Emergence of division of labor in tissues through cell interactions and spatial cues.
Emergence of division of labor in tissues through cell interactions and spatial cues.
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DOI:
10.1016/j.celrep.2023.112412
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发表时间:
2023-05-30
期刊:
影响因子:
8.8
通讯作者:
中科院分区:
文献类型:
--
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Most cell types in multicellular organisms can perform multiple functions. However, not all functions can be optimally performed simultaneously by the same cells. Functions incompatible at the level of individual cells can be performed at the cell population level, where cells divide labor and specialize in different functions. Division of labor can arise due to instruction by tissue environment or through self-organization. Here, we develop a computational framework to investigate the contribution of these mechanisms to division of labor within a cell-type population. By optimizing collective cellular task performance under trade-offs, we find that distinguishable expression patterns can emerge from cell-cell interactions versus instructive signals. We propose a method to construct ligand-receptor networks between specialist cells and use it to infer division-of-labor mechanisms from single-cell RNA sequencing (RNA-seq) and spatial transcriptomics data of stromal, epithelial, and immune cells. Our framework can be used to characterize the complexity of cell interactions within tissues. Division of labor theory predicts distinct, mechanism-dependent spatial patterns Spatial proximity of specialist cells suggests involvement of cell interactions Archetype crosstalk networks reveal patterns of interactions between specialist cells Enterocytes and fibroblasts exemplify distinct division of labor strategies Two of the mechanisms that can drive division of labor in tissues are external signaling gradients and cell-cell interactions. Theory for optimal task allocation explains how to distinguish between the two strategies in single-cell and spatial omics data. This is demonstrated for colon fibroblasts, intestinal enterocytes, lung fibroblasts, and macrophages.
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影响因子:
64.8
作者:
Halpern KB;Shenhav R;Matcovitch-Natan O;Toth B;Lemze D;Golan M;Massasa EE;Baydatch S;Landen S;Moor AE;Brandis A;Giladi A;Avihail AS;David E;Amit I;Itzkovitz S
通讯作者:
Itzkovitz S
影响因子:
3
作者:
Baum J;Duffy HS
通讯作者:
Duffy HS
影响因子:
7.5
作者:
Ma F;Zhang S;Song L;Wang B;Wei L;Zhang F
通讯作者:
Zhang F
DOI:
10.1038/s41577-019-0131-x
发表时间:
2019-04
期刊:
Nature reviews. Immunology
影响因子:
--
作者:
Altan-Bonnet G;Mukherjee R
通讯作者:
Mukherjee R
影响因子:
16.6
作者:
Muhl, Lars;Genove, Guillem;Betsholtz, Christer
通讯作者:
Betsholtz, Christer