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Abstract The morphology and morphodynamics of cells as important biomarkers of the cellular state are widely appreciated in both fundamental research and clinical applications. Quantification of cell morphology often requires a large number of geometric measures that form a high-dimensional feature vector. This mathematical representation creates barriers to communicating, interpreting, and visualizing data. Here, we develop a deep learning-based algorithm to project 13-dimensional (13D) morphological feature vectors into 2-dimensional (2D) morphological latent space (MLS). We show that the projection has less than 5% information loss and separates the different migration phenotypes of metastatic breast cancer cells. Using the projection, we demonstrate the phenotype-dependent motility of breast cancer cells in the 3D extracellular matrix, and the continuous cell state change upon drug treatment. We also find that dynamics in the 2D MLS quantitatively agrees with the morphodynamics of cells in the 13D feature space, preserving the diffusive power and the Lyapunov exponent of cell shape fluctuations even though the dimensional reduction projection is highly nonlinear. Our results suggest that MLS is a powerful tool to represent and understand the cell morphology and morphodynamics. 

Abstract Contemporary approaches to instance segmentation in cell science use 2D or 3D convolutional networks depending on the experiment and data structures. However, limitations in microscopy systems or efforts to prevent phototoxicity commonly require recording sub-optimally sampled data that greatly reduces the utility of such 3D data, especially in crowded sample space with significant axial overlap between objects. In such regimes, 2D segmentations are both more reliable for cell morphology and easier to annotate. In this work, we propose the projection enhancement network (PEN), a novel convolutional module which processes the sub-sampled 3D data and produces a 2D RGB semantic compression, and is trained in conjunction with an instance segmentation network of choice to produce 2D segmentations. Our approach combines augmentation to increase cell density using a low-density cell image dataset to train PEN, and curated datasets to evaluate PEN. We show that with PEN, the learned semantic representation in CellPose encodes depth and greatly improves segmentation performance in comparison to maximum intensity projection images as input, but does not similarly aid segmentation in region-based networks like Mask-RCNN. Finally, we dissect the segmentation strength against cell density of PEN with CellPose on disseminated cells from side-by-side spheroids. We present PEN as a data-driven solution to form compressed representations of 3D data that improve 2D segmentations from instance segmentation networks. 

Abstract Cell shape is linked to cell function. The significance of cell morphodynamics, namely the temporal fluctuation of cell shape, is much less understood. Here we study the morphodynamics of MDA-MB-231 cells in type I collagen extracellular matrix (ECM). We systematically vary ECM physical properties by tuning collagen concentrations, alignment, and gelation temperatures. We find that morphodynamics of 3D migrating cells are externally controlled by ECM mechanics and internally modulated by Rho/ROCK-signaling. We employ machine learning to classify cell shape into four different morphological phenotypes, each corresponding to a distinct migration mode. As a result, we map cell morphodynamics at mesoscale into the temporal evolution of morphological phenotypes. We characterize the mesoscale dynamics including occurrence probability, dwell time and transition matrix at varying ECM conditions, which demonstrate the complex phenotype landscape and optimal pathways for phenotype transitions. In light of the mesoscale dynamics, we show that 3D cancer cell motility is a hidden Markov process whereby the step size distributions of cell migration are coupled with simultaneous cell morphodynamics. Morphological phenotype transitions also facilitate cancer cells to navigate non-uniform ECM such as traversing the interface between matrices of two distinct microstructures. In conclusion, we demonstrate that 3D migrating cancer cells exhibit rich morphodynamics that is controlled by ECM mechanics, Rho/ROCK-signaling, and regulate cell motility. Our results pave the way to the functional understanding and mechanical programming of cell morphodynamics as a route to predict and control 3D cell motility. 

