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1. Cell mechanics, environmental geometry, and cell polarity control cell–cell collision outcomes

   https://doi.org/10.1039/D5SM00359H  

   Luo, Yongtian; Nain, Amrinder S; Camley, Brian A (September 2025, Soft Matter)

   Interactions between crawling cells, which are essential for many biological processes, can be quantified by measuring cell–cell collisions. Conventionally, experiments of cell–cell collisions are conducted on two-dimensional flat substrates, where colliding cells repolarize and move away upon contact with one another in ‘‘contact inhibition of locomotion’’ (CIL). Inspired by recent experiments that show cells on suspended nanofibers have qualitatively different CIL behaviors than those on flat substrates, we develop a phase field model of cell motility and two-cell collisions in fiber geometries. Our model includes cell–cell and cell–fiber adhesion, and a simple positive feedback mechanism of cell polarity. We focus on cell collisions on two parallel fibers, finding that larger cell deformability (lower membrane tension), larger positive feedback of polarization, and larger fiber spacing promote more occurrences of cells walking past one another. We can capture this behavior using a simple linear stability analysis on the cell–cell interface upon collision. 

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2. The landscape of cell–cell communication through single-cell transcriptomics

   https://doi.org/10.1016/j.coisb.2021.03.007  

   Almet, Axel A.; Cang, Zixuan; Jin, Suoqin; Nie, Qing (June 2021, Current Opinion in Systems Biology)
3. Cell alignment modulated by surface nano-topography – Roles of cell-matrix and cell-cell interactions

   https://doi.org/10.1016/j.actbio.2022.01.057  

   Coyle, Stephen; Doss, Bryant; Huo, Yucheng; Singh, Hemang Raj; Quinn, David; Jimmy Hsia, K.; LeDuc, Philip R. (April 2022, Acta Biomaterialia)
4. Learning the dynamics of cell–cell interactions in confined cell migration

   https://doi.org/10.1073/pnas.2016602118  

   Brückner, David B.; Arlt, Nicolas; Fink, Alexandra; Ronceray, Pierre; Rädler, Joachim O.; Broedersz, Chase P. (February 2021, Proceedings of the National Academy of Sciences)

   null (Ed.)

   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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5. Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data

   https://doi.org/10.1038/s41467-025-61149-7  

   Hou, Jiawen; Zhao, Wei; Nie, Qing (July 2025, Nature Communications)

   Abstract During cell-cell communication (CCC), pathways activated by different ligand-receptor pairs may have crosstalk with each other. While multiple methods have been developed to infer CCC networks and their downstream response using single-cell RNA-seq data (scRNA-seq), the potential crosstalk between pathways connecting CCC with its downstream targets has been ignored. Here we introduce a machine learning-based method SigXTalk to analyze the crosstalk using scRNA-seq data by quantifying signal fidelity and specificity, two critical quantities measuring the effect of crosstalk. Specifically, a hypergraph learning method is used to encode the higher-order relations among receptors, transcription factors and target genes within regulatory pathways. Benchmarking of SigXTalk using simulation and real-world data shows the effectiveness, robustness, and accuracy in identifying key shared molecules among crosstalk pathways and their roles in transferring shared CCC information. Analysis of disease data shows SigXTalk’s capability in identifying crucial signals, targets, regulatory networks, and CCC patterns that distinguish different disease conditions. Applications to the data with multiple time points reveals SigXTalk’s capability in tracking the evolution of crosstalk pathways over time. Together our studies provide a systematic analysis of CCC-induced regulatory networks from the perspective of crosstalk between pathways. 

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