflammasome by western blotting analysis, 4.9. statistical analysis, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

• Cesare, D.; Bixby, H.; Gaziano, T.; Hadeed, L.; Kabudula, C.; McGhie, D.V.; Mwangi, J.; Pervan, B.; Perel, P.; Piñeiro, D.; et al. World Heart Report 2023-Confronting the world’s number one killer. Nat. Med. 2022 , 28 , 2019–2026. [ Google Scholar ] [ CrossRef ]
• Rafieian-Kopaei, M.; Setorki, M.; Doudi, M.; Baradaran, A.; Nasri, H. Atherosclerosis: Process, Indicators, Risk Factors and New Hopes. Int. J. Prev. Med. 2014 , 5 , 927–946. [ Google Scholar ]
• Tannock, L.R.; King, V.L. Proteoglycan mediated lipoprotein retention: A mechanism of diabetic atherosclerosis. Rev. Endocr. Metab. Disord. 2008 , 9 , 289–300. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yoshida, H.; Kisugi, R. Mechanisms of LDL oxidation. Clin. Chim. Acta 2010 , 411 , 1875–1882. [ Google Scholar ] [ CrossRef ]
• Cunningham, K.S.; Gotlieb, A.I. The role of shear stress in the pathogenesis of atherosclerosis. Lab. Investig. 2005 , 85 , 9–23. [ Google Scholar ] [ CrossRef ]
• Gimbrone, M.A.; García-Cardeña, G. Endothelial Cell Dysfunction and the Pathobiology of Atherosclerosis. Circ. Res. 2016 , 118 , 620–636. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Falk, E. Pathogenesis of Atherosclerosis. J. Am. Coll. Cardiol. 2006 , 47 , C7–C12. [ Google Scholar ] [ CrossRef ]
• Colin, S.; Chinetti-Gbaguidi, G.; Staels, B. Macrophage phenotypes in atherosclerosis. Immunol. Rev. 2014 , 262 , 153–166. [ Google Scholar ] [ CrossRef ]
• Xu, H.; Jiang, J.; Chen, W.; Li, W.; Chen, Z. Vascular Macrophages in Atherosclerosis. J. Immunol. Res. 2019 , 2019 , 1–14. [ Google Scholar ] [ CrossRef ]
• Moore, K.J.; Freeman, M.W. Scavenger Receptors in Atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 2006 , 26 , 1702–1711. [ Google Scholar ] [ CrossRef ]
• Kzhyshkowska, J.; Neyen, C.; Gordon, S. Role of macrophage scavenger receptors in atherosclerosis. Immunobiology 2012 , 217 , 492–502. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• de Villiers, W.J.S.; Smart, E.J. Macrophage scavenger receptors and foam cell formation. J. Leukoc. Biol. 1999 , 66 , 740–746. [ Google Scholar ] [ CrossRef ]
• Glass, C.K.; Witztum, J.L. Atherosclerosis: The Road Ahead. Cell 2001 , 104 , 503–516. [ Google Scholar ] [ CrossRef ]
• Libby, P. The changing landscape of atherosclerosis. Nature 2021 , 592 , 7855. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Newman, D.J.; Cragg, G.M. Natural Products as Sources of New Drugs over the Nearly Four Decades from 01/1981 to 09/2019. J. Nat. Prod. 2020 , 83 , 770–803. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Sedighi, M.; Bahmani, M.; Asgary, S.; Beyranvand, F.; Rafieian-Kopaei, M. A review of plant-based compounds and medicinal plants effective on atherosclerosis. J. Res. Med. Sci. 2017 , 22 , 30. [ Google Scholar ] [ CrossRef ]
• Singletary, K. Ginger: An Overview of Health Benefits. Nutr. Today 2010 , 45 , 171. [ Google Scholar ] [ CrossRef ]
• Shahrajabian, M.H.; Sun, W.; Cheng, Q. Clinical aspects and health benefits of ginger ( Zingiber officinale ) in both traditional Chinese medicine and modern industry. Acta Agric. Scand. Sect. B—Soil Plant Sci. 2019 , 69 , 546–556. [ Google Scholar ] [ CrossRef ]
• Al-Awwadi, N.A.J. Potential health benefits and scientific review of ginger. JPP 2017 , 9 , 111–116. [ Google Scholar ] [ CrossRef ]
• Murad, M.S.; Shahina; Arain, A.Q.; Azmi, A.A. A Prospective Study on Hypolipidaemic Effects in Fresh and Dried Ginger Varieties. J. Pharmacovigil. Drug Saf. 2020 , 17 , 2. [ Google Scholar ]
• Li, C.; Li, J.; Jiang, F.; Tzvetkov, N.T.; Horbanczuk, J.O.; Li, Y.; Atanasov, A.G.; Wang, D. Vasculoprotective effects of ginger (Zingiber officinale Roscoe) and underlying molecular mechanisms. Food Funct. 2021 , 12 , 1897–1913. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, S.; Tian, M.; Yang, R.; Jing, Y.; Chen, W.; Wang, J.; Zheng, X.; Wang, F. 6-Gingerol Ameliorates Behavioral Changes and Atherosclerotic Lesions in ApoE−/−Mice Exposed to Chronic Mild Stress. Cardiovasc. Toxicol. 2018 , 18 , 420–430. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Praillet, C.; Lortat-Jacob, H.; Grimaud, J.-A. Les protéoglycanes II. Rôles en pathologie. M.S. Méd. Sci. 1998 , 14 , 421–428. [ Google Scholar ] [ CrossRef ]
• Mankan, A.K.; Dau, T.; Jenne, D.; Hornung, V. The NLRP3/ASC/Caspase-1 axis regulates IL-1β processing in neutrophils. Eur. J. Immunol. 2012 , 42 , 710–715. [ Google Scholar ] [ CrossRef ]
• Salaramoli, S.; Mehri, S.; Yarmohammadi, F.; Hashemy, S.I.; Hosseinzadeh, H. The effects of ginger and its constituents in the prevention of metabolic syndrome: A review. Iran. J. Basic Med. Sci. 2022 , 25 , 664–674. [ Google Scholar ] [ CrossRef ]
• Roudsari, N.M.; Lashgari, N.-A.; Momtaz, S.; Roufogalis, B.; Abdolghaffari, A.H.; Sahebkar, A. Ginger: A complementary approach for management of cardiovascular diseases. BioFactors 2021 , 47 , 933–951. [ Google Scholar ] [ CrossRef ]
• Fakhri, S.; Patra, J.K.; Das, S.K.; Das, G.; Majnooni, M.B.; Farzaei, M.H. Ginger and Heart Health: From Mechanisms to Therapeutics. Curr. Mol. Pharmacol. 2021 , 14 , 943–959. [ Google Scholar ] [ CrossRef ]
• Liu, Q.; Liu, J.; Guo, H.; Sun, S.; Wang, S.; Zhang, Y.; Li, S.; Qiao, Y. [6]-Gingerol: A Novel AT1 Antagonist for the Treatment of Cardiovascular Disease. Planta Med. 2013 , 79 , 322–326. [ Google Scholar ] [ CrossRef ]
• Halliwell, B. Oxidative stress in cell culture: An under-appreciated problem? FEBS Lett. 2003 , 540 , 3–6. [ Google Scholar ] [ CrossRef ]
• Hosseinzadeh, A.; Bahrampour Juybari, K.; Fatemi, M.J.; Kamarul, T.; Bagheri, A.; Tekiyehmaroof, N.; Sharifi, A.M. Protective Effect of Ginger ( Zingiber officinale Roscoe ) Extract against Oxidative Stress and Mitochondrial Apoptosis Induced by Interleukin-1β in Cultured Chondrocytes. Cells Tissues Organs 2017 , 204 , 241–250. [ Google Scholar ] [ CrossRef ]
• Scalia, A.; Doumani, L.; Kindt, N.; Journé, F.; Trelcat, A.; Carlier, S. The Interplay between Atherosclerosis and Cancer: Breast Cancer Cells Increase the Expression of Endothelial Cell Adhesion Markers. Biology 2023 , 12 , 896. [ Google Scholar ] [ CrossRef ]
• Lara-Guzmán, O.J.; Gil-Izquierdo, Á.; Medina, S.; Osorio, E.; Álvarez-Quintero, R.; Zuluaga, N.; Oger, C.; Galano, J.M.; Durand, T.; Muñoz-Durango, K. Oxidized LDL triggers changes in oxidative stress and inflammatory biomarkers in human macrophages. Redox Biol. 2018 , 15 , 1–11. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hu, Z.; Hui, B.; Hou, X.; Liu, R.; Sukhanov, S.; Liu, X. APE1 inhibits foam cell formation from macrophages via LOX1 suppression. Am. J. Transl. Res. 2020 , 12 , 6559–6568. [ Google Scholar ] [ PubMed ]
• Badrnya, S.; Schrottmaier, W.C.; Kral, J.B.; Yaiw, K.C.; Volf, I.; Schabbauer, G.; Söderberg-Nauclér, C.; Assinger, A. Platelets Mediate Oxidized Low-Density Lipoprotein–Induced Monocyte Extravasation and Foam Cell Formation. Arterioscler. Thromb. Vasc. Biol. 2014 , 34 , 571–580. [ Google Scholar ] [ CrossRef ]
• Wang, Q.; Wu, J.; Zeng, Y.; Chen, K.; Wang, C.; Yang, S.; Sun, N.; Chen, H.; Duan, K.; Zeng, G. Pyroptosis: A pro-inflammatory type of cell death in cardiovascular disease. Clin. Chim. Acta 2020 , 510 , 62–72. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Balanescu, S.; Barbu, E.; Georgescu, C.; Popescu, A.C. NLRP3 Inflammasome in Cardiovascular Disease: David’s Stone against Goliath? Rom. J. Cardiol. 2021 , 31 , 517–527. [ Google Scholar ] [ CrossRef ]
• Wang, P.; Zhang, W.; Feng, Z.; Zhang, J.; Sun, Y.; Zhang, W. LDL-induced NLRC3 inflammasome activation in cardiac fibroblasts contributes to cardiomyocytic dysfunction. Mol. Med. Rep. 2021 , 24 , 526. [ Google Scholar ] [ CrossRef ]
• Gram, A.M.; Wright, J.A.; Pickering, R.J.; Lam, N.L.; Booty, L.M.; Webster, S.J.; Bryant, C.E. Salmonella Flagellin Activates NAIP/NLRC4 and Canonical NLRP3 Inflammasomes in Human Macrophages. J. Immunol. 2021 , 206 , 631–640. [ Google Scholar ] [ CrossRef ]
• Vlaminck, B.; Calay, D.; Genin, M.; Sauvage, A.; Ninane, N.; Zouaoui Boudjeltia, K.; Raes, M.; Michiels, C. Effects of copper sulfate-oxidized or myeloperoxidase-modified LDL on lipid loading and programmed cell death in macrophages under hypoxia. Hypoxia 2014 , 2 , 153–169. [ Google Scholar ] [ CrossRef ]
• Cathcart, M.K. Regulation of Superoxide Anion Production by NADPH Oxidase in Monocytes/Macrophages. Arterioscler. Thromb. Vasc. Biol. 2004 , 24 , 23–28. [ Google Scholar ] [ CrossRef ]
• Council of Europe. High-performance thin-layer chromatography of herbal drugs and herbal drug preparations (2.8.25). In European Pharmacopoeia , 11th ed.; Council of Europe: Strasbourg, France, 2023. [ Google Scholar ]
• Scalia, A.; Kindt, N.; Trelcat, A.; Nachtergael, A.; Duez, P.; Journé, F.; Carlier, S. Development of a Method for Producing oxLDL: Characterization of Their Effects on HPV-Positive Head and Neck Cancer Cells. Int. J. Mol. Sci. 2022 , 23 , 12552. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Justo, O.R.; Simioni, P.U.; Gabriel, D.L.; da S, W.M.; de T, P.; Moraes, Â.M. Evaluation of in vitro anti-inflammatory effects of crude ginger and rosemary extracts obtained through supercritical CO 2 extraction on macrophage and tumor cell line: The influence of vehicle type. BMC Complement. Altern. Med. 2015 , 15 , 390. [ Google Scholar ] [ CrossRef ] [ PubMed ]

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Scalia, A.; Coquay, M.; Kindt, N.; Duez, P.; Aro, R.; Journé, F.; Fabjanczyk, M.; Trelcat, A.; Carlier, S. In Vitro Modulation of Human Foam Cell Formation and Adhesion Molecules Expression by Ginger Extracts Points to Potential Cardiovascular Preventive Agents. Int. J. Mol. Sci. 2024 , 25 , 9487. https://doi.org/10.3390/ijms25179487

Scalia A, Coquay M, Kindt N, Duez P, Aro R, Journé F, Fabjanczyk M, Trelcat A, Carlier S. In Vitro Modulation of Human Foam Cell Formation and Adhesion Molecules Expression by Ginger Extracts Points to Potential Cardiovascular Preventive Agents. International Journal of Molecular Sciences . 2024; 25(17):9487. https://doi.org/10.3390/ijms25179487

Scalia, Alessandro, Maxime Coquay, Nadège Kindt, Pierre Duez, Rania Aro, Fabrice Journé, Mathilde Fabjanczyk, Anne Trelcat, and Stéphane Carlier. 2024. "In Vitro Modulation of Human Foam Cell Formation and Adhesion Molecules Expression by Ginger Extracts Points to Potential Cardiovascular Preventive Agents" International Journal of Molecular Sciences 25, no. 17: 9487. https://doi.org/10.3390/ijms25179487

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• Published: 22 May 2024

Optical sensor reveals the hidden influence of cell dissociation on adhesion measurements

• Kinga Dóra Kovács 1 , 2 ,
• Zoltán Szittner 1 ,
• Beatrix Magyaródi 1 , 3 ,
• Beatrix Péter 1 ,
• Bálint Szabó 2 , 4 ,
• Alexa Vörös 1 ,
• Nicolett Kanyó 1 ,
• Inna Székács 1 &
• Robert Horvath 1  

Scientific Reports volume  14 , Article number:  11719 ( 2024 ) Cite this article

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• Biomaterials
• Optics and photonics

Cell adhesion experiments are important in tissue engineering and for testing new biologically active surfaces, prostheses, and medical devices. Additionally, the initial state of adhesion (referred to as nascent adhesion) plays a key role and is currently being intensively researched. A critical step in handling all adherent cell types is their dissociation from their substrates for further processing. Various cell dissociation methods and reagents are used in most tissue culture laboratories (here, cell dissociation from the culture surface, cell harvesting, and cell detachment are used interchangeably). Typically, the dissociated cells are re-adhered for specific measurements or applications. However, the impact of the choice of dissociation method on cell adhesion in subsequent measurements, especially when comparing the adhesivity of various surfaces, is not well clarified. In this study, we demonstrate that the application of a label-free optical sensor can precisely quantify the effect of cell dissociation methods on cell adhesivity, both at the single-cell and population levels. The optical measurements allow for high-resolution monitoring of cellular adhesion without interfering with the physiological state of the cells. We found that the choice of reagent significantly alters cell adhesion on various surfaces. Our results clearly demonstrate that biological conclusions about cellular adhesion when comparing various surfaces are highly dependent on the employed dissociation method. Neglecting the choice of cellular dissociation can lead to misleading conclusions when evaluating cell adhesion data from various sources and comparing the adhesivity of two different surfaces (i.e., determining which surface is more or less adhesive).

