Cells interacting over an extracellular matrix (ECM) exhibit
emergent behaviors, which are often observably different from
single-cell dynamics. Fibroblasts embedded in a 3-D ECM, for
example, compact the surrounding gel and generate an
anisotropic strain field, which cannot be observed in single cellinduced
gel compaction. This emergent matrix behavior results
from collective intracellular mechanical interaction and is crucial
to explain the large deformations and mechanical tensions that
occur during embryogenesis, tissue development and wound
healing. Prediction of multi-cellular interactions entails
nonlinear dynamic simulation, which is prohibitively complex to
compute using first principles especially as the number of cells
increase. Here, we introduce a new methodology for predicting
nonlinear behaviors of multiple cells interacting mechanically
through a 3D ECM. In the proposed method, we first apply Dual-
Faceted Linearization to nonlinear dynamic systems describing
cell/matrix behavior. Using this unique linearization method, the
original nonlinear state equations can be expressed with a pair of
linear dynamic equations by augmenting the independent state
variables with auxiliary variables which are nonlinearly
dependent on the original states. Furthermore, we can find a
reduced order latent space representation of the dynamic
equations by orthogonal projection onto the basis of a lower
dimensional linear manifold within the augmented variable
space. Once converted to latent variable equations, we superpose
multiple dynamic systems to predict their collective behaviors.
The method is computationally efficient and accurate as
demonstrated through its application for prediction of emergent
cell induced ECM compaction.
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Learning the dynamics of cell–cell interactions in confined cell migration
The migratory dynamics of cells in physiological processes, ranging from wound healing to cancer metastasis, rely on contact-mediated cell–cell interactions. These interactions play a key role in shaping the stochastic trajectories of migrating cells. While data-driven physical formalisms for the stochastic migration dynamics of single cells have been developed, such a framework for the behavioral dynamics of interacting cells still remains elusive. Here, we monitor stochastic cell trajectories in a minimal experimental cell collider: a dumbbell-shaped micropattern on which pairs of cells perform repeated cellular collisions. We observe different characteristic behaviors, including cells reversing, following, and sliding past each other upon collision. Capitalizing on this large experimental dataset of coupled cell trajectories, we infer an interacting stochastic equation of motion that accurately predicts the observed interaction behaviors. Our approach reveals that interacting noncancerous MCF10A cells can be described by repulsion and friction interactions. In contrast, cancerous MDA-MB-231 cells exhibit attraction and antifriction interactions, promoting the predominant relative sliding behavior observed for these cells. Based on these experimentally inferred interactions, we show how this framework may generalize to provide a unifying theoretical description of the diverse cellular interaction behaviors of distinct cell types.
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- Award ID(s):
- 1734030
- NSF-PAR ID:
- 10230576
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 118
- Issue:
- 7
- ISSN:
- 0027-8424
- Page Range / eLocation ID:
- e2016602118
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The ability to program collective cell migration can allow us to control critical multicellular processes in development, regenerative medicine, and invasive disease. However, while various technologies exist to make individual cells migrate, translating these tools to control myriad, collectively interacting cells within a single tissue poses many challenges. For instance, do cells within the same tissue interpret a global migration ‘command’ differently based on where they are in the tissue? Similarly, since no stimulus is permanent, what are the long-term effects of transient commands on collective cell dynamics? We investigate these questions by bioelectrically programming large epithelial tissues to globally migrate ‘rightward’ via electrotaxis. Tissues clearly developed distinct rear, middle, side, and front responses to a single global migration stimulus. Furthermore, at no point poststimulation did tissues return to their prestimulation behavior, instead equilibrating to a 3rd, new migratory state. These unique dynamics suggested that programmed migration resets tissue mechanical state, which was confirmed by transient chemical disruption of cell–cell junctions, analysis of strain wave propagation patterns, and quantification of cellular crowd dynamics. Overall, this work demonstrates how externally driving the collective migration of a tissue can reprogram baseline cell–cell interactions and collective dynamics, even well beyond the end of the global migratory cue, and emphasizes the importance of considering the supracellular context of tissues and other collectives when attempting to program crowd behaviors.
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Abstract A major advantage of single cell RNA-sequencing (scRNA-Seq) data is the ability to reconstruct continuous ordering and trajectories for cells. Here we present TraSig, a computational method for improving the inference of cell-cell interactions in scRNA-Seq studies that utilizes the dynamic information to identify significant ligand-receptor pairs with similar trajectories, which in turn are used to score interacting cell clusters. We applied TraSig to several scRNA-Seq datasets and obtained unique predictions that improve upon those identified by prior methods. Functional experiments validate the ability of TraSig to identify novel signaling interactions that impact vascular development in liver organoids. Software https://github.com/doraadong/TraSig .more » « less
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Abstract Advances in single-cell technologies allow scrutinizing of heterogeneous cell states, however, detecting cell-state transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique to identify the underlying stochastic dynamics that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transitions, and distinguishes stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using coarse-grained transition path theory. Downstream analysis identifies distinct genes that mark the transient states or drive the transitions. The method is consistent with the well-established Langevin equation and transition rate theory. Applying MuTrans to datasets collected from five different single-cell experimental platforms, we show its capability and scalability to robustly unravel complex cell fate dynamics induced by transition cells in systems such as tumor EMT, iPSC differentiation and blood cell differentiation. Overall, our method bridges data-driven and model-based approaches on cell-fate transitions at single-cell resolution.
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Abstract Tumors develop in a complex physical, biochemical, and cellular milieu, referred to as the tumor microenvironment. Of special interest is the set of immune cells that reciprocally interact with the tumor, the tumor-immune microenvironment (TIME). The diversity of cell types and cell–cell interactions in the TIME has led researchers to apply concepts from ecology to describe the dynamics. However, while tumor cells are known to induce immune cells to switch from anti-tumor to pro-tumor phenotypes, this type of ecological interaction has been largely overlooked. To address this gap in cancer modeling, we develop a minimal, ecological model of the TIME with immune cell conversion, to highlight this important interaction and explore its consequences. A key finding is that immune conversion increases the range of parameters supporting a co-existence phase in which the immune system and the tumor reach a stalemate. Our results suggest that further investigation of the consequences of immune cell conversion, using detailed, data-driven models, will be critical for greater understanding of TIME dynamics.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2016602118