To maintain normal functionality, it is necessary for a multicellular organism to generate robust responses to external temporal signals. However, the underlying mechanisms to coordinate the collective dynamics of cells remain poorly understood. Here, we study the calcium activity of biological neuron networks excited by periodic ATP stimuli. We use micropatterning to control the cells' physical connectivity. We find that whereas isolated cells become more synchronized in their calcium activity at long driving periods, connected cells become less synchronized, despite expressing more gap junctions which enable calcium exchange. To understand this result, we use a mathematical model in which a bifurcation analysis has previously shown coupling-induced desynchronization in an oscillatory network. Using parameters close to this bifurcation but in the excitable regime, we find that this desynchronization persists and can explain the experimental observations. The model further predicts that co-culturing with gap-junction-deficient cells should restore synchronization, which experiments confirm. Combining quantitative experiments, the physical and biological manipulation of cells, and mathematical modeling, our results suggest that cell-to-cell connectivity significantly affects how populations encode an external temporal signal as it slows down: Sparse networks synchronize due to longer entrainment, whereas highly connected networks can desynchronize due to dynamic frustration. Published by the American Physical Society2025 

The metastasis of solid tumors hinges on cancer cells navigating through complex three-dimensional tissue environments, characterized by mechanical heterogeneity and biological diversity. This process is closely linked to the dynamic migration behavior exhibited by cancer cells, which dictates the invasiveness of tumors. In our study, we investigate tumor spheroids composed of breast cancer cells embedded in three-dimensional (3D) collagen matrices. Through a combination of quantitative experiments, artificial-intelligence-driven image processing, and mathematical modeling, we uncover rapid transitions in cell phenotypes and phenotype-dependent motility among disseminating cells originating from tumor spheroids. Persistent invasion leads to continuous remodeling of the extracellular matrix surrounding the spheroids, altering the landscape of migration phenotypes. Consequently, filopodial cells emerge as the predominant phenotype across diverse extracellular matrix conditions. Our findings unveil the complex mesoscale dynamics of invading tumor spheroids, shedding light on the complex interplay between migration phenotype plasticity, microenvironment remodeling, and cell motility within 3D extracellular matrices. Published by the American Physical Society2024 

Altered tissue mechanics is an important signature of invasive solid tumors. While the phenomena have been extensively studied by measuring the bulk rheology of the extracellular matrix (ECM) surrounding tumors, micromechanical remodeling at the cellular scale remains poorly understood. By combining holographic optical tweezers and confocal microscopy on in vitro tumor models, we show that the micromechanics of collagen ECM surrounding an invading tumor demonstrate directional anisotropy, spatial heterogeneity and significant variations in time as tumors invade. To test the cellular mechanisms of ECM micromechanical remodeling, we construct a simple computational model and verify its predictions with experiments. We find that collective force generation of a tumor stiffens the ECM and leads to anisotropic local mechanics such that the extension direction is more rigid than the compression direction. ECM degradation by cell-secreted matrix metalloproteinase softens the ECM, and active traction forces from individual disseminated cells re-stiffen the matrix. Together, these results identify plausible biophysical mechanisms responsible for the remodeled ECM micromechanics surrounding an invading tumor. 

Coordinated responses to environmental stimuli are critical for multicellular organisms. To overcome the obstacles of cell-to-cell heterogeneity and noisy signaling dynamics within individual cells, cells must effectively exchange information with peers. However, the dynamics and mechanisms of collective information transfer driven by external signals are poorly understood. Here we investigate the calcium dynamics of neuronal cells that form confluent monolayers and respond to cyclic ATP stimuli in microfluidic devices. Using Granger inference to reconstruct the underlying causal relations between the cells, we find that the cells self-organize into spatially decentralized and temporally stationary networks to support information transfer via gap junction channels. The connectivity of the causal networks depends on the temporal profile of the external stimuli, where short periods, or long periods with small duty fractions, lead to reduced connectivity and fractured network topology. We build a theoretical model based on communicating excitable units that reproduces our observations. The model further predicts that connectivity of the causal network is maximal at an optimal communication strength, which is confirmed by the experiments. Together, our results show that information transfer between neuronal cells is externally regulated by the temporal profile of the stimuli and internally regulated by cell–cell communication.