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Introduction.

The significance of studying cell adhesion has become essential in various fields, including cell biology, materials science, and surface chemistry. The study of cell adhesion can provide insights into fundamental principles of cell biology, such as the role of cell-surface interactions in cell differentiation and tissue development. By understanding how cells adhere to different materials, it becomes possible to develop new biologically active surfaces and medical devices 1 , 2 that can interact with cells in specific ways. These advancements are applied in the design of novel materials with both cell-repellent and cell-adhesive properties. Additionally, a thorough characterization of cancer cell adhesion can contribute to our understanding of factors that contribute to tumor progression and metastasis.

A large number of cells are adherent and attach to the extracellular matrix components (ECM), which is a complex network of proteins and other molecules that surrounds cells and provides structural support, via integrin receptors. Integrins are expressed as heterodimers, their composition determines their ligand specificity 3 , 4 . The tripeptide Arg–Gly–Asp (RGD) was identified as the minimal pattern required to trigger cell adhesion 5 , 6 , 7 . Integrins form different types of adhesion complexes which are linked to the intracellular actin-cytoskeleton system through various adaptor molecules and thus enable mechanotransduction 8 . Mechanotransduction plays a crucial role in regulating cellular functions, differentiation, and proliferation 9 , 10 , 11 . In addition to integrins, cells can also use other adhesion molecules, such as cadherins or selectins, to adhere to other cells or the ECM. These molecules are involved in a range of biological processes, such as tissue development, immune response, and wound healing.

Cell dissociation methods and reagents are widely used in tissue culture laboratories for handling adherent cell types, but their impact on subsequent measurements or tissue engineering is poorly understood. Cell dissociation can have unintended effects on cell behavior, such as altering cell surface markers or inducing stress responses, which can impact the accuracy and reproducibility of downstream experiments.

In standard tissue culture-treated polystyrene dishes cell adhesion is facilitated by the treatment of the bare polystyrene surface with various, mainly oxidizing methods to introduce most importantly hydroxyl and carbonyl groups, and rendering the surface more hydrophilic, and supporting cell adhesion 12 . Upon initial attachment cells produce various ECM proteins, for example, fibronectin and laminin, and thus enable cell adhesion in tissue culture dishes with integrins and form adhesion complexes 13 .

Carrying out experiments with adherent cells requires their dissociation from the bottom of culture dishes. How various cell detachment strategies affect phenotypes of adherent cells is an important issue in cell biology. Mechanical-, enzymatic- and chelation-based cell dissociation approaches are all known for their ability to alter viability, receptor and gene expression, moreover cell response to various stimuli, by disrupting the status quo in adherent cells. This process is unavoidable since the detachment of cells is necessary for experimentation and biomedical applications as well.

Integrins have multiple binding sites for divalent cations and require these ions to take their active and conformation enabling effective ligand–integrin interactions 14 . Chelating these ions through the addition of chelating agents such as EDTA and EGTA renders the integrins, necessary for adhesion, inactive and thus promotes cell dissociation from the substrate. While in some cases EDTA alone is enough to remove cells from the surface, in many cases it is not effective enough and many cells remain attached, resulting in a lower yield. In these cases, more effective methods are required. Moreover, in the case of mesenchymal stem cells, (MSCs) using EDTA also results in lower viability, while retaining the higher chemotactic activity of the cells when compared to enzyme-based methods 15 , 16 .

The application of proteolytic enzymes, such as trypsin, results in the dissociation of the cells from the surface they adhered to. Proteolytic enzymes act by digesting extracellular proteins, and components of the ECM, the adhesion complexes, and partially the glycocalyx 17 . The superiority of trypsin treatment over the mechanical methods seems established 18 moreover scraping results in lower viability while increasing the amount of necrotic and apoptotic cells 19 , 20 .The regularly used 0.05% trypsin for 2 min protocol for cell dissociation does not lead to complete removal of the glycocalyx 21 . However, the density of the glycocalyx seems to contribute to the fine morphology of the plasma membrane 22 . The efficiency of trypsin to cleave certain membrane proteins is difficult to predict even based on the number of trypsin cleavage sites present in each protein, some remain unaffected, while others are almost completely removed in the case of MSCs after 5 min of incubation with the enzyme 23 . Mutein et al. looked at the expression of endothelial markers and found that based on flow cytometry data 1.5 min of incubation with the trypsin–EDTA treatment did not lead to significant changes in the expression of five markers 24 . In general, it is suggested that the expression of each cell surface antigen is to be tested specifically, and the detachment method is to be fine-tuned accordingly 25 . Trypsinization can also influence further biological processes through the proteolysis of the cell surface proteome. This is for example illustrated by the inability of trypsinized tumor cell lysates to induce dendritic cell (DC) maturation compared to tumor cells detached by EDTA only 26 . The intracellular trafficking of integrins allows cells to monitor their environment’s adhesive properties, therefore, it is tempting to speculate that mild trypsinization does not lead to impaired integrin-mediated cellular functions 27 . However, when Doaga et al. compared trypsin–EDTA with only EDTA-treated NIH-3T3 mouse fibroblasts, they found that the adhesion of the non-trypsin-treated cells is faster to collagen I-based scaffolds 28 . Another more direct assay applies single-cell force spectrometry to determine the adhesive properties of cells detached by using either EDTA or trypsin treatment. They found that HeLa cell adhesion properties are not affected by trypsin treatment, unlike in the case of mouse embryonic kidney fibroblasts, and that each cell type is to be assessed for its post-detachment adhesive properties 29 . Trypsin treatment of cells also resulted in alterations in their electrophoretic mobility. Importantly, it was shown that trypsin treatment of HeLa and HL-60 cells affected their electrophoretic mobility (EPM) and the net surface charge can both increase and decrease 30 .

We demonstrate that the application of a label-free surface sensitive optical sensor can precisely quantify the effect of cell dissociation methods on the single cell 31 and population level 32 , 33 , 34 cellular adhesivity. Importantly, one can potentially reveal subpopulations with different behaviors even if the population mean is the same. The shape of the single-cell adhesion distribution can also give us additional information 35 . These detailed experiments can be essential in cancer research, where the individual tumor cells can have highly different behaviors 36 , 37 . The monitoring of cellular adhesion with the high resolution provided by the optical measurements allowed us to quantify the effect of most typical cell dissociation methods on cell adhesion without interfering with the physiological state of the cells. Our findings highlight the importance of carefully considering cell dissociation methods when conducting downstream experiments or tissue engineering and demonstrate the potential of using optical sensors to improve our understanding of cell behavior. In the present study, we demonstrate that the dissociation reagents have a short-term effect on cell adhesion and its population distribution. The experiments involved the use of enzymatic (trypsin–EDTA mixture) and non-enzymatic (EDTA and commercially available dissociation buffer, which contains salts, chelating agents, and cell-conditioning agents in calcium-free and magnesium-free phosphate-buffered saline) dissociation reagents on HeLa cancer cells. Three different surfaces (non-coated (biocompatible metal-oxide), fibronectin-coated, and RGD-coated) were used to study whether the observed effects of dissociation protocols on cell adhesion depend on the adhesion motifs available to the cells. Our results are noteworthy as they demonstrate that when comparing the adhesivity of two different surfaces, such as determining which surface is more or less adhesive, the outcome depends on the cell dissociation method employed.

Materials and methods

All chemicals and reagents were obtained from Sigma–Aldrich Chemie GmbH (Schelldorf, Germany) unless stated otherwise.

Cell culture

HeLa cells (93021013 Sigma–Aldrich) were cultured in a humidified incubator at 37 °C with 5% CO 2 in 60 mm tissue culture petri dishes in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 100 μg/ml streptomycin and 100 U/ml penicillin mixture(Merck, Germany), 10% fetal bovine serum (Biowest SAS, France) and 4 mM l -glutamine (Merck, Germany).

Preparation of surface coatings

1 mg fibronectin (human plasma, 354008 Corning) was suspended in 1 ml sterile water and was allowed to go into solution at room temperature for 30 min. The solution was diluted with Dulbecco’s phosphate-buffered saline (DPBS) (pH 7.4) to 31 μg/ml. Each well was coated by adding 10 μl of fibronectin solution. The plate was centrifuged for 10 s at 130× g and then incubated at room temperature for 1 h. The remaining material was aspirated, and each well was rinsed with sterile water three times (40–50 μl per well). The plate was air-dried in a hood overnight at room temperature. It was then sealed with aluminum foil and stored at 4 °C. For the experiment, it was used within 2 weeks from the preparation date.

The polymer powders were dissolved in 10 mM 2-[4-(2-hydroxyethyl)piperazin-1-yl]ethanesulfonic acid (HEPES) at 1 mg/ml concentration and stored at − 20 °C. PP (poly( l -lysine)- graft -poly(ethylene glycol) (PLL- g -PEG, [PLL(20)-g(3.5)-PEG(2)]) (SZ42-28, SuSoS AG, Dübendorf, Switzerland)) and PPR (PLL- g -PEG-DBCO-Mal)-CKK-(Acp)-(Acp)-(Acp)- GRGDS (hereafter PP-DBCO-R), obtained as powders from SuSoS AG, Dübendorf, Switzerland (SZ43-74)), were mixed in 1:1 ratio and diluted with 10 mM HEPES to 0.5 mg/ml concentration. 30 μl of this solution was added to each well and the plate was shaken gently for 30 min at room temperature. The solution was aspirated, then the wells were rinsed twice with 40 μl sterile water and filled with the assay buffer (20 mM 2-[4-(2-hydroxyethyl)piperazin-1-yl]ethanesulfonic acid (HEPES) in Hank’s balanced salt solution (HBSS), pH 7.4).

Resonant waveguide grating optical sensor with single-cell resolution

The Epic Cardio (Corning Incorporated, Corning, NY, USA) is a high-resolution and high-throughput resonant waveguide grating (RWG)-based label-free single-cell resolution optical sensor. For our experiments, Corning Epic 384 well cell assay microplates were used. The bottom of each well has a 2 × 2 mm 2 RWG sensor area which consists of 80 × 80 pixels with the pixel size of 25x × 25 μm. The microplate is illuminated from below with a tunable broadband light source (wavelength between 825 and 840 nm) and the grating areas couple the light into the thin high refractive index biocompatible Nb 2 O 5 , waveguide layer, which is located on a glass substrate. The coupled light propagates in the waveguide layer with a series of total reflections and then reflects onto a CCD camera. The propagating light creates an evanescent electromagnetic field that decays exponentially with distance from the sensor surface and can interact with the sample placed on the waveguide surface. The instrument detects the wavelength shift of the reflected light with a sensitivity of 0.25 pm within 15,000 pm via the CCD camera with a microplate read time of 3 s.

Cell dissociation protocols

The media, reagents, and buffers were warmed up to 37 °C. Adhered HeLa cells in the cell culture dish were rinsed twice with DPBS. After that, the cells were detached from the surface by adding 1 ml of (a) 0.05% trypsin, 0.02% EDTA mixture (Merck, Germany) and incubating for 2 min at room temperature, then dissociation reagent was removed, (b) Gibco’s enzyme-free cell dissociation buffer (13151014 Gibco), incubating for 2 min at room temperature then discarding the buffer and tapping gently the bottom of the dish for 5 min with the lid on, (c) 10 mM EDTA in DPBS, incubating for 2 min at room temperature then discarding the buffer and tapping gently the bottom of the dish for 5 min with the lid on. The reaction of dissociation agents was terminated by adding 1 ml of completed culture medium, cells were collected from the surface of the culture dish, transferred into a 15 ml Falcon tube, and then centrifuged for 5 min at 200× g . The supernatant was discarded and cells were resuspended in the 3 ml of assay buffer. The cells were centrifuged again for 5 min at 200× g , the supernatant was discarded, and the cells were resuspended in the assay buffer. After cell counting the cell suspension was diluted to the final concentration of 100 cells/25 μl and added into the wells of the sensor plate for the measurement (Fig.  1 ).

Schematic illustration of the different cell dissociation protocols. First, the cells were treated with different cell dissociation buffers and then shaken if needed. Next, the cells were dissociated from the bottom of the cell culture dish with cell culture media and centrifuged twice to remove the media. Finally, the cells were brought into suspension and diluted with the assay buffer.

Cell adhesion assay

After the surface coating procedure, 25 μl of assay buffer was used in each well for the baseline in the Epic Cardio sensor. Then the cell suspension was pipetted into the wells, and they were let to fully adhere to the sensor surface for 90 min (Fig.  2 A, B ). After the sensor signal, measured as the wavelength shift (WS) of the pixel corresponding to the maximal signal of the cell, reached a stable level (see Fig.  2 C), the microplate was removed from the instrument and the morphology of the cells was examined with optical microscopy.

Schematic illustration of the measurement with single-cell resolution optical sensor. The cells were added to 12 wells in the microplate and measured ( A ). The sensor can detect refractive index change above the surface in an approximately 150 nm thick layer, which corresponds to the evanescent field [( B ), ‘side view’ part, red zone]. The integrin-ligand binding happens in this layer, thus the sensor can monitor cell adhesion and provides kinetic data of the process ( C ).

Statistical analysis

Kruskal–Wallis H-test with Wilcoxon post-hoc test was used to analyze the difference between the cell dissociation methods and cell adhesion surfaces because our data doesn’t show normal distribution.

Results and discussion

The adhesion signal of HeLa cells was measured on three different surfaces (50% PPR, fibronectin, and non-coated) with three different cell dissociation methods (Gibco, EDTA, TE). The sensor data were analyzed as previously described in Sztilkovics et al. Briefly, each cell occupied 1–4 pixels on the sensor surface, and we used the pixel which corresponded to the maximal wavelength shift in our analysis 31 .

