Emergence of division of labor in tissues through cell interactions and spatial cues

Miri Adler, Noa Moriel, Aleksandrina Goeva, Inbal Avraham-Davidi, Simon Mages, Taylor S. Adams, Naftali Kaminski, Evan Z. Macosko, