Multiscale mechanobiology: Coupling models of adhesion kinetics and nonlinear tissue mechanics

Integrin mechanosensing relies on a pivot-clip mechanism to reinforce cell adhesion

Cells intricately sense mechanical forces from their surroundings, driving biophysical and biochemical activities. This mechanosensing phenomenon occurs at the cell-matrix interface, where mechanical forces resulting from cellular motion, such as migration or matrix stretching, are exchanged through surface receptors, primarily integrins, and their corresponding matrix ligands. A pivotal player in this interaction is the α5β1 integrin and fibronectin (FN) bond, known for its role in establishing cell adhesion sites for migration. However, upregulation of the α5β1-FN bond is associated with uncontrolled cell metastasis. This bond operates through catch bond dynamics, wherein the bond lifetime paradoxically increases with greater force. The mechanism sustaining the characteristic catch bond dynamics of α5β1-FN remains unclear. Leveraging molecular dynamics simulations, our approach unveils a pivot-clip mechanism. Two key binding sites on FN, namely the synergy site and the RGD (Arg-Gly-Asp) motif, act as active points for structural changes in α5β1 integrin. Conformational adaptations at these sites are induced by a series of hydrogen bond formations and breaks at the synergy site. We disrupt these adaptations through a double mutation on FN, known to reduce cell adhesion. A whole-cell finite-element model is employed to elucidate how the synergy site may promote dynamic α5β1-FN binding, resisting cell contraction. In summary, our study integrates molecular- and cellular-level modeling to propose that FN's synergy site reinforces cell adhesion through enhanced binding dynamics and a mechanosensitive pivot-clip mechanism. This work sheds light on the interplay between mechanical forces and cell-matrix interactions, contributing to our understanding of cellular behaviors in physiological and pathological contexts.

Electric field-mediated adhesive dynamics of cells inside bio-functionalised microchannels offers important cues for active control of cell-substrate adhesion

Adhesive dynamics of cells plays a critical role in determining different biophysical processes orchestrating health and disease in living systems. While the rolling of cells on functionalised substrates having similarity with biophysical pathways appears to be extensively discussed in the literature, the effect of an external stimulus in the form of an electric field on the same remains underemphasized. Here, we bring out the interplay of fluid shear and electric field on the rolling dynamics of adhesive cells in biofunctionalised micro-confinements. Our experimental results portray that an electric field, even restricted to low strengths within the physiologically relevant regimes, can significantly influence the cell adhesion dynamics. We quantify the electric field-mediated adhesive dynamics of the cells in terms of two key parameters, namely, the voltage-altered rolling velocity and the frequency of adhesion. The effect of the directionality of the electric field with respect to the flow direction is also analysed by studying cellular migration with electrical effects acting both along and against the flow. Our experiment, on one hand, demonstrates the importance of collagen functionalisation in the adhesive dynamics of cells through micro channels, while on the other hand, it reveals how the presence of an axial electric field can lead to significant alteration in the kinetic rate of bond breakage, thereby modifying the degree of cell-substrate adhesion and quantifying in terms of the adhesion frequency of the cells. Proceeding further forward, we offer a simple theoretical explanation towards deriving the kinetics of cellular bonding in the presence of an electric field, which corroborates favourably with our experimental outcome. These findings are likely to offer fundamental insights into the possibilities of local control of cellular adhesion via electric field mediated interactions, bearing critical implications in a wide variety of medical conditions ranging from wound healing to cancer metastasis.

Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist

The extracellular matrix (ECM) is a highly complex structure through which biochemical and mechanical signals are transmitted. In processes of cell migration, the ECM also acts as a scaffold, providing structural support to cells as well as points of potential attachment. Although the ECM is a well-studied structure, its role in many biological processes remains difficult to investigate comprehensively due to its complexity and structural variation within an organism. In tandem with experiments, mathematical models are helpful in refining and testing hypotheses, generating predictions, and exploring conditions outside the scope of experiments. Such models can be combined and calibrated with in vivo and in vitro data to identify critical cell-ECM interactions that drive developmental and homeostatic processes, or the progression of diseases. In this review, we focus on mathematical and computational models of the ECM in processes such as cell migration including cancer metastasis, and in tissue structure and morphogenesis. By highlighting the predictive power of these models, we aim to help bridge the gap between experimental and computational approaches to studying the ECM and to provide guidance on selecting an appropriate model framework to complement corresponding experimental studies.