The HeLa cell line was selected as a representative cell line for this study due to its widespread use in research as a human cell line, providing insights into numerous fundamental biological processes. Fibronectin is a large extracellular matrix glycoprotein that contains the RGD sequence, which serves as a recognition site for integrins. The RGD-binding integrins comprise α5β1, α8β1, αvβ1, αvβ3, αvβ5, αvβ6, αvβ8, and αIIbβ3 3 . HeLa cells express α5β1, αvβ3, and αvβ5 integrins. These integrins recognize different regions of fibronectin, including the RGD motif. The interaction between integrins and fibronectin is not solely dependent on the RGD sequence; other regions as so-called synergy site Pro–His–Ser–Arg–Asn (PHSRN) of fibronectin also contribute to the binding. The affinity and specificity of integrin binding to fibronectin can vary. The response of other cell types would depend on their specific integrin repertoire and the adhesive surfaces available. Trypsin cleaves peptide bonds at the C-terminal side of lysine and arginine residues. The reaction of trypsin on different types of integrins can vary.

To compare the single-cell adhesion signals, we plotted the distribution of the last data point of the adhesion signal measured as wavelength shift (WS end ). Each distribution consists of between 185 and 380 single-cell signals from at least two independent measurements. We tested the reproducibility of the experiments and found that there is no significant difference in the adhesion signals distribution with a correlation coefficient of 0.965. A lognormal distribution was obtained for all surfaces with every dissociation method, as shown in Fig.  3 . Non-parametric Kruskal–Wallis H-test test with Wilcoxon signed-rank test was used to quantitatively compare the effect of the different dissociation methods on the different surfaces. The largest effect of the different cell dissociation methods was observed on the non-coated surface, whereas there was barely a difference on the fibronectin-coated surface.

The adhesion signal distribution of HeLa fitted with lognormal distributions, on different surfaces (columns) and with different cell dissociation methods (rows). In the last row the median, mean, and standard deviation of the distributions of the different dissociation methods are depicted. In the last column the median, mean, and standard deviation of the distributions of the different cell adhesion surfaces are depicted. The significance analysis was carried out with a non-parametric Kruskal–Wallis H-test test with Wilcoxon signed-rank test. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

To determine the effect, we compared the distribution of WS end , a measurement of cell adhesion strength, of single cells harvested using different cell dissociation reagents on various surfaces (Fig.  3 and Table 1 ).

Firstly, we compared the distribution of cell adhesion signals obtained by collecting the cells using different dissociation reagents on the same surface. On fibronectin-coated surfaces, only the Gibco-TE comparison showed a significant difference. Interestingly, this comparison showed no difference on the 50% PPR surface. However, both the Gibco-EDTA and EDTA–TE comparisons showed significant differences in the distribution of cell adhesion signals. Surprisingly, all comparisons resulted in significant differences in the cell adhesion distribution on the non-coated surface. The application of TE resulted in a more uniform cell adhesion distribution profile, leading to highly significant differences when compared to the enzyme-free EDTA and Gibco-based methods. Altogether, these results suggest that the more biologically relevant the surface studied for cell adhesion, the less the cell adhesion is affected by employing another dissociation method.

Next, the WS end distribution of single cells obtained by various cell harvesting methods was compared on different surfaces. Both Gibco and EDTA reagents showed similar effects, with both only showing significant differences in the comparison of fibronectin and 50% PPR. However, in the case of TE, these comparisons yielded inverted results, and significant differences were only found when comparing cell adhesion on non-coated surfaces with both 50% PPR and fibronectin surfaces.

These results underscore the significant impact of different cell dissociation reagents on cell adhesion across different surfaces. To further demonstrate the importance of cell dissociation in the process of cell adhesion, we also compared the distribution of WS end signals across all conditions. For example, the comparison of cell adhesion on non-coated and 50% PPR-coated surfaces did not result in significant differences when cells were harvested using EDTA. However, replacing EDTA with either Gibco or TE for cells applied on the non-coated surface showed highly significant differences in the distribution of cell adhesion signals for the same comparison. Similarly, the comparison of the 50% PPR surface with the fibronectin surface, when both cell populations were harvested using EDTA, showed highly significant differences. However, if the cells on the 50% PPR surface were collected using Gibco or TE, the differences were abolished and non-significant.

Our findings highlight the contribution of the cell harvesting method to cell adhesion, especially in the early and nascent phases of the process, when adhesion complexes have a submicron size (~ 0.1 μm in diameter) 38 dot-like 39 structure that exists only for 2–10 min, and consists of about 50 integrins with a key role in the attachment process of cells to the ECM. Furthermore, our results suggest that the measured differences on various surfaces are highly dependent on the cell harvesting method used. These considerations highlight the importance of careful interpretation of previously published data. Especially since not only TE and EDTA-based harvesting modified the cell adhesion but also the additives present in commercial cell dissociation reagents. Next to changes in receptor expression and net surface charge of the cell, viability likely also plays a role in the process of early cell adhesion investigated in this study.

This difference in the cell adhesion distribution between the surfaces can be due to the different biological ‘activeness’, where on the fibronectin-coated surface, cells can find various cell adhesion motifs readily available so the extent of the digestion of the surface proteins does not matter a lot. On the 50% PPR-coated surface, where only RGD cell adhesion motifs are available to the cells, for optimal adhesion the cells need their integrin receptor, glycocalyx, and surface proteins intact 21 . Therefore, the extent of the digestion of these elements from the cell surface will influence the cell adhesion, the weakest adhesion was observable with the enzyme-treated cells (Fig.  3 ). We also compared the effect of glycocalyx digestion with ChrABC enzyme 35 to the effect of the different cell dissociation methods. Importantly, enzyme addition resulted in a wider distribution, similarly to the effects of EDTA and GIBCO compared to TE (see Fig. S1 ). However, enzyme addition at large concentration resulted in a weakly adhering subpopulation completely missing in case of cell dissociation treatment, and interestingly the changes in the median and mean were opposite. Therefore, more work is needed to in depth understand the molecular scale changes caused by the various dissociation protocols, presumably being a complex process influencing many cell adhesion factors in a complicated manner. On the non-coated surface there are no cell adhesion motifs, the cells adhere to surface through a passive process, so digestion which effect the cell’s charge greatly influences the cell adhesion.

In the images shown in Fig.  4 , it is evident that cells on the non-coated surface exhibit a hemisphere-like shape, indicating passive adhesion. However, moving to a more biologically active substratum, the 50% PPR-coated surface, the cells appear to be more spread out and exhibit more irregular shapes.

Microscopic image of HeLa cells on ( A ) non-coated and ( B ) 50% PPR-coated sensor surface. Holomonitor images of HeLa cells on ( C ) non-coated and ( D ) 50% PPR-coated sensor surface. For all images TE was used as the dissociation protocol. On the non-coated surface, there are no cell adhesion motifs, the cells adhere to the surface through a passive process, so the cells have a hemispherical shape.

As shown in Fig.  5 D, a signal drop was sometimes observed in the single-cell kinetic data, which usually follows a simple sigmoidal curve. We compared this phenomenon between different surfaces and cell dissociation methods and plotted the ratio of the maximum of the curve (WS max ) and the last data point (WS end ) for each individual cell. The results are shown in Fig.  5 . Clearly, there is no drop in the single-cell adhesion signal if the dot representing the given cell is on the y = x line. We found that this phenomenon was only notable on the 50% PPR-treated surface regardless of the dissociation methods.

( A – C ) Correlation scatterplots show the last wavelength shift values plotted (WS end ) as a function of the maximal wavelength shift (WS max ) for each measured cell, detached with the various dissociation methods (orange, purple, green) on the three different surfaces. ( D ) Normalized (with WS max ) single-cell kinetic data with a drop in the wavelength signal.

To further analyze this phenomenon, we plotted the distributions of the extent of the signal drop for each cell dissociation method on all surfaces (Fig. S2 ). Subsequently, we quantified the size of the subpopulation exhibiting a notable signal drop (1−WSend /WSmax > 0.1 or 0.2), as presented in Table S1 and Table S2 . We noticed that the size of the drop depended greatly on the type of surface but not the dissociation method.

Of note, in the employed coatings, the averaged RGD-RGD density is predefined, around 8.63 nm, and the RGD motifs deposited are largely localized 21 . This is in contrast to other types of coatings where freely diffusing RGD motifs are deposited using thiol groups on gold 40 , 41 . Therefore, this relatively rigid surface structure of RGD motifs can potentially lead to suboptimal local RGD–RGD distances, leading to adhesion weakening, through creating mature adhesion contacts with decreased stability. It is still interesting why this phenomenon only happens for a distinct subpopulation of cells (see SI Table S1 , S2 ). On the non-coated surface, there is a strong passive adhesion, independent of adhesion contacts, and the surface is also completely homogeneous, therefore the cells do not have the opportunity or the driving force for any kind of movement governed by biologically active motifs on the surface. We believe, the signal drop phenomenon cannot be attributed to increased motility, as evidenced by the literature demonstrating that cell motility is lower on the PPR-coated surface compared to the fibronectin-coated surface 42 . Additionally, the presence of the PHSRN synergy peptide in the fibronectin molecules promotes cell motility and migration, aligning with the aforementioned statement 43 , 44 . We believe, however, more work should be done to test the above hypotheses and reveal the exact nature of the subpopulation showing relatively fast adhesion signal drops after reaching the adhesion plateau.

Due to the fact, that the cell adhesion signals are clearly dependent on not only the surface but also on the above-investigated cell dissociation methods, it is important to consider other dissociation methods to eliminate the observed effects from the adhesion measurements. The most recent reagent-free cell detachment methods, such as acoustic waves 45 , 46 and temperature-responsive cell culture dishes 47 , 48 , have the potential to enable cell detachment with minimal impact on the cells. However, the effects of these approaches have yet to be compared in detail to classical methods.

Conclusions

We demonstrated that the various cell dissociation methods (Gibco, EDTA, TE) have a significant impact on cell adhesion, and this effect is also influenced by the surface (non-coated biocompatible metal-oxide, fibronectin-coated, and RGD-coated) used in the adhesion experiments. The most pronounced effect of the dissociation methods was observed on the non-coated metal-oxide surface, while only a slight difference was discernible on the fibronectin-coated surface. Our measurements were conducted using a high-throughput label-free optical sensor, allowing us to eliminate the influence of labeling and enabling the measurement of adhesion signals at the single-cell level with statistics. Consequently, we were able to in-depth analyze the population distribution of cell adhesion signals on multiple surfaces using different cell dissociation methods. Our findings emphasize the importance of using enzyme- and chelating-free cell dissociation techniques in adhesion measurements. Additionally, it is crucial to consider the impact of different cell dissociation methods during the validation of new biologically active surfaces and other biomaterials, especially when comparing the adhesivity of various surfaces. Conclusions stating that one surface is more adhesive than the other can be misleading or even incorrect if the choice of cell dissociation method is overlooked. Moreover, the platform used to study cellular adhesion kinetics could potentially open up novel application directions in antibody screening 49 , monitoring environmental contaminants 50 and antibody-based biosensors 51 , 52 , 53 due to its excellent lateral and temporal resolution and flexibility in available surface coatings.

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Fritsche, A. et al. Measuring bone cell adhesion on implant surfaces using a spinning disc device. Mater. Werkst. 41 , 83–88 (2010).

Article   CAS   Google Scholar  

Lu, J., Rao, M. P., MacDonald, N. C., Khang, D. & Webster, T. J. Improved endothelial cell adhesion and proliferation on patterned titanium surfaces with rationally designed, micrometer to nanometer features. Acta Biomater. 4 , 192–201 (2008).

Article   CAS   PubMed   Google Scholar  

Chastney, M. R., Conway, J. R. W. & Ivaska, J. Integrin adhesion complexes. Curr. Biol. 31 , R536–R542 (2021).

Kanchanawong P, Calderwood DA. Organization, dynamics and mechanoregulation of integrin-mediated cell–ECM adhesions. Nat. Rev. Mol. Cell Biol. 24 , 142–161. (2023).

Sun, Z., Guo, S. S. & Fässler, R. Integrin-mediated mechanotransduction. J. Cell Biol. 215 , 445–456 (2016).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Arnaout, M. A., Goodman, S. L. & Xiong, J.-P. Structure and mechanics of integrin-based cell adhesion. Curr. Opin. Cell Biol. 19 , 495–507 (2007).

Schumacher, S. et al. Structural insights into integrin α5β1 opening by fibronectin ligand. Sci. Adv. 7 , eabe9716 (2021).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Wang, N. Review of cellular mechanotransduction. J. Phys. D Appl. Phys. 50 , 233002 (2017).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Martino, F., Perestrelo, A. R., Vinarský, V., Pagliari, S. & Forte, G. Cellular mechanotransduction: From tension to function. Front. Physiol. 9 , 824 (2018).

Article   PubMed   PubMed Central   Google Scholar  

Jansen, K. A., Atherton, P. & Ballestrem, C. Mechanotransduction at the cell-matrix interface. In Seminars in Cell & Developmental Biology 75–83 (Elsevier, 2017).

Google Scholar  

Jiang, X. et al. Mechanoregulation of proliferation. Mol. Cell. Biol. 29 , 5104–5114 (2009).

Lerman, M. J., Lembong, J., Muramoto, S., Gillen, G. & Fisher, J. P. The evolution of polystyrene as a cell culture material. Tissue Eng. Part B Rev. 24 , 359–372 (2018).

Streuli, C. H. & Bissell, M. J. Expression of extracellular matrix components is regulated by substratum. J. Cell Biol. 110 , 1405–1415 (1990).

Zhang, K. & Chen, J. The regulation of integrin function by divalent cations. Cell Adhes. Migr. 6 , 20–29 (2012).

Article   Google Scholar  

Garg, A. et al. Non-enzymatic dissociation of human mesenchymal stromal cells improves chemokine-dependent migration and maintains immunosuppressive function. Cytotherapy 16 , 545–559 (2014).

Fong, D., Duceppe, N. & Hoemann, C. D. Mesenchymal stem cell detachment with trace trypsin is superior to EDTA for in vitro chemotaxis and adhesion assays. Biochem. Biophys. Res. Commun. 484 , 656–661 (2017).

