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Abstract 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).

Cell adhesion is a fundamental phenomenon vital for all multicellular organisms. Recognition of and adhesion to specific macromolecules is a crucial task of leukocytes to initiate the immune response. To gain statistically reliable information of cell adhesion, large numbers of cells should be measured. However, direct measurement of the adhesion force of single cells is still challenging and today's techniques typically have an extremely low throughput (5-10 cells per day). Here, we introduce a computer controlled micropipette mounted onto a normal inverted microscope for probing single cell interactions with specific macromolecules. We calculated the estimated hydrodynamic lifting force acting on target cells by the numerical simulation of the flow at the micropipette tip. The adhesion force of surface attached cells could be accurately probed by repeating the pick-up process with increasing vacuum applied in the pipette positioned above the cell under investigation. Using the introduced methodology hundreds of cells adhered to specific macromolecules were measured one by one in a relatively short period of time (∼30 min). We blocked nonspecific cell adhesion by the protein non-adhesive PLL-g-PEG polymer. We found that human primary monocytes are less adherent to fibrinogen than their in vitro differentiated descendants: macrophages and dendritic cells, the latter producing the highest average adhesion force. Validation of the here introduced method was achieved by the hydrostatic step-pressure micropipette manipulation technique. Additionally the result was reinforced in standard microfluidic shear stress channels. Nevertheless, automated micropipette gave higher sensitivity and less side-effect than the shear stress channel. Using our technique, the probed single cells can be easily picked up and further investigated by other techniques; a definite advantage of the computer controlled micropipette. Our experiments revealed the existence of a sub-population of strongly fibrinogen adherent cells appearing in macrophages and highly represented in dendritic cells, but not observed in monocytes.

Abstract Selecting and isolating various cell types is a critical procedure in many applications, including immune therapy, regenerative medicine, and cancer research. Usually, these selection processes involve some labeling or another invasive step potentially affecting cellular functionality or damaging the cell. In the current proof of principle study, we first introduce an optical biosensor-based method capable of classification between healthy and numerous cancerous cell types in a label-free setup. We present high classification accuracy based on the monitored single-cell adhesion kinetic signals. We developed a high-throughput data processing pipeline to build a benchmark database of ~ 4500 single-cell adhesion measurements of a normal preosteoblast (MC3T3-E1) and various cancer (HeLa, LCLC-103H, MDA-MB-231, MCF-7) cell types. Several datasets were used with different cell-type selections to test the performance of deep learning-based classification models, reaching above 70–80% depending on the classification task. Beyond testing these models, we aimed to draw interpretable biological insights from their results; thus, we applied a deep neural network visualization method (grad-CAM) to reveal the basis on which these complex models made their decisions. Our proof-of-concept work demonstrated the success of a deep neural network using merely label-free adhesion kinetic data to classify single mammalian cells into different cell types. We propose our method for label-free single-cell profiling and in vitro cancer research involving adhesion. The employed label-free measurement is noninvasive and does not affect cellular functionality. Therefore, it could also be adapted for applications where the selected cells need further processing, such as immune therapy and regenerative medicine.

Abstract The glycocalyx is thought to perform a potent, but not yet defined function in cellular adhesion and signaling. Since 95% of cancer cells have altered glycocalyx structure, this role can be especially important in cancer development and metastasis. The glycocalyx layer of cancer cells directly influences cancer progression, involving the complicated kinetic process of cellular adhesion at various levels. In the present work, we investigated the effect of enzymatic digestion of specific glycocalyx components on cancer cell adhesion to RGD (arginine–glycine–aspartic acid) peptide motif displaying surfaces. High resolution kinetic data of cell adhesion was recorded by the surface sensitive label-free resonant waveguide grating (RWG) biosensor, supported by fluorescent staining of the cells and cell surface charge measurements. We found that intense removal of chondroitin sulfate (CS) and dermatan sulfate chains by chondroitinase ABC reduced the speed and decreased the strength of adhesion of HeLa cells. In contrast, mild digestion of glycocalyx resulted in faster and stronger adhesion. Control experiments on a healthy and another cancer cell line were also conducted, and the discrepancies were analysed. We developed a biophysical model which was fitted to the kinetic data of HeLa cells. Our analysis suggests that the rate of integrin receptor transport to the adhesion zone and integrin-RGD binding is strongly influenced by the presence of glycocalyx components, but the integrin-RGD dissociation is not. Moreover, based on the kinetic data we calculated the dependence of the dissociation constant of integrin-RGD binding on the enzyme concentration. We also determined the dissociation constant using a 2D receptor binding model based on saturation level static data recorded at surfaces with tuned RGD densities. We analyzed the discrepancies of the kinetic and static dissociation constants, further illuminating the role of cancer cell glycocalyx during the adhesion process. Altogether, our experimental results and modelling demonstrated that the chondroitin sulfate and dermatan sulfate chains of glycocalyx have an important regulatory function during the cellular adhesion process, mainly controlling the kinetics of integrin transport and integrin assembly into mature adhesion sites. Our results potentially open the way for novel type of cancer treatments affecting these regulatory mechanisms of cellular glycocalyx.