More Like this

1. 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)
2. 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)
3. 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. 

   more » « less
4. CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics

   https://doi.org/10.1038/s41596-024-01045-4  

   Jin, Suoqin; Plikus, Maksim V; Nie, Qing (September 2024, Nature Protocols)
5. A single-cell resolved cell-cell communication model explains lineage commitment in hematopoiesis

   https://doi.org/10.1242/dev.199779  

   Rommelfanger, Megan K.; MacLean, Adam L. (December 2021, Development)

   ABSTRACT Cells do not make fate decisions independently. Arguably, every cell-fate decision occurs in response to environmental signals. In many cases, cell-cell communication alters the dynamics of the internal gene regulatory network of a cell to initiate cell-fate transitions, yet models rarely take this into account. Here, we have developed a multiscale perspective to study the granulocyte-monocyte versus megakaryocyte-erythrocyte fate decisions. This transition is dictated by the GATA1-PU.1 network: a classical example of a bistable cell-fate system. We show that, for a wide range of cell communication topologies, even subtle changes in signaling can have pronounced effects on cell-fate decisions. We go on to show how cell-cell coupling through signaling can spontaneously break the symmetry of a homogenous cell population. Noise, both intrinsic and extrinsic, shapes the decision landscape profoundly, and affects the transcriptional dynamics underlying this important hematopoietic cell-fate decision-making system. This article has an associated ‘The people behind the papers’ interview. 

   more » « less