Wijesekara, P. et al. Accessing and assessing the cell-surface glycocalyx using DNA origami. Nano Lett. 21 , 4765–4773 (2021).

Bundscherer, A. et al. Cell harvesting method influences results of apoptosis analysis by annexin V staining. Anticancer Res. 33 , 3201–3204 (2013).

PubMed   Google Scholar  

Nowak-Terpiłowska, A., Śledziński, P. & Zeyland, J. Impact of cell harvesting methods on detection of cell surface proteins and apoptotic markers. Braz. J. Med. Biol. Res. https://doi.org/10.1590/1414-431x202010197 (2021).

Yan, G. E. & Efferth, T. Cell harvesting methods affect cellular integrity of adherent cells during apoptosis detection. Anticancer Res. 38 , 6669–6672 (2018).

Kanyo, N. et al. Glycocalyx regulates the strength and kinetics of cancer cell adhesion revealed by biophysical models based on high resolution label-free optical data. Sci. Rep. https://doi.org/10.1038/s41598-020-80033-6 (2020).

Shurer, C. R. et al. Physical principles of membrane shape regulation by the glycocalyx. Cell 177 , 1757–1770 (2019).

Tsuji, K. et al. Effects of different cell-detaching methods on the viability and cell surface antigen expression of synovial mesenchymal stem cells. Cell Transpl. 26 , 1089–1102 (2017).

Mutin, M., George, F., Lesaule, G. & Sampol, J. Reevaluation of trypsin-EDTA for endothelial cell detachment before flow cytometry analysis. Endothelium 4 , 289–295 (1996).

Lai, T.-Y. et al. Different methods of detaching adherent cells and their effects on the cell surface expression of Fas receptor and Fas ligand. Sci. Rep. 12 , 5713 (2022).

Herzog, G. I. et al. Quality of tumor lysates used for pulsing dendritic cells is influenced by the method used to harvest adherent tumor cells. BMC Res. Notes 4 , 1–4 (2011).

Moreno-Layseca, P., Icha, J., Hamidi, H. & Ivaska, J. Integrin trafficking in cells and tissues. Nat. Cell Biol. 21 , 122–132 (2019).

Doagă, I. O., Savopol, T., Neagu, M., Neagu, A. & Kovács, E. The kinetics of cell adhesion to solid scaffolds: An experimental and theoretical approach. J. Biol. Phys. 34 , 495–509 (2008).

Schubert, R. et al. Assay for characterizing the recovery of vertebrate cells for adhesion measurements by single-cell force spectroscopy. FEBS Lett. 588 , 3639–3648 (2014).

Akagi, T., Suzuki, M., Sato, T. & Ichiki, T. On-chip evaluation of damage on cell surfaces induced by cell dissociation agents. Jpn. J. Appl. Phys. 46 , 6404 (2007).

Article   ADS   CAS   Google Scholar  

Sztilkovics, M. et al. Single-cell adhesion force kinetics of cell populations from combined label-free optical biosensor and robotic fluidic force microscopy. Sci. Rep. https://doi.org/10.1038/s41598-019-56898-7 (2020).

Orgovan, N. et al. Dependence of cancer cell adhesion kinetics on integrin ligand surface density measured by a high-throughput label-free resonant waveguide grating biosensor. Sci. Rep. https://doi.org/10.1038/srep04034 (2014).

Fang, Y. Label-free and non-invasive biosensor cellular assays for cell adhesion. J. Adhes. Sci. Technol. https://doi.org/10.1163/016942409X12598231568267 (2010).

Fang, Y. Label-free biosensors for cell biology. Int. J. Electrochem. https://doi.org/10.4061/2011/460850 (2011).

Kanyo, N. et al. Single-cell adhesivity distribution of glycocalyx digested cancer cells from high spatial resolution label-free biosensor measurements. Matrix Biol. Plus 14 , 100103 (2022).

Dexter, D. L. et al. Heterogeneity of cancer cells from a single human colon carcinoma. Am. J. Med. 71 , 949–956 (1981).

Meacham, C. E. & Morrison, S. J. Tumour heterogeneity and cancer cell plasticity. Nature 501 , 328–337 (2013).

Bidone, T. C., Skeeters, A. V., Oakes, P. W. & Voth, G. A. Multiscale model of integrin adhesion assembly. PLoS Comput. Biol. 15 , e1007077 (2019).

Alexandrova, A. Y. et al. Comparative dynamics of retrograde actin flow and focal adhesions: Formation of nascent adhesions triggers transition from fast to slow flow. PLoS One 3 , e3234 (2008).

Yoon, S.-H. & Mofrad, M. R. K. Cell adhesion and detachment on gold surfaces modified with a thiol-functionalized RGD peptide. Biomaterials 32 , 7286–7296 (2011).

Gujraty, K. V. et al. Thiol-mediated anchoring of ligands to self-assembled monolayers for studies of biospecific interactions. Langmuir 22 , 10157–10162 (2006).

Kim, J. P. et al. Mechanism of human keratinocyte migration on fibronectin: Unique roles of RGD site and integrins. J. Cell. Physiol. 151 , 443–450 (1992).

Livant, D. L. et al. The PHSRN sequence induces extracellular matrix invasion and accelerates wound healing in obese diabetic mice. J. Clin. Invest. 105 , 1537–1545 (2000).

Hu, Y. et al. Highly stable fibronectin-mimetic-peptide-based supramolecular hydrogel to accelerate corneal wound healing. Acta Biomater. 159 , 128–139 (2023).

Salari, A., Appak-Baskoy, S., Coe, I. R., Tsai, S. S. H. & Kolios, M. C. An ultrafast enzyme-free acoustic technique for detaching adhered cells in microchannels. RSC Adv. 11 , 32824–32829 (2021).

Kurashina, Y. et al. Enzyme-free release of adhered cells from standard culture dishes using intermittent ultrasonic traveling waves. Commun. Biol. 2 , 393 (2019).

Kobayashi, J., Akiyama, Y., Yamato, M., Shimizu, T. & Okano, T. Design of temperature-responsive cell culture surfaces for cell sheet-based regenerative therapy and 3D tissue fabrication. In Cutting-Edge Enabling Technologies for Regenerative Medicine (eds Heung, J. C. et al. ) 371–393 (Springer, 2018).

Chapter   Google Scholar  

Nakao, M. et al. Phenotypic traits of mesenchymal stem cell sheets fabricated by temperature-responsive cell culture plate: Structural characteristics of MSC sheets. Stem Cell Res. Ther. 10 , 1–14 (2019).

Shi, Y. Z. et al. Sculpting nanoparticle dynamics for single-bacteria-level screening and direct binding-efficiency measurement. Nat. Commun. 9 , 815 (2018).

Farkas, E. et al. Label-free optical biosensor for real-time monitoring the cytotoxicity of xenobiotics: A proof of principle study on glyphosate. J. Hazard. Mater. 351 , 80–89 (2018).

Liu, L. et al. TriPleX ™ waveguide-based fluorescence biosensor for multichannel environmental contaminants detection. Biosens. Bioelectron. 106 , 117–121 (2018).

Liu, L. et al. An array fluorescent biosensor based on planar waveguide for multi-analyte determination in water samples. Sens. Actuators B Chem. 240 , 107–113 (2017).

Farkas, E. et al. Development and in-depth characterization of bacteria repellent and bacteria adhesive antibody-coated surfaces using optical waveguide biosensing. Biosensors 12 , 56 (2022).

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Acknowledgements

This research was funded by the Hungarian Academy of Sciences [Lendület (Momentum) Program], the National Research, Development and Innovation Office (NKFIH) [ERC_HU, PD 131543 for B.P., PD 134195 for Z.S. K131425, and KKP_19 Programs] and the National Research, Development, and Innovation Fund of Hungary under Grant TKP2021-EGA-04. Project no. TKP2021-EGA-04 has been implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021 funding scheme. Supported by the KDP-2021 program of the Ministry of innovation and Technology from the source of the National Research, Development and Innovation Fund. This paper was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences (for B.P).

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Kinga Dóra Kovács, Zoltán Szittner, Beatrix Magyaródi, Beatrix Péter, Alexa Vörös, Nicolett Kanyó, Inna Székács & Robert Horvath

Department of Biological Physics, ELTE Eötvös University, Budapest, Hungary

Kinga Dóra Kovács & Bálint Szabó

Chemical Engineering and Material Sciences Doctoral School, University of Pannonia, Veszprém, Hungary

Beatrix Magyaródi

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RH established the research line and supervised the work. KDK, AV, NK, ISz performed the experiments. KDK, BP made manuscript figures. KDK, BP, BM, ISz, and RH analyzed the data, KDK, ZSz, BM, BP, BSz and RH wrote the manuscript. All involved in discusssions and checked the final verion.

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Kovács, K.D., Szittner, Z., Magyaródi, B. et al. Optical sensor reveals the hidden influence of cell dissociation on adhesion measurements. Sci Rep 14 , 11719 (2024). https://doi.org/10.1038/s41598-024-61485-6

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Received : 15 December 2023

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DOI : https://doi.org/10.1038/s41598-024-61485-6

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• v.28; 2024 Oct
• PMC11357813

Advanced tumor organoid bioprinting strategy for oncology research

Xiangran cui.

a Department of Orthopedics, The Second Hospital of Jilin University Changchun, 130041, PR China

Jianhang Jiao

Weibo jiang, zhonghan wang.

b Orthopaedic Research Institute of Jilin Province, Changchun, 130041, PR China

Associated Data

No data was used for the research described in the article.

Bioprinting is a groundbreaking technology that enables precise distribution of cell-containing bioinks to construct organoid models that accurately reflect the characteristics of tumors in vivo . By incorporating different types of tumor cells into the bioink, the heterogeneity of tumors can be replicated, enabling studies to simulate real-life situations closely. Precise reproduction of the arrangement and interactions of tumor cells using bioprinting methods provides a more realistic representation of the tumor microenvironment. By mimicking the complexity of the tumor microenvironment, the growth patterns and diffusion of tumors can be demonstrated. This approach can also be used to evaluate the response of tumors to drugs, including drug permeability and cytotoxicity, and other characteristics. Therefore, organoid models can provide a more accurate oncology research and treatment simulation platform. This review summarizes the latest advancements in bioprinting to construct tumor organoid models. First, we describe the bioink used for tumor organoid model construction, followed by an introduction to various bioprinting methods for tumor model formation. Subsequently, we provide an overview of existing bioprinted tumor organoid models.

Graphical abstract

Characteristics and applications of bioprinting in constructing tumor organoid models.

• • Bioprinting is a pioneering technology to construct tumor organoid models that more realistically reflect in vivo tumor characteristics.
• • Advanced biomaterials can precisely replicate the arrangement of tumor cells, providing a more realistic characterization of the in vivo tumor microenvironment.
• • This review highlights the various options of advanced biomaterials and combined bioprinting methods in constructing tumor organoid models for oncology reaearch.

1. Introduction

Malignant tumors are a significant public health problem and one of the leading causes of mortality globally [ 1 ]. It was estimated that tumors will impact approximately 28.4 million people globally in 2024, posing a significant risk to human health [ 2 , 3 ]. Owing to the complex heterogeneity of tumors and insufficient understanding of tumor development and invasion mechanisms [ 4 , 5 ], it is essential to increase the knowledge of tumor development and explore effective treatment methods.

Various in vivo and in vitro complex tumor models have been developed to advance the study of tumor pathology and promote progress in anti-tumor therapy. The 2D cell culture model provides hypothetical results related to the pathogenesis of tumors. However, the in vivo microenvironment is far more complex than that in 2D model, where the behavior of tumor cells is regulated by interactions between cells, cell-extracellular matrix interactions, and chemotaxis [ 6 ]. This can sometimes lead to contradictory results [ 7 , 8 ]. A large body of evidence suggests that three-dimensional cell culture models (3D models) are more physiologically relevant than 2D cell culture models. This has led to widespread adoption of 3D culture techniques to establish more reliable and complex tumor models [ 8 , 9 ]. 3D models allow for the replication of tumor migration and proliferation in vivo [ 10 ], and more accurately reflect tumor responses to anti-tumor drugs [ [11] , [12] , [13] , [14] ]. Conversely, xenograft models in mice exhibit significant potential but fall short in simulating tumor-specific microenvironments as the tumor stroma is typically replaced by host stroma. Additionally, tumor model constructed in immune-deficient mice cannot simulate interactions between tumors and immune cells, and issues such as ethical concerns, high costs, and technical differences make it a great challenge [ 15 ]. The extensive use of in vitro models has demonstrated their potential for application in medical tissue engineering [ [16] , [17] , [18] ].

Traditional 3D printing technology, also known as additive manufacturing or rapid prototyping, forms 3D geometric shapes by depositing inert materials layer by layer using computer-aided design [ 6 , 19 ]. The materials typically used in traditional 3D printing are geared towards non-biological substances such as plastics, metals, and ceramics. High temperatures and pressures are required during the printing process for material modification, catering to the needs of industries within the engineering and design sectors. As an extension of tradition 3D printing, bioprinting offers many advantages over traditional 3D printing [ 20 , 21 ]. Bioprinting is the process of manipulating cell-containing bioinks to create living structures [ [22] , [23] , [24] ]. By selecting appropriate printing methods, biologically active materials are printed layer by layer onto a receiving substrate or liquid reservoir. This enables efficient, cost-effective, consistent, and high-throughput creation of tumor organoid models containing complex geometric structures [ [25] , [26] , [27] ]. As bioprinting technology continues to mature, 3D scaffolds produced through bioprinting allow for the precise distribution and positioning of cells, active molecules, and biomaterials, enabling control over the shape and size of tumor organoids constructed using bioinks [ 28 ]. Furthermore, the complex structures created through bioprinting simulate the heterogeneous characteristics of the complex 3D tumor microenvironment, including cell arrangement, morphology, migration, and invasion, as well as cell-cell and cell-matrix interactions [ 29 , 30 ]. Consequently, tumor organoid models with different configurations and complexities have been studied for oncology research and drug discovery. Hence, bioprinting has become an ideal method for constructing in vitro tumor organoid models with batch-to-batch consistency and replicability [ 7 , 31 ] ( Fig. 1 ).

Schematic illustration of the characteristics and applications of bioprinting to construct tumor organoid models. (A) Deposition of bioink using different bioprinting methods. (B) Prominent advantages of bioprinting methods in constructing tumor organoid models. (C) Potential application value of tumor organoid models.