Multiscale mechanical characterization and computational modeling of fibrin gels

Fibrin is a naturally occurring protein network that forms a temporary structure to enable remodeling during wound healing. It is also a common tissue engineering scaffold because the structural properties can be controlled. However, to fully characterize the wound healing process and improve the design of regenerative scaffolds, understanding fibrin mechanics at multiple scales is necessary. Here, we present a strategy to quantify both the macroscale (1-10 mm) stress-strain response and the deformation of the mesoscale (10-1000 µm) network structure during unidirectional tensile tests. The experimental data were then used to inform a computational model to accurately capture the mechanical response of fibrin gels. Simultaneous mechanical testing and confocal microscopy imaging of fluorophore-conjugated fibrin gels revealed up to an 88% decrease in volume coupled with increase in volume fraction in deformed gels, and non-affine fiber alignment in the direction of deformation. Combination of the computational model with finite element analysis enabled us to predict the strain fields that were observed experimentally within heterogenous fibrin gels with spatial variations in material properties. These strategies can be expanded to characterize and predict the macroscale mechanics and mesoscale network organization of other heterogeneous biological tissues and matrices. STATEMENT OF SIGNIFICANCE: Fibrin is a naturally-occurring scaffold that supports cellular growth and assembly of de novo tissue and has tunable material properties. Characterization of meso- and macro-scale mechanics of fibrin gel networks can advance understanding of the wound healing process and impact future tissue engineering approaches. Using structural and mechanical characteristics of fibrin gels, a theoretical and computational model that can predict multiscale fibrin network mechanics was developed. These data and model can be used to design gels with tunable properties.

Understanding How Cells Probe the World: A Preliminary Step towards Modeling Cell Behavior?

Cell biologists have long aimed at quantitatively modeling cell function. Recently, the outstanding progress of high-throughput measurement methods and data processing tools has made this a realistic goal. The aim of this paper is twofold: First, to suggest that, while much progress has been done in modeling cell states and transitions, current accounts of environmental cues driving these transitions remain insufficient. There is a need to provide an integrated view of the biochemical, topographical and mechanical information processed by cells to take decisions. It might be rewarding in the near future to try to connect cell environmental cues to physiologically relevant outcomes rather than modeling relationships between these cues and internal signaling networks. The second aim of this paper is to review exogenous signals that are sensed by living cells and significantly influence fate decisions. Indeed, in addition to the composition of the surrounding medium, cells are highly sensitive to the properties of neighboring surfaces, including the spatial organization of anchored molecules and substrate mechanical and topographical properties. These properties should thus be included in models of cell behavior. It is also suggested that attempts at cell modeling could strongly benefit from two research lines: (i) trying to decipher the way cells encode the information they retrieve from environment analysis, and (ii) developing more standardized means of assessing the quality of proposed models, as was done in other research domains such as protein structure prediction.

Hyperelastic characterization reveals proteoglycans drive the nanoscale strain-stiffening response in hyaline cartilage

Degenerative diseases such as osteoarthritis (OA) result in deterioration of cartilage extracellular matrix (ECM) components, significantly compromising tissue function. For measurement of mechanical properties at micron resolution, atomic force microscopy (AFM) is a leading technique in biomaterials research, including in the study of OA. It is common practice to determine material properties by applying classical Hertzian contact theory to AFM data. However, errors are consequential because the application of a linear elastic contact model to tissue ignores the fact that soft materials exhibit nonlinear properties even at small strains, influencing the biological conclusions of clinically-relevant studies. Additionally, nonlinear material properties are not well characterized, limiting physiological relevance of Young's modulus. Here, we probe the ECM of hyaline cartilage with AFM and explore the application of Hertzian theory in comparison to five hyperelastic models: NeoHookean, Mooney-Rivlin, Arruda-Boyce, Fung, and Ogden. The Fung and Ogden models achieved the best fits of the data, but the Fung model demonstrated robust sensitivity during model validation, demonstrating its ideal application to cartilage ECM and potentially other connective tissues. To develop a biological understanding of the Fung nonlinear parameter, we selectively degraded ECM components to target collagens (purified collagenase), hyaluronan (bacterial hyaluronidase), and glycosaminoglycans (chondroitinase ABC). We found significant differences in both Fung parameters in response to enzymatic treatment, indicating that proteoglycans drive the nonlinear response of cartilage ECM, and validating biological relevance of these phenomenological parameters. Our findings add value to the biomechanics community of using two-parameter material models for microindentation of soft biomaterials.

Dynamic and static biomechanical traits of cardiac fibrosis

Cardiac fibrosis is a common pathology in cardiovascular diseases which are reported as the leading cause of death globally. In recent decades, accumulating evidence has shown that the biomechanical traits of fibrosis play important roles in cardiac fibrosis initiation, progression and treatment. In this review, we summarize the four main distinct biomechanical traits (i.e., stretch, fluid shear stress, ECM microarchitecture, and ECM stiffness) and categorize them into two different types (i.e., static and dynamic), mainly consulting the unique characteristic of the heart. Moreover, we also provide a comprehensive overview of the effect of different biomechanical traits on cardiac fibrosis, their transduction mechanisms, and in-vitro engineered models targeting biomechanical traits that will aid the identification and prediction of mechano-based therapeutic targets to ameliorate cardiac fibrosis.