MOTIVATION: In this work, we present an analytical method for quantifying both single-cell morphologies and cell network topologies of tumor cell populations and use it to predict 3D cell behavior. RESULTS: We utilized a supervised deep learning approach to perform instance segmentation on label-free live cell images across a wide range of cell densities. We measured cell shape properties and characterized network topologies for 136 single-cell clones derived from the YUMM1.7 and YUMMER1.7 mouse melanoma cell lines. Using an unsupervised clustering algorithm, we identified six distinct morphological subclasses. We further observed differences in tumor growth and invasion dynamics across subclasses in an in vitro 3D spheroid model. Compared to existing methods for quantifying 2D or 3D phenotype, our analytical method requires less time, needs no specialized equipment and is capable of much higher throughput, making it ideal for applications such as high-throughput drug screening and clinical diagnosis. AVAILABILITY AND IMPLEMENTATION: https://github.com/trevor-chan/Melanoma_NetworkMorphology. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Cancer cell heterogeneity can manifest genetically and phenotypically. Bioinformatics methods have been used to analyze complex genomics and transcriptomics data, but have not been well-established for analyzing biophysical data of phenotypically heterogeneous tumor cells. Here, we take an informatics approach to analyze the biophysical data of MDA-MB-231 cells, a widely used breast cancer cell line, during their spontaneous migration through confined environments. Experimentally, we vary the constriction microchannel geometries (wide channel, short constriction, and long constriction) and apply drug treatments. We find that cells in the short constriction are similar in morphology to the cells in the wide channel. However, their fluorescence profiles are comparable to those in the long constriction. We demonstrate that the cell migratory phenotype is correlated more to mitochondria in a non-confined environment and more to actin in a confined environment. We demonstrate that the cells' migratory phenotypes are altered by ciliobrevin D, a dynein inhibitor, in both confined and non-confined environments. Overall, our approach elucidates phenotypic heterogeneity in cancer cells under confined microenvironments at single-cell resolution. RESULTS: Here, we apply a bioinformatics approach to a single cell invasion assay. We demonstrate that this method can determine distinctions in morphology, cytoskeletal activities, and mitochondrial activities under various geometric constraints and for cells of different speeds. Our approach can be readily adapted to various heterogeneity studies for different types of input biophysical data. In addition, this approach can be applied to studies related to biophysical changes due to differences in external stimuli, such as treatment effects on cellular and subcellular activities, at single-cell resolution. Finally, as similar bioinformatics methods have been widely applied in studies of genetic heterogeneity, biophysical information extracted using this approach can be analyzed together with the genetic data to relate genetic and phenotypic heterogeneity. AVAILABILITY: The data that support the findings of this study are available from the corresponding author upon reasonable request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The mechanical properties of the extracellular matrix (ECM)-a complex, 3D, fibrillar scaffold of cells in physiological environments-modulate cell behavior and can drive tissue morphogenesis, regeneration, and disease progression. For simplicity, it is often convenient to assume these properties to be time-invariant. In living systems, however, cells dynamically remodel the ECM and create time-dependent local microenvironments. Here, we show how cell-generated contractile forces produce substantial irreversible changes to the density and architecture of physiologically relevant ECMs-collagen I and fibrin-in a matter of minutes. We measure the 3D deformation profiles of the ECM surrounding cancer and endothelial cells during stages when force generation is active or inactive. We further correlate these ECM measurements to both discrete fiber simulations that incorporate fiber crosslink unbinding kinetics and continuum-scale simulations that account for viscoplastic and damage features. Our findings further confirm that plasticity, as a mechanical law to capture remodeling in these networks, is fundamentally tied to material damage via force-driven unbinding of fiber crosslinks. These results characterize in a multiscale manner the dynamic nature of the mechanical environment of physiologically mimicking cell-in-gel systems.

Dimensionality is a fundamental component that can have profound implications on the characteristics of physical systems. In cell biology, however, the majority of studies on cell physical properties, from rheology to force generation to migration, have been performed on 2D substrates, and it is not clear how a more realistic 3D environment influences cell properties. Here, we develop an integrated approach and demonstrate the combination of mitochondria-tracking microrheology, microfluidics, and Brownian dynamics simulations to explore the impact of dimensionality on intracellular mechanics and on the effects of intracellular disruption. Additionally, we consider both passive thermal and active motor-driven processes within the cell and demonstrate through modeling how active internal fluctuations are modulated via dimensionality. Our results demonstrate that metastatic breast cancer cells (MDA-MB-231) exhibit more solid-like internal motions in 3D compared to 2D, and actin network disruption via Cytochalasin D has a more pronounced effect on internal cell fluctuations in 2D. Our computational results and modeling show that motor-induced active stress fluctuations are enhanced in 2D, leading to increased local intracellular particle fluctuations and apparent fluid-like behavior.