The bioactive substances used for bioprinting can be called "bioink," and is composed of biomaterials and biological units [ 32 ]. Bioinks are crucial for developing functional tissue or organoid structures via bioprinting [ 33 ]. Bioinks have the following characteristics: printability, biocompatibility, favorable mechanical properties, and biological stability [ [34] , [35] , [36] ]. Therefore, to successfully construct tumor organoid models, an appropriate bioink should be selected based on the structure of the tumor tissue to be printed and the specific tumor biology behaviors intended to be investigated.

Although bioprinting for constructing in vitro tumor organoid models encompasses various methods, it is mainly based on three core techniques: extrusion, inkjet, and light-curing bioprinting. Extrusion-based bioprinting is the most commonly used printing method [ 37 ]. This method forms continuous filaments through extrusion and stacking [ 38 ], whereas inkjet-based bioprinting forms tumor organoid structures by printing discrete droplets [ 39 ]. Light-curing bioprinting can enhance the resolution of printed structures by solidifying them layer by layer [ 40 ]. Appropriate biomaterials, referred to as bioinks, are selected based on the tumor tissue structure and the expected printing method. The precursor structures of tumor organs can be established through layer-by-layer stacking, and stable 3D scaffolds can be created using suitable crosslinking methods. As each printing method has specific applications, the desired hardness, spatial structure, and cellular composition of the tumor model should be considered when selecting the appropriate printing method.

Bioprinting is deemed as a novel and promising technology in constructing tumor organoids [ 41 ]. Due to the precise and controlling character of bioprinting, a variety of cell types and ECM can be set at pre-designed location. This technology enables the construction of organoid models with cellular diversity and complexity, allowing for more realistic organoid model development by controlling the layers and composition of bioprinting. Additionally, bioprinting technology can build 3D structures by layer-by-layer stacking cells and scaffold materials, thus enhancing the long-term stability and manipulability of organoid models. Most importantly, bioprinting technology enables the customized construction of organoid models such as utilizing patient's autologous cells to build individual organoid that can recurrent personal disease mechanisms and, drug response. In conclusion, researchers can leverage bioprinting technology to construct intricate organoid models, leading to a better understanding of the structure and function of biological systems and advancing the development of biomedical research and applications [ 42 ].

In general, 3D bioprinting is an innovative technology that is leading the way for conventional in vitro and in vivo cultivation models. Unlike 2D cell culture models, 3D bioprinting can precisely construct structures composed of cells and biological materials in a three-dimensional space, allowing for a more realistic simulation of the biological environment. Unlike traditional 3D models that typically use non-biological materials like plastics or metals, bioprinting technology utilizes biological materials and cells to create models, resulting in more biologically similar tissue structures and thus a more realistic biological environment. Bioprinting technology can avoid animal experiments, reduce the use of animals and related ethical issues, and provide a more sustainable, stable, and controllable experimental environment. Compared to traditional co-culture organ models where tumor cells are directly co-cultured with other cell types, bioprinting allows for precise control of cell positioning and distribution during the model construction process. The advantages of traditional co-culture models lie in their simplicity and cost-effectiveness, requiring no special equipment or complex operations. However, the unpredictable cell interactions lead to variability in experimental results. Additionally, for biological entities with complex geometric shapes and microstructures, the inability to control interactions limits their ability to simulate real biological scenarios, highlighting the advantages of 3D bioprinting [ 43 , 44 ].

In this review, we summarize the research on constructing tumor organoid models using bioprinting technology over the years. Literature on the construction of in vitro tumor organoid models by bioprinting is listed in Table 1 . Finally, we provide prospects for the future development of bioprinting technology to motivate further oncology research.

Summary of methods and materials for constructing in vitro tumor models using 3D bioprinting.

2. Materials and strategies for bioprinting

2.1. bioinks, 2.1.1. alginate-based bioinks.

Alginate is a polysaccharide mainly derived from brown algae and bacteria and is, widely used in bioprinting because of its excellent biocompatibility, low cost, rapid gelation, good printability, and versatility [ 84 , 85 ]. Alginate-based bioinks typically refer to bioinks containing alginate, which can be prepared by adding components such as alginate, crosslinkers, and cell suspensions. This type of bioink is commonly used in 3D printing systems. During the preparation process, alginate can serve as the scaffold material for the bioink, providing structural support and a conducive environment for cell growth. A low concentration of alginate-based bioink promotes cell activity and proliferation, but significantly reduces the mechanical strength of the 3D printed structure, leading to structural collapse. Conversely, a high concentration of alginate-based bioink decreases cell viability [ 86 ], limiting its application in simulating tumor organ formation. Moreover, alginate forms chemical crosslinks with divalent cations such as calcium (Ca 2+ ), strontium (Sr 2+ ), and barium (Ba 2+ ), resulting in immediate gelation, with the sol-gel transition temperature being below 0 °C. Therefore, using alginate as a standalone bioactive material for bioprinting is difficult. Typically, substances such as gelatin are added as physical crosslinking agents to enhance the stability of printed structures. Bioinks that form a fixed structural network through crosslinking to enhance stability and shape retention prior to printing are referred to as pre-crosslinked bioinks. Pre-crosslinked bioinks form a structurally stable scaffold during the printing process and provide a conducive environment for cell growth [ 49 ].

Owing to their excellent biocompatibility, rapid biodegradability, and chemical gelation properties, pre-crosslinked alginate-based bioinks can be bioprinted using extrusion-based methods to construct soft-tissue tumor organoid models, such as breast tumors, glioblastomas, and lung tumors. Alternatively, 3D vascularized tissue models with controllable vessel wall thicknesses can be printed using coaxial nozzle-assisted crosslinking [ 87 , 88 ].

2.1.2. Gelatin-based bioinks

Gelatin is a biologically sourced material obtained through the acidic or alkaline hydrolysis of collagen. It is a readily available water-soluble and highly biodegradable polypeptide that exhibits good biocompatibility [ 89 ]. At 28 °C, gelatin demonstrates unique thermally reversible gelation behavior, enabling the temperature or concentration of cell-loaded gelatin solutions to be conveniently adjusted to achieve the desired 3D printing structure [ 90 , 91 ], making it particularly attractive as a bioink. Therefore, gelatin-based hydrogels with specific thermo-responsive properties enable cells and bioactive substances to be extruded through the nozzle or needle of a 3D bioprinter. In this way, they can be stacked into layers in a relatively mild environment to form predefined 3D structures that support cell growth while maintaining extremely high cell viability. The versatility, biocompatibility, and high bioactivity of gelatin-based bioinks are widely utilized in high-throughput drug screening and the creation of organotypic tumor models with specific tissue structures.

However, the poor mechanical properties of gelatin limit its application as a bioink. The stability of printed structures can be improved by adding alginate and fibronectin and forming chemical crosslinks in gelatin-based bioinks [ 92 ]. By adding modifiers, gelatin-based bioinks can serve as both a support structure and a source of RGD peptides [ 93 , 94 ], providing the necessary biological signals for tumor cell migration. As a cell adhesion sequence, RGD peptide can bind with integrins on the cell surface, thereby enhancing cell adhesion and interactions within the biological scaffold. This simulated cell-matrix interaction contributes to better understanding and studying the mechanisms of tumor cell migration and invasion, providing crucial guidance and insights to unravel the process of tumor metastasis [ 95 , 96 ].

The amino groups in gelatin can be chemically modified with methacrylamide groups (such as chloro-methacrylate, glycerol methacrylate, and methacrylic anhydride) to form a hybrid gelatin-methacrylate hydrogel [ 97 ], which enhances the adhesion and printability of bioinks under physiological conditions [ 98 ]. Owing to the presence of an Arg-Gly-Asp (RGD) sequence and matrix metalloproteinase (MMP) degradable motifs in the polymer chain, GelMA demonstrates strong cell adhesion and migration capabilities. The cross-linking of functional groups added to the gelatin backbone by photocrosslinking or enzymatic cross-linking, along with temporal and spatial control of the cross-linking process, enables the manipulation of the GelMA-based bioink design and properties. This significantly improves the mechanical performance and shape fidelity of 3D-printed structures [ 99 , 100 ]. The GelMA bioink is often combined with photopolymerization-based bioprinting methods such that is rapidly solidify into finely structured microchannels with high shape fidelity in specific regions [ 101 ].

2.1.3. Collagen-based bioinks

Collagen is an abundant component in animals and a primary component of connective tissue with a triple helix structure. Various types of collagens, including Types I, II, III, IV, and V, are used in tissue engineering research. Type I collagen is widely used in bioprinting because of its ability to self-assemble. However, Type I collagen cross-links slowly at 37 °C, which may result in insufficient structural stability in the later stages of bioprinting and lead to uneven cell distribution. In addition, the low viscosity and rapid degradation of pure collagen bioinks severely limit their application as "bioinks" in bioprinting tumor organoid models. Other compounds, such as alginate and hyaluronic acid, have been incorporated into collagen hydrogels to enhance viscosity, reduce degradation rate, and improve the printability of natural collagen.

Natural collagen molecules contain the same RGD peptide domain as gelatin [ 102 ], contributing to cell adhesion, proliferation, and differentiation. In addition, tumor-related modifications and remodeling of collagen proteins are key factors that enhance tumor invasion and metastasis [ 103 , 104 ]. Therefore, Type I collagen is typically used as an internal cell carrier. In contrast, compounds, such as alginate, are used as external support structures or combined with different polymers through extrusion to create consistent, high-throughput tumor organoid models [ 105 ].

2.1.4. Hyaluronic acid-based bioinks (HA)

HA is a polysaccharide present in organisms. As an important ECM component, hyaluronic acid possesses excellent rheological properties, biocompatibility, and biodegradability [ 106 ]. Furthermore, HA can promote cell proliferation and angiogenesis, mediate receptor interactions [ 107 ], and modulate cell behavior and function through physical or chemical cross-linking. These unique properties make it an ideal polymer for creating a 3D microenvironment that supports tumor cell growth. In addition, it is a lubricious hydrophilic polymer that can form highly viscous gels at low concentrations. It is commonly used as an additive to enhance the viscosity of gelatin and collagen-based bioinks, to maintain the stability of the printing structure.

However, the drawback of HA is its low shape fidelity during the bioprinting process. This can be addressed by utilizing photo-crosslinking-based bioprinting methods with methacrylate to form methacrylated hyaluronic acid (HAMA) [ 108 , 109 ], or further incorporating GelMA to form HAMA-GelMA "bioink" for constructing highly authentic vascularized tissues and neural networks [ 110 ].

2.1.5. Polyethylene glycol-based bioinks (PEG)

PEG is a widely used biomaterial in the construction of biomimetic scaffolds in vitro [ 111 ]. PEG exhibits excellent mechanical properties, is non-cytotoxic within a specific molecular weight range, and is non-immunogenic as a biomaterial [ 112 ]. However, unlike natural polymers, PEG cannot form hydrogel structures with temperature variations or ionic cross-linking properties. In addition, PEG cannot promote cell adhesion and interaction. Therefore, PEG must be coupled with functional groups (such as methacrylates) or other functional polymer materials to achieve these cellular activities [ 113 ].

Polyethylene glycol diacrylate (PEGDA) is a polymer that undergoes copolymerization with acrylate. Compared with PEG, PEGDA possesses cross-linking properties, making it a biologically active material for preparing bioinks. Using photocrosslinking methods, PEGDA is commonly used to fabricate finely structured or controllable tumor models, such as lung tumors, glioblastomas, and multiple myelomas. Pluronic® F127 (PF127), composed of PEG and polypropylene glycol (PPG), is often used in extrusion-based bioprinting to serve as a sacrificial layer for observing the biological behavior of tumor cells by dissolving PF127 [ 114 ].

We have summarized and expanded upon the information regarding the advantages, disadvantages, and potential applications of the six bioinks in Table 2 for a more intuitive comparison.

Summary of the advantages, disadvantages, and applications of bioinks used in 3D bioprinting.

2.2. Bioprinting techniques

2.2.1. extrusion-based bioprinting.

Extrusion-based bioprinting is the most commonly used method whcih consists of three parts: a lifting platform, a nozzle, and an outlet structure [ 20 ]. With computer-assisted control, the mixed bioink is extruded from the nozzle under continuous squeezing pressure along the x-axis [ 115 ]. Simultaneously, the lifting platform moves along the y- and z-axes, depositing the material in a 2D pattern, sequentially stacking to form a 3D scaffold [ 116 ]. This method has a wide range of applications. It demonstrates good compatibility with biomaterials of different viscosities and cells of various concentrations and types [ 117 ], which enables the construction of tumor organoids with sufficient mechanical strength for building tumor microenvironments in hard tissues such as bone. Furthermore, it allows multiple nozzles to deposit different bioinks, which is suitable for constructing co-culture models to study the interactions between tumor cells and other cells. However, low resolution of hydrogel model is the key limitation of extrusion-based bioprinting [ 118 ]. The second limitation is the material nature of the bioink, which requires precise control of the nozzle temperature to prevent liquefaction and nozzle blockage, which can lead to material deformation and collapse [ 119 , 120 ] ( Fig. 2 A).

Schematic diagram illustrating the principle of tumor model formation based on extrusion-based and inkjet-based bioprinting methods with deposition of bioink. (A) Cellink 3D bioprinter. (B) Extrusion-based bioprinting method, and inkjet-based bioprinting method. Reproduced with permission [ 121 ]. Copyright 2021, Wiley-VCH. ( C ) By increasing the number of layers and decreasing the thickness, the resolution of the printed structure is improved, resulting in a tumor organoid model with a smooth surface appearance [ 122 ]. Reproduced with permission. Copyright 2018, Company Biologists.

Coaxial bioprinting is an extension of the extrusion-based printing method, where the coaxial circular structure of the nozzle has the advantage of simultaneously controlling the internal and external hierarchical structures, thereby enabling the printing of hollow tubular structures, particularly in the field of vascularization [ 123 , 124 ]. The core-shell structure allows the co-extrusion of two different bioinks, addressing the problem of insufficient mechanical strength of a single bioink and making the printing of tubular structures more convenient. The combination of extrusion-based bioprinting with sacrificial material can also be employed to construct functional vascular networks in tumor models, creating specifically shaped tumor models such as breast ductal carcinomas [ 50 ], vascularized tumor models, and microtumor microarrays [ 51 ]. This is crucial for revealing the close relationship between blood vessels and tumors including the interaction of circulating tumor cells with stromal and infiltrating immune cells, the exchange of secreted factors between different cells, the response to external stimuli, and the adaptive behavior of the tumor to the metastatic microenvironment [ 112 , 125 ].

