Data driven and biophysical insights into the regulation of trafficking vesicles by extracellular matrix stiffness

Abstract

Biomechanical signals from remodeled extracellular matrix (ECM) promote tumor progression. Here, we show that cell-matrix and cell-cell communication may be inherently linked and tuned through mechanisms of mechanosensitive biogenesis of trafficking vesicles. Pan-cancer analysis of cancer cells' mechanical properties (focusing primarily on cell stiffness) on substrates of varied stiffness and composition elucidated a heterogeneous cellular response to mechanical stimuli. Through machine learning, we identified a fingerprint of cytoskeleton-related proteins that accurately characterize cell stiffness in different ECM conditions. Expression of their respective genes correlates with patient prognosis across different tumor types. The levels of selected cytoskeleton proteins indicated that cortical tension mirrors the increase (or decrease) in cell stiffness with a change in ECM stiffness. A mechanistic biophysical model shows that the tendency for curvature generation by curvature-inducing proteins has an ultrasensitive dependence on cortical tension. This study thus highlights the effect of ECM stiffness, mediated by cortical tension, in modulating vesicle biogenesis.

Keywords: Biocomputational method; Biophysics; Cancer; Immunology; Mathematical biosciences.

Graphical abstract

Figure 1

Cell stiffness characterization for cancer cell lines grown on different ECM substrates (A) 20 cancer cell lines grown on seven different matrix conditions explored in cell stiffness measurements. (HA: Hyaluronic Acid). (B) Cell stiffness (Pa) for each cell line averaged over respective cells grown on different ECM substrates (left). Ratio of cell counts in high vs. low stiffness categories where cutoff used for demarcation is the median of KDE_all (right). (C and D) COL: Collagen, FN: Fibronectin, HACOL: Collagen coated HA, and HAFN: Fibronectin coated HA. (C) Average cell stiffness (Pa) for each cell line on each of the seven ECM substrates. (D) Ratio of cell counts in high vs. low stiffness categories where cutoff used for demarcation is the median of the cell type-specific KDE of cell stiffness over all substrates. (E) For each cell line, the average value of the ratio of the Young’s Moduli of the cells in moving from one substrate to another as a measure of the cell stiffness sensitivity to substrate change. (COL fold: 30kPa COL/500Pa COL, FN fold: 30kPa FN/500Pa FN, HYAL fold: HACOL/HAFN, Glass fold: Glass/500Pa COL, HAFN fold: HAFN/500Pa FN, HACOL fold: HACOL/500Pa COL).

Figure 2

Cytoskeleton proteins correlated with and predictive of cell stiffness for cancer cell lines grown on different ECM substrates (A) 18 cytoskeletal-related proteins whose mutual information with cell stiffness ≥0.8. (B) PCA heatmap of first five principal components (PC1-5) of protein expression levels in low stiffness and high stiffness cells. Pairwise angles between the respective PC1-5 of low and high stiffness cells. (C) Shapley values of the selected 18 cytoskeletal-related proteins used as features in neural network to predict cell stiffness category (with balanced accuracy of 98% on the test set). Histogram showing the distribution of protein expressions of the top five proteins in low and high cell stiffness categories.

Figure 3

Effect of cortical tension on curvature generation by curvature-inducing proteins (A) Snapshot illustrating the change in membrane curvature owing to curvature-inducing proteins (here, modeled as curvature fields). (B) Mean square errors (MSE) between analytical (non-linear fit of Equation 1) and simulation data for different values of degree n in Equation 1 across various values of the total number of protein fields (n_p) on the simulated membrane patch. (C) Number of protein fields in the tubular region (n_t) as a function of excess area (A/A_p, proxy for cortical tension) for different values of n_p on the simulated membrane patch (Eqn: non-linear fits of Equation 1 with n = 4, Sim: simulation data).

Figure 4

Schematic of how ECM stiffness affects the formation of curved structures, essential in endocytic pathways, by curvature-inducing proteins Here, the generation of highly curved structures (such as vesicles and tubules) as a function of excess area A/A_p (proxy for cortical tension) has been quantified using the results from our biophysical model (Figure 3C) as the number of protein fields in membrane protrusions n_t) normalized by the total number of protein fields (n_p). Fold change in the curvature generation (i.e. ratio of the normalized metric) with increase (or decrease) of cortical tension when going from low ECM stiffness to high ECM stiffness is shown for different amounts of curvature-inducing proteins on the membrane. MVE: multivesicular endosomes, ECM: Extracellular Matrix. Created with Biorender.com.