2.2.2. Inkjet-based bioprinting

Inkjet bioprinting is the first bioprinting technology and is a non-contact droplet-based bioprinter [ 126 ]. The inkjet printer consists of a liquid binder cartridge, a nozzle that moves along the x- and y-axes, and a platform along the z-axis. By electrically heating the nozzle head or inducing acoustic waves using piezoelectric crystals inside the print head [ 127 ], liquid droplets were ejected onto the substrate, adhering adjacent hydrogel inks together, forming a single layer of 2D patterns, subsequently lowering the layer and printing 3D structures layer by layer ( Fig. 2 B). By controlling the droplet size, deposition speed, and nozzle orientation, the bioink consisting of cells, scaffold material, and growth factors can be precisely deposited at high resolution (approximately 50 μm) and high printing speed (up to 10,000 drops per second) [ 128 ] ( Fig. 2 C). However, inkjet-printed models typically require long drying periods at high temperatures, which can lead to decreased utilization of bioinks. Similar to extrusion-based bioprinting, higher cell densities can lead to a high-viscosity of the bioink and nozzle clogging [ 129 ]; thus, only allowing the printing of low-viscosity bioinks. Additional crosslinking is required to ensure the stability of the printed structure [ 130 ].

Through inkjet printing, different types of cells and biomaterials can be printed at predetermined locations [ 18 , 122 ], simulating the complex structures and microenvironments of real tumor tissues such as osteosarcoma and ovarian tumors. This technology can also be customized according to the specific conditions of the patient, helping to simulate different types of tumor tissues better and providing more realistic and reliable in vitro models for drug development and treatment research.

2.2.3. Stereolithography-based bioprinting

Stereolithography can also be used to manufacture 3D printed models. It is based on the photopolymerization of photosensitive polymers [ 122 ]. Furthermore, the method uses light of specific wavelengths and intensities to scan from a point to a line focused on the surface of a liquid hydrogel in a container, resulting in a single layer of 2D cured patterns in the container. Subsequently, as the platform descends or rises to the designed single-layer thickness, the next layer continues to solidify using the abovementioned process to completely recover the previously generated 2D pattern with fresh bioink on the prefabricated structure until the 3D structure is completed [ 131 ]. Photolithography is not constrained by the viscosity of the bioink, which means that multiple bioinks with different viscosities can be used for printing, thereby enabling a more diverse range of applications for various biomaterials. Laser-assisted printing precents direct contact between dispensers and bioinks, enabling non-contact printing [ 132 ]. This method does not subject cells to mechanical stress, which is beneficial for maintaining cell viability, and provides the highest resolution among the three printing methods [ 133 ] ( Fig. 3 ).

Schematic diagram illustrating the principle of constructing organoid models using light-based bioprinting method. (A) Schematic representation of a liver organoid model constructed by light-based bioprinting. (B) Grayscale digital mask corresponding to the vascular structures of the liver lobules. (C, D) Images taken under fluorescence and bright field channels (5 × ) of fluorescently labeled hiPSC-HPC in GelMA [ 134 ]. Reproduced with permission. Copyright 2016, National Academy of Sciences.

Bioinks based on GelMA [ 135 ] and PEGDA [ 136 ] are commonly used in photolithography technology to create scaffolds with precise structures and controllable mechanical strength [ 137 ]. These scaffolds are subsequently used to simulate tumor tissues and provide valuable tools for oncology research. Scaffolds with precise structures and controllable mechanical strengths have been used in oncology research.

2.3. Emerging 3D bioprinting technology

Laser-Induced Forward Transfer (LIFT) utilizes high-energy laser pulses to irradiate biological precursor materials, inducing instantaneous vaporization and gas formation, thereby transferring the biological precursor material from one substrate to another with rapid and precise micrometer or nanometer-level deposition. This technique is suitable for constructing intricate biological tissue structures and microscale biological chips. In the field of biomedicine, Laser-Induced Forward Transfer technology can be employed to build biomimetic tissue structures, biosensors, artificial bones, and provide essential tools for tissue engineering, drug development, disease diagnosis, and more [ 138 , 139 ].

The technology of volumetric bioprinting through tomographic scanning is an innovative approach that combines medical imaging techniques with 3D bioprinting. It leverages tomographic scanning (CT) or magnetic resonance imaging (MRI) to acquire three-dimensional biological structural information of specific parts of a patient's body, and then uses bioprinting to deposit or stack biological materials according to this model, enabling precise replication and reproduction of complex biological tissues. This technology can provide more personalized and customized solutions for tissue regeneration, transplantation, and disease treatment in the medical field. It holds potential for significant breakthroughs and innovations in medical research and treatment [ [140] , [141] , [142] ].

Electrospray bioprinting technology is an innovative method that utilizes the principle of electrospray to perform biological printing. The nozzle, loaded with bio-ink or cell suspension, is activated by a high electric field voltage, creating tiny sprayed droplets. These droplets then deposit onto a substrate in a controlled manner, forming the desired biological tissue structure. Electrospray bioprinting can be used to construct complex biological tissue engineering structures, such as neural tissues, vascular networks, and more. Additionally, it can be applied in areas like cell microarray preparation, biosensor manufacturing, and beyond. However, constraints such as the viscosity of biological materials and surface tension need further research and optimization before clinical application [ 143 , 144 ].

Plasma-enhanced bioprinting is a method that combines plasma technology with bioprinting technology. In this technique, plasma is used to modify the chemical properties of biological materials such as extracellular matrix and hyaluronic acid, altering factors like crosslinking degree and surface charge to enhance adhesion, biocompatibility, and mechanical performance. By activating the surface of printing substrates or support materials, enhancing their wettability and affinity, it promotes cell and biomaterial adhesion, thereby improving the success rate and forming quality of bioprinting. This method strengthens and optimizes the bioprinting process, showcasing innovation and cutting-edge advancements [ 145 ].

Magnetic-assisted printing technology is an advanced printing method that utilizes magnetic materials and an external magnetic field to assist in positioning and printing. It typically involves introducing magnetic particles or magnetic liquid into the printing material or support structures, and controlling the positioning and shape of these magnetic components by applying an external magnetic field. The magnetic stimulation can help overcome factors such as gravity and surface tension, induce the oriented alignment of cells, activate cell signaling pathways, enhance cell proliferation and differentiation, and achieve a flexible, efficient, and controllable printing process. Magnetic-assisted 3D bioprinting technology has brought new breakthroughs to tissue regeneration and organ repair in the biomedical field, offering new possibilities and development opportunities [ 146 , 147 ].

Acoustic bioprinting technology utilizes sound waves as a driving force to achieve precise positioning and organization of bio-materials. By using special piezoelectric elements or acoustic lenses to convert electrical signals into high-frequency sound waves and focusing them on specific areas, a high-energy density sound wave beam is formed. This beam can generate thrust and tension on bio-materials, avoiding mechanical damage to cells, while achieving precise positioning and arrangement of cells, bio-inks, or other biological components with a resolution down to the micrometer or even submicron level. The accuracy, efficiency, and controllability of acoustic bioprinting technology contribute significantly to innovation in the fields of tissue engineering and regenerative medicine in the biomedical field [ [148] , [149] , [150] ].

3. Tumor organoids model for bioprinting

3.1. breast tumor organoid bioprinting.

Breast tumors are among the most common tumors in women and a leading cause of female mortality [ 151 ]. Breast tumors are characterized by the uncontrolled proliferation of breast epithelial cells in response to various carcinogenic factors. Symptoms such as breast lumps, nipple discharge, and axillary lymphadenopathy occur in the early stages of the disease. In the late stages, distant metastasis of tumor cells may lead to multi-organ damage, directly threatening the patient's life. Breast tissue has a unique structure comprising mammary glands (lobules and ducts) and adipose tissue [ 152 ]. The tumor microenvironment plays a crucial role in tumor progression. Traditional 2D in vitro cell cultures lack spatial heterogeneity and exhibit overly simple structures [ 153 ]. Establishing physiologically relevant in vitro tumor models through bioprinting, including interactions between tumor cells and the extracellular matrix of the breast microenvironment, as well as simulating hollow ductal channels of the mammary gland, is essential for a better understanding of the biological behavior of tumor cells in a natural breast tumor microenvironment [ 154 ].

Invasive proliferation and migration are key features of tumors in vivo . Tumor cell metastasis is significantly influenced by the biophysical properties of the tumor microenvironment. The stiffness of in vitro biomimetic organoid models is one of key issues that influences the behavior of tumor cells. GELMA hydrogel is a photosensitive hydrogel that has proven to be a candidate material for basic biological research [ 155 ] and is used to construct biologically relevant tissue structures. In an organoid tumor model constructed using photolithography Nitish et al. [ 45 ] observed that MDAMB231 cells moved slowly and maintained stable migration in the central region with high hardness (748 ± 90 Pa) based on the photosensitive properties of GELMA. In contrast, the opposite behavior is observed in the surrounding region with low hardness (313 ± 89 Pa). Similarly, the alginate-gelatin composite bioink with low hardness (A1G5 and A1G7) facilitated the formation of tumor spheroids and the migration of tumor cells in a 3D environment. Conversely, materials with high hardness (A3Gy and A5Gy) inhibited the formation of tumor spheroids [ 49 ]. Therefore, the stiffness of ECM can significantly influence biological behaviors such as tumor cell migration, invasion, and metastatic potential, providing new strategies and targets for tumor treatment and prevention, which warrants further in-depth research and exploration. By adding Matrigel, iterative culturing of MDAMB231 tumor spheroids was achieved in the AxGyMz composite bioink [ 53 ]. Matrigel is the basement membrane (BM) extract most commonly used for 3D organoid cultures. Matrigel is the most commonly used basement membrane (BM) extract for three-dimensional organoid culture, but it is extracted from mouse tumors and cannot fully replicate the specific microenvironment of human tumors. Additionally, the composition and mechanical properties of Matrigel vary between batches, affecting the reproducibility of experiments. In a composite bioink of 5 % GelMA +0.5 % collagen, MDAMB231 exhibited similar invasive behavior to that of Matrigel, suggesting that this stiffness-adjustable, cost-effective, and novel hydrogel has potential as an alternative to Matrigel [ 51 ]. Researching new hydrogel materials as alternatives to Matrigel is of great significance. It helps improve and optimize the performance of materials, broaden the functional range of materials, achieve consistency between batches, and further drive innovation and advancement in tissue engineering and disease modeling technologies. However, MDAMB231 cells exhibited the opposite biological behavior in the novel PEG-4MAL bioink, which has highly tunable mechanical and biological functional properties. Compared to softer hydrogel systems (0.7 kPa + RGD), significant migratory behavior was observed in MDAMB231 cells within the harder hydrogel (1.1 kPa + RGD), likely because of the modification with cell adhesion peptide (RGD) [ 55 ]. Similarly, interaction with RGD promotes the migration of MCF-7 cells [ 50 ]. Therefore, future research should focus on precisely controlling the stiffness factor of the tumor microenvironment by adding different extracellular matrix components in hydrogel systems, including temporal and spatial control. Multi-level, multi-component hydrogel structures can help researchers better understand how tumor cells respond to different mechanical microenvironments, potentially providing new perspectives and methods for revealing the mechanisms of tumor initiation and development. This could contribute to the development of more effective strategies for tumor treatment and prevention. In recent years, bioprinting using dynamically cross-linked hydrogel networks has attracted significant attention because they can better mimic the mechanical properties of the ECM and respond to biological stimuli. A dual cross-linked dynamic hydrogel network based on the boronic acid motifs of laminarin (LAM-PBA) and alginate exhibited excellent cell compatibility (cell viability exceeded 90 %). By controlling the cross-linking process of both types, the processability, mechanical behavior, and stability of the bioink can be further improved [ 52 ]. This study ingeniously combines dynamic covalent crosslinking and ion crosslinking to form a double network structure, providing a new tool and perspective for the field of tissue engineering. However, detailed mechanical property data of this double network structure bioink, such as tensile strength and modulus, were not provided in the study. These data are crucial for evaluating the performance of the bioink in the 3D printing process and for further optimizing the material formulation.

Interactions among tumor, immune, and mesenchymal cells within the tumor microenvironment significantly affect tumor growth and behavior. Understanding these interactions is crucial for a deeper comprehension of breast tumor cellular characteristics and behavioral changes. In breast tumors, adipocytes are important agents that play roles in promoting tumor progression within the tumor microenvironment. They can induce inflammatory responses, influence the metabolic reprogramming of tumor cells, and provide the necessary nutrients and growth signals for tumor growth and dissemination. Hence, understanding the regulatory role of adipocytes in the breast tumor microenvironment helps deepen our understanding of the pathogenic mechanisms of breast tumors [ 156 ]. Hannes et al. [ 54 ] constructed a co-culture model of adipose tissue with breast tumor cells. After nine days of co-culturing, they observed that tumor cells induced a decrease in the lipid content of the adipose tissue and remodeling of the extracellular matrix (with a significant increase in the expression of collagens I, VI and fibronectin). This integrated 3D breast cancer-adipose tissue model illustrated the pro-tumorigenic effects of the adipose in breast cancer. In the future, the introduction of other key cell types such as immune cells, vascular endothelial cells, etc., can further elucidate the specific regulatory roles of factors secreted by adipose cells in tumor development. The bone is one of the most common sites of metastasis in advanced breast tumors [ 157 ]. To better understand bone metastasis in breast tumors, a bionic bone-specific microenvironment was created by incorporating hydroxyapatite-containing nanoparticles into a PEG/PEGDA hydrogel, the presence of MSC increased the number of MDAMB231 cell spheroids compared to culturing tumor cells alone [ 47 ]. Similarly, in a co-culture of osteoblasts with MDAMB231 cells, osteoblasts promoted tumor cell proliferation and tumor sphere formation, whereas MDAMB231 cells inhibited osteoblast proliferation [ 46 ]. Overall, the addition of helper cells enhanced the bionic nature of the tumor model, which is more valuable in studying the interactions of the tumor microenvironment. In the future, it may be considered to incorporate other cell types, such as inflammatory cells, to comprehensively simulate the impact of multiple factors on bone metastasis. Alternatively, integrating the 3D printing model with clinical case data for comparison of research outcomes with actual patient conditions can provide better validation of experimental results.

Resistance to anti-tumor drugs is another important characteristic of malignant tumors [ 64 ]. Using bioprinting in vitro organoid tumor models can prevent the false positive behavior of tumor cells exposed to anti-tumor drugs in 2D cultures. Therefore, bioprinting in vitro organoid tumor models can serve as a better preclinical platform for drug screening and personalized drug development. Song et al. [ 57 ] successfully maintained the growth of drug-resistant MCF-7 breast tumor spheroids in a gelatin-sodium alginate hydrogel, preserving the CD44 high/CD24 low/ALDH1 high phenotype ( Fig. 4 ). At the same time, the EC50 values for apoptosis and necrosis concurrently induced by PTX in the resistant spheroids were 124 nM and 131 nM, respectively. In contrast, the EC50 values for PTX-induced apoptotic and necrotic cell death in larger spheroids were 59 nM and 54 nM, respectively. In the same year, the team further achieved a novel in situ assessment of the efficacy of PDT on tumor spheroids, with significantly higher IC50 values for the photosensitizers sTPP and Ce6 in 3D spheroids than in 2D cultures (7-fold difference) at the same radiant power. Interestingly, heterogeneous responses of individual tumor cells within a single tumor sphere to photodynamic therapy were observed suing 3D imaging. Furthermore, individual drug-resistant cells within the spheres were suggested to be responsible for the emergence of drug resistance in the tumor spheres. However, the main shortcoming of these experiments is the lack of in-depth research on the mechanisms of PDT. The analysis was conducted merely from the perspective of overall cell death. In the next step, other cell death detection methods (such as apoptosis markers and cell cycle analysis) could be combined to further explore the mechanisms of cell death induced by PDT. Similarly, in other experiments inducing tumor apoptosis with PDT, 3D tumor spheroids overexpressed the ABCG2 transporter protein, which expelled an excess of the photosensitizer PPIX from the tumor cells, thereby reducing the therapeutic effect of PDT compared to 2D models [ 158 ]. Unfortunately, this experiment did not compare the genetic changes in in vivo tumor models with 3D spheroids, making it unable to further demonstrate the persuasiveness of the 3D tumor model. Similarly, upon establishing a 3D culture model with a spontaneously generated hypoxia and drug resistance central core, the generation of the central core in tumors may be one of the most critical factors limiting the effectiveness of PDT in clinical practice [ 159 ]. 3D models provide a visual representation of the interaction between tumor spheroids and drugs in vitro . This demonstrates the ability of 3D models to effectively mimic the tumor microenvironment in vivo , which is crucial for understanding the response to PDT treatment and the process of hypoxic core formation [ 56 ]. In the future, patient-derived tumor cells or organ samples can be extracted for model construction with high clinical relevance to reflect the response of tumors in patients to PDT. Additionally, by combining higher resolution imaging techniques (such as multiphoton microscopy) to monitor microenvironmental oxygen levels and metabolic changes, in-depth research on the mechanism of action of PDT can be conducted.

Schematic representation of the construction of breast cancer organoid models for drug screening using bioprinting. (A) Confocal microscopy images showing the expression of CD44, ALDH1, and CD24 in drug-resistant spheroids. (B) Methodology for quantitative cell viability measurement in embedded MCF-7 spheroids. 3D spheroid images based on their respective intensity profiles under white light and fluorescent modes for 7-AAD [ 57 ]. Reproduced with permission. Copyright 2022, Elsevier.

3.2. Glioblastoma organoid bioprinting

Glioblastomas, or gliomas, are the most common primary malignant tumors of the central nervous system in adults [ 160 ]. Owing to its high malignancy rate, rapid progression, and diffuse infiltration, glioblastomas typically cannot be completely removed through surgery and tend to recur after surgery [ 161 ]. In addition, glioblastomas exist within a complex tumor microenvironment (TME) containing various cell types, including glioblastoma cells, glioblastoma stem cells (GSCs), mesenchymal stem cells (MSCs), and immune cells [ 162 ]. In addition, there are differences in cell characteristics and gene expression patterns in different regions. Traditional 2D cultures cannot simulate cell interactions and tumor microenvironment interactions. Therefore, studies of glioblastomas are limited. Bioprinting has facilitated the construction of clinically relevant brain tissue organoid models, by accurately placing tumor cells to replicate the natural tumor microenvironment. It is becoming a promising tool for creating simulated GBM structures and cell compositions and studying tumor biology.

In recent years, various laboratories have advanced the bioprinting of glioblastoma models by developing new bioprinting methods, leading to in-depth research on in vitro glioblastoma models. Shu et al. [ 58 ] first constructed a U87MG glioblastoma model on a sodium alginate hydrogel using extrusion bioprinting. After 11 days of printing, the cell viability remained at 88 ± 4.3 %. By utilizing photolithography and adjusting the shape of the PEGDA microcavities, they further controlled the shape, size, and thickness of U87MG glioma spheroids [ 59 ]. Similarly, in a bioink composed of fibronectin, alginate, and laminin, U87MG formed tumor spheroids, with high CD133 and DCX expression, indicating the maintenance of glioblastoma stem cell-like characteristics [ 62 ]. Erin et al. [ 66 ] utilized an immersion bioprinting method to construct a high-throughput in vitro glioblastoma organoid model using patient-derived tumor cells in a 96-well plate. This technology retains the heterogeneity of patient-derived tumors but requires further research to improve and expand the consistency of patient tumor organoids (PTOs). Further optimize 3D printing techniques to enhance the stability and complexity of tumor organoids, better simulating the biological characteristics of primary tumors. This high-throughput modeling method can be combined with artificial intelligence technology to achieve a more intelligent process for tumor organoid construction and drug screening. Interestingly, Matteo et al. [ 67 ] utilized FRESH 3D bioprinting to construct a glioblastoma organoid model of human neuroblastoma (SH-SY5Y cell line) using conductive bioink based on cellulose nanofibers (CNF), alginate, and single-walled carbon nanotubes (SWCNTs). This model promotes the mature differentiation of SH-SY5Y cells into mature neurons and facilitates the formation of neural networks ( Fig. 5 A). Furthermore, this innovative experiment provides a tool for better understanding the pathophysiological mechanisms of brain tumor-related neurological disorders. However, this study did not delve into the mechanisms by which the conductive nanocellulose scaffold induces neuroblastoma cell differentiation. Future works are suggested to focus on attempting to implant the scaffold into animal models for in vivo validation to observe its impact on neural function restoration, thereby assessing its feasibility for clinical applications. Combined with high-throughput technologies such as transcriptomics and proteomics, a systematic analysis of the molecular mechanisms by which the scaffold promotes neuroblastoma cell differentiation can provide a deeper theoretical foundation for related drug development.

Schematic representation of the construction of a glioma organoid model using bioprinting. (A) Workflow of FRESH bioprinting technology and brain-like scaffolds obtained by bioprinting with cellulose-based bioinks [ 67 ]. Reproduced with permission. Copyright 2020, MDPI. (B) Schematic representation of the bioprinting process and bioprinting mini-brains, as well as a schematic representation of the crosstalk between glioblastoma cells and macrophages [ 64 ]. Reproduced with permission. Copyright 2019, Wiley-VCH.

Glioblastoma is an intracranial tumor with a poor prognosis, characterized by an extensive abnormal vascular network. Furthermore, glioblastomas often use the microvasculature to guide migration. Understanding the cellular interactions between vascular and GBM cells may lead to new therapeutic strategies. By co-culturing primary adipose-derived stem cells with human umbilical vein endothelial cells (HUVECs) in a composite hydrogel of 5 % GelMA and 2.5 mg ml−1 fibronectin at a ratio of 1:0.75, a successful simulation of a vascularized tumor microenvironment was achieved. Patient-derived STA-NB1 neuroblastomas attract microvessels to approach and migrate within them [ 69 ]. To further explore the angiogenic potential of glioma cells, an in vitro model using neuroglioma U118 cells and glioma stem cells GSC23 cells was established. Both 3D-U118 and 3D-GSC23 cells demonstrated the ability to form blood vessels. 3D-GSC23 cells exhibit strong capabilities to form cell spheroids, secret VEGFA, and form tubular structures in vitro [ 65 ]. In summary, the experimental results above suggest that the glioma microenvironment model exerts a promoting effect on the vascularization process of glioma cells. Glioma cells can stimulate the proliferation of endothelial cells and the formation of luminal structures, thereby promoting the growth and dissemination of gliomas. Xu et al. conducted a series of studies on glioma stem cells. Firstly, they successfully enriched glioma stem cells using a gelatin-alginate-fibrinogen (GAF) hydrogel scaffold, and the enriched glioma stem cells retained the inherent characteristics of tumor stem cells [ 63 ]. They also showed the potential to differentiate into glial, neuronal, and vascular endothelial cells [ 60 ]. To observe the crosstalk between tumor microenvironment cells, glioma stem cells (GSC) and mesenchymal stem cells (MSC) were co-cultured using coaxial bioprinting. The interactions between GSC and MSC and their roles in tumor progression were observed. The results showed that GSC and MSC fused, and the fused cells co-expressed biological markers of both GSCs and MSCs, and exhibited stronger proliferation, clonogenicity, and invasive capabilities than GSCs and MSCs. Furthermore, the fused cells showed stronger tumorigenicity in nude mice, exhibiting pathological features of malignant tumors [ 61 , 68 ]. This cell fusion may be an important mechanism leading to the poor prognosis of gliomas. The newly formed hybrid cell lines resulting from cell fusion exhibit more aggressive and hypoxia-tolerant malignant phenotypes, providing insights into further understanding tumor heterogeneity and treatment resistance. Blocking cell fusion or disrupting the key signaling pathways of fused cells could potentially become a new therapeutic strategy. Similarly, during co-culture with glioblastoma cells, glioma-associated macrophages (GAM) are recruited by glioma cells and polarized into a GAM-specific phenotype. They actively secreted growth factors to promote tumor cell proliferation [ 64 ] ( Fig. 5 B). Therefore, by disrupting the interactions between glioma cells and GAMs, or altering the polarization state of GAMs, an important strategy for future glioblastoma treatment may be developed.

3.3. Lung tumor organoid bioprinting

Lung tumors originate from the bronchial mucosa or glands in the lungs. Lung tumors are one of the most common and deadliest malignant tumors worldwide, posing a significant problem health issue and a substantial burden [ 163 ]. The treatment options for lung tumors include surgery, radiation therapy, chemotherapy, and targeted drug therapy. The development of drug resistance often contributes to the recurrence of lung tumors, as lung tumor cells have demonstrated the ability to develop resistance to chemotherapy [ 163 , 164 ]. In addition, the tumor microenvironment plays a crucial role in drug resistance. Inflammatory cells and factors within the tumor inflammatory microenvironment promote tumor angiogenesis, epithelial-mesenchymal transition, cell apoptosis, and the activation of inflammatory pathways, leading to the occurrence, development, metastasis, and drug resistance of lung tumors [ 165 , 166 ]. Moreover, most studies on tumor occurrence, progression, and the assessment of anti-tumor drugs are based on 2D tumor models, which may lead to the loss or alteration of some original features and functions. Bioprinting offers a reliable, biomimetic 3D tumor model replicating the actual in vivo environment, aiding in the study of tumor development and drug screening.

Gelatin-alginate hydrogels are commonly used to construct in vitro models of lung tumors by simulating the in vivo tumor microenvironment (TME) to aid in the study of tumor growth, invasion, and drug screening. Xu et al. [ 70 ] maintained continuous proliferation of A549 cells in gelatin-alginate hydrogels for up to 28 days. Arindam et al. [ 71 ] observed upregulation of vimentin, α-SMA, and loss of E-cadherin during co-culturing of non-small cell lung tumor (NSCLC) patient-derived xenograft (PDX) cells and lung CAFs, confirming the feasibility of using gelatin-alginate hydrogel for studying cell-cell crosstalk. Furthermore, drug sensitivity testing of eight traditional anti-tumor Chinese medicines showed that, compared to 2D models, 3D models exhibited higher drug resistance [ 73 ]. This validates the practicality of using a gelatin-sodium alginate hydrogel as a 3D bioprinting lung organoid tumor model for drug screening.

In recent years, the emergence of many novel composite bio-inks has deepened our understanding of lung tumor organoid models. By printing the Hphil-CNF hydrogel on the surface of the Hphob-CNF hydrogel, hollow 3D channels were formed, allowing the real-time observation of cell morphology, cellular responses to drug stimuli, and chemical flow within the channels [ 72 ]. It provides an innovative and promising experimental platform for cell culture and biomedical research. Similarly, in GelMA-PEGDA hydrogels, the high upregulation of lung CSC-specific marker genes indicates that this model promotes the expression of lung CSC-specific markers in non-small cell lung tumor (NSCLC) cells [ 75 ]. It revealed the biological behavior of lung cancer stem cells under different conditions. Likewise, patient-derived NSCLC cells form 3D spheroids in the polysaccharide-based ink H4-RGD, showing stronger resistance than 2D monolayer cells to NSCLCPDX cells [ 74 ]. This suggests a role for the tumor microenvironment created by Ink H4-RGD in determining the variability of chemotherapeutic responses in three-dimensional spheroids.

3.4. Cervical tumor organoid bioprinting

Cervical tumors are the most common malignant tumors of the female reproductive tract, and human papillomavirus (HPV) is a primary risk factor for the development of this disease [ 167 ]. Early cervical tumors often have no obvious symptoms or signs, and in advanced stages, they can present with systemic symptoms such as anemia and cachexia. Furthermore, early-stage cervical tumors are prone to lymphatic metastasis, leading to a relatively poor prognosis, while late-stage metastasis results in poor prognosis and a high mortality rate [ 168 ]. However, the potential mechanisms underlying metastasis remain unclear [ 169 ]. Therefore, establishing biologically relevant organoid models to elucidate the mechanisms of cervical tumor cell migration and invasion is crucial for providing a platform for in vitro mechanistic studies and personalized treatment of HPV-related cervical diseases [ 170 ].

Chen et al. [ 76 ] used photolithography-based bioprinting technology to construct a 3D in vitro microchip with a honeycomb-like branched blood vessel structure in a PEGDA hydrogel. The migration speed of HeLa cells increased as the width of the microvascular channels decreased, revealing a close correlation between tumor cell migration and blood vessel diameter. Although this 3D model provides a rapid and cost-effective tool for studying tumor migration, it does not elucidate the mechanisms of tumor cell migration. To further investigate the crucial stage of epithelial-mesenchymal transition (EMT) in cervical tumor cell metastasis, Sun et al. added the main inducer of EMT, TGF-β [ 171 , 172 ]. They observed the disintegration of 3D HeLa cell spheroids formed in collagen-sodium alginate-Matrigel, with immunohistochemistry showing activation of the Smad2/3 pathway, promotion of the transcription factor Snail, and suppression of E-cadherin, indicating achievement of the EMT process [ 77 ]. By further combining gene editing technologies (such as CRISPR-Cas9) and single-cell RNA sequencing, it is possible to study in greater detail the changes in gene expression and signaling pathways during the TGF-β-induced EMT process. Therefore, establishing an effective environmental stimulus as a regulatory sign in a 3D tumor model can help us better understand the occurrence and development of cervical tumors and subsequently regulate tumor metastasis by modulating the tumor microenvironment. Overall, this study provides new methodological tools for EMT research in cervical cancer, which is of significant importance. Further optimization and application of this model are expected to bring more discoveries regarding EMT and tumor progression mechanisms.

3.5. Ovarian tumor organoid bioprinting

Among all gynecological malignancies, ovarian tumors have the highest mortality rate [ 173 ]. Early ovarian tumors lack symptoms, and when symptoms appear, they are nonspecific, leading to a poor overall prognosis and propensity for metastasis and recurrence [ 174 ]. Traditional 2D cell culture systems have led to significant medical advancements in oncology research; however, the progression of ovarian tumors remains unclear. Organoid models constructed based on bioprinting can recreate the unique glandular structure of ovarian tissue in vitro and, through temporal and spatial control of the tumor microenvironment, simulate the interactions between different cell types in a high-throughput and reproducible manner. This allows for a systematic study of the various unknown regulatory feedback mechanisms between tumor and stromal cells and provides a tool for researching tumor biology [ 175 , 176 ].

Fibroblasts play a crucial role in the malignant progression of ovarian tumors [ 175 ]. An in vitro ovarian tumor model was constructed by co-culturing OVCAR-5 cells and MRC-5 cells on Matrigel™. Through the use of a 150 μm micro-nozzle, precise cell positioning and assembly were achieved, and it was observed that tumor cells spontaneously formed glandular structures resembling ovarian tumor micro-nodules in vivo . Where tumor cells spontaneously formed glandular structures resembling micro-nodules of ovarian tumor in vivo [ 79 ]. In future studies, various stromal cells could be introduced into this 3D ovarian tumor in vitro model to systematically study many unknown regulatory feedback mechanisms between cells, facilitating high-throughput drug screening and therapeutic interventions.

3.6. Liver tumor organoid bioprinting

Intrahepatic cholangiocarcinoma (ICC) cells are the second most common primary liver tumor cells within the liver [ 177 ]. The incidence and prevalence of ICC have been increasing every decade, and most patients with ICC present with advanced or refractory metastatic disease [ 178 ]. Moreover, treatment options for ICC are limited, with only approximately 20–30 % of patients qualifying for surgical resection, which is considered the only potentially curative treatment [ 179 ]. However, drug therapy has shown limited effectiveness. Mouse models are a crucial tool for drug screening in ICC, but they are associated with ethical controversies, time-consuming processes, high costs, and complex operations [ 180 , 181 ]. The bioprinted ICC tumor model exhibits higher resistance to anti-tumor drugs than 2D cultures, highlighting the potential role of patient-derived tumor models created through bioprinting in oncology research and the development of personalized treatments.

Patient-derived ICC cells are likely to have more clinical significance compared to ICC cell lines, as the cell lines have already lost their heterogeneity. Mao et al. [ 78 ] employed patient-derived primary ICC cells in a gelatin-alginate-MatrigelTM composite hydrogel system to construct an in vitro tumor model. Compared to 2D culture, the tumor markers CA19-9 and CEA, cancer stem cell markers CD133 and EpCAM, and liver damage-related liver function markers ALT, AST, and ALB were upregulated by 1.9, 5.7, 3.7, 9.7, 3.7, 1.9, and 2.0 times, respectively, in 3D bioprinting models. Therefore, the 3D in vitro culture model can more accurately mimic in vivo tumor phenotypes and thus can more precisely simulate treatment responses.

3.7. Osteosarcoma organoid bioprinting

Osteosarcoma is the most common primary malignant bone tumor in adolescents [ 182 ], characterized by the direct production of bone-like tissue from tumor cells. The development of drug resistance and metastasis is closely associated with poor prognosis. In addition, the osteosarcoma microenvironment is now recognized as essential for its growth and spread [ 183 ]. To identify new therapeutic targets, a better understanding of the mechanisms underlying tumor drug resistance and metastasis is urgently needed. Therefore, it is crucial to elucidate the interactions between osteosarcoma cells and the complex bone and bone marrow microenvironments [ 184 ]. Bioprinting technology can manufacture novel bone tissue engineering scaffolds with customized shapes [ 185 ], mechanical strength, and cellular composition, providing accurate in vitro migration and drug screening experiments [ 186 ].

Pellegrini et al. [ 82 ] constructed a 3D in vitro osteosarcoma model by embedding U2-OS cells and their drug-resistant strain U–2OS/CDDP 1 μg in collagen hydrogel. The cells grew uniformly within the scaffold, and the tumor cell clusters degraded the collagen matrix, creating lacunae through which they migrated, similar to acellular scaffolds. This invasion behavior is akin to that of tumors in vivo . This osteosarcoma model successfully maintained the biological characteristics of OS cells in their natural microenvironment, making it a promising tool for drug screening and OS cell biology research.

3.8. Melanoma organoid bioprinting

Melanoma is one of the most aggressive and progressive forms of skin tumor [ 187 ]. It primarily occurs in the skin but can also develop in various locations and tissues such as the mucous membranes and meninges. As the disease progresses, melanoma exhibits regional and distant metastases, leading to a poor prognosis, with a 5-year survival rate of less than 5 %. Although several new drugs have been developed in recent years, most patients do not show a lasting response to these treatments [ 188 ]. Therefore, new biomarkers and drug targets are required to improve the accuracy of melanoma diagnosis and treatment [ 189 ]. 3D bioprinting based on in vitro cell culture is a novel and creative method for creating a simulated microenvironment for the growth of malignant melanoma cells that mimics the human body environment.

A 3D scaffold composed of GelMA-PEGDA composite hydrogel was fabricated to construct an in vitro tumor model simulating the growth microenvironment of human malignant melanoma cells (A375) [ 80 ]. The melanoma cells on the 3D scaffold exhibited higher proliferation rates, elevated MMP-9 secretion levels, and increased invasiveness compared to those in a 2D environment. Conducting longer-term cultivation experiments to observe the long-term behavior of tumor cells within the scaffold could further advance this technology's application in cancer research and drug development.

3.9. Multiple myeloma organoid bioprinting

Multiple myeloma (MM) is a malignant proliferative disease of plasma cells that accounts for approximately 12 % of malignant tumors of the hematopoietic system. A characteristic feature of this disease is the uncontrolled proliferation of plasma cells in the bone marrow, leading to organ or tissue damage [ 190 ]. MM is characterized by the following four main features: bone destruction, renal dysfunction, hypercalcemia, and anemia. MM occurs almost exclusively within the bone marrow microenvironment, which provides the necessary signals and stimuli to induce cell proliferation and/or prevent apoptosis, promoting the development of drug resistance [ 191 ]. Therefore, reproducing the specific bone marrow microenvironment of MM cells is crucial for understanding the molecular mechanisms driving MM progression and treatment

resistance ( Fig. 6 ).

Schematic representation of an osteosarcoma organoid model constructed using coaxial bioprinting. (A) Schematic of the coaxial nozzle used for bioprinting. MM cells filled with a low concentration of GelMA represent the inner core of the cartilage marrow, the sheath used to mimic the surrounding cortical bone. (B) The inner/outer diameter of bioprinting nucleus-sheath structures positively correlates to the supply rate of core bioinks [ 81 ]. Reproduced with permission. Copyright 2022, Wiley.

Using coaxial bioprinting, a composite bioink simulating the outer cortical bone composed of GelMA, alginate, PEGDA, and nHA, as well as the inner bone marrow-like microenvironment composed of GelMA and MM cells, was printed simultaneously to mimic the human bone marrow niche [ 81 ]. With this method, a 3D core-sheath model was employed for the first time to achieve co-culturing of MM and HS-5 stromal cells. Moreover, it was observed that IL-6 secreted by HS-5 promotes the proliferation and aggregation of MM cells, demonstrating this co-culture model held significant implications for guiding combination drug therapy in tumors.

3.10. Chronic lymphocytic leukemia organoid bioprinting

Chronic lymphocytic leukemia (CLL) is a complex and heterogeneous hematologic malignancy of the blood system [ 192 ], and it is the most common adult leukemia in Western countries [ 193 ]. Studies have shown that CLL pathogenesis is closely related to the tumor-supportive microenvironment and immune system dysfunction. The disease exhibits significant clinical variability and remains incurable [ 194 , 195 ]. In addition, culturing primary CLL cells in vitro is challenging, and traditional two-dimensional in vitro models lack the cellular and spatial complexity present in vivo , leading to an incomplete understanding of the actual events occurring at these sites. Francesca et al. [ 83 ] established the first long-term 3D culture model for CLL by embedding CLL cells in a fibronectin 411 hydrogel matrix, maintaining the growth of primary CLL cells for up to 28 days while preserving the expression of the characteristic surface markers CD19 and CD5. This represents a groundbreaking advancement and provides a reference for simulating the physiological settings of other non-solid tumors ( Fig. 7 ).

Schematic representation of the construction of a chronic leukemia organoid model using bioprinting. (A) Schematic representation of the bioprinting strategy using the CLL cell line MEC1 or CLL primary B cells. (B, a) Representative H&E staining of 5 μm cryosections of 3D bioprinting CLL progenitor cells showing their distribution in the scaffold. (B, b) Representative images of bioprinting CLL progenitor cells acquired using Axio Observer Zeiss fluorescence microscopy for live/dead assay at day 28 [ 83 ]. Reproduced with permission Copyright 2021, Frontiers Media.

4. Conclusion

This study detailed the construction of in vitro organoid tumor models using various bioinks combined with different printing methods. The emergence of 3D bioprinting technology has led to significant breakthroughs in developing various in vitro organoid tumor models and hydrogel tissue engineering. Using bioprinting technology, significant technological innovations have been achieved in material selection and overall construction. Multiple printing techniques and bio-materials are being used to address the limitations of traditional tumor organoid modeling. In vitro organoid tumor models modify the heterogeneity of the microenvironment, including the presence of non-tumor cells and their functions, the signaling of soluble cell factors, and changes in extracellular matrix components.

Tumor treatments are increasingly shifting towards personalized therapies targeting individuals with unique and heterogeneous diseases. Some studies have linked in vitro model responses to drugs with patient outcomes. The use of in vitro models for high-throughput drug screening is widely applied in oncology research and holds promise as a potential tool for screening effective drugs for patients with tumors. The main limitations of functional precision medicine are the establishment of physiological culture models, the development of high-throughput systems, and difficulties in measuring tumor heterogeneity. Bioprinting overcomes these barriers, with 3D in vitro tumor models being promising for precision medicine, allowing for rapid in vitro modeling using tumor cells sourced from patients to accurately simulate patient responses to treatment. They are physiologically relevant, personalized tumor models that are highly suitable for drug development and clinical applications and facilitate individual tumor response analysis.

Although bioprinting organoid tumor models are continuously advancing and providing innovative biomedical and clinical translational research approaches, some obstacles remain to be addressed. First, current tumor models only simulate a single type of tumor and cannot simultaneously simulate the development of multiple tumors. In the future, the various advantages of bioprinting will make it suitable for developing human tumor microchip models, and microchip technology is expected to achieve connectivity and communication between multiple tumor models, treating various organs/tissues as one model. Using this multi-organ microchip model, complex drug metabolism that single-organ tumor models cannot simulate can be achieved, similar to the human body environment.

Second, limited reproducibility is a significant barrier to generating organoid tumor models and maintaining their functionality. The primary factors affecting the reproducibility of tumor models include batch-to-batch variability, production scalability, cell composition, and tumor model structure. Future material research should focus on developing and designing bioinks with better biological performance or composite bioactive factors to functionalize bioinks continually, thus mimicking in vivo growth factors or other mechanical and chemical stimuli to functionalize and maintain the reproducibility of printed structures. By improving the standards of bioprinting materials, the reproducibility of tumor models will continue to improve, ultimately leading to significant progress in clinical trials.

Further expansion of bioprinting technology is needed to elucidate the interactions between multiple cells and build organoid tumor models that better reflect physiological states, thereby increasing their usefulness in clinical trials. Meanwhile, 4D printing, a promising technology platform with highly tunable material selection, allows bio-materials to respond to external stimuli, enabling the development of bio-folding hydrogel scaffolds with self-folding behavior and allowing pre-printed 3D configurations to change over time, advancing the manufacture of functional 3D tissues significantly. 4D printing technology is also attractive for drug delivery systems, allowing the programmable release of drugs or cells, reducing drug leakage, and improving drug delivery efficiency. So far, existing self-assembly or self-folding 4D printing systems have been limited to macroscopic deformations, restricting the precise spatial manipulation of 4D printed structures. Therefore, the exact construction of organoid tumor models requires the integration of advanced technologies from various fields.

CRediT authorship contribution statement

Xiangran Cui: Writing – original draft, Visualization, Validation, Resources, Methodology, Formal analysis. Jianhang Jiao: Validation, Resources, Methodology, Funding acquisition. Lili Yang: Writing – review & editing, Validation, Resources. Yang Wang: Writing – review & editing, Supervision. Weibo Jiang: Writing – review & editing, Supervision. Tong Yu: Writing – review & editing, Validation, Resources. Mufeng Li: Writing – review & editing, Supervision. Han Zhang: Writing – review & editing, Validation, Resources. Bo Chao: Writing – review & editing, Validation, Resources. Zhonghan Wang: Writing – review & editing, Supervision, Resources, Conceptualization. Minfei Wu: Writing – review & editing, Supervision, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This study was supported by the Department of Science and Technology of Jilin Province ( 20210204104YY ;, YDZJ202201ZYTS281 ;, YDZJ202201ZYTS135 ;, YDZJ202301ZYTS031 ;, YDZJ202301ZYTS032 ) and Scientific Development Program of Jilin Province ( 20240402016 GH ;, 20240602083RC ) and Bethune Plan of Jilin University ( 2023B08 ;, 2023B10 ) and project of the Department of Science and Technology of Jilin Province, with the project identifier YDZJ202401434ZYTS .

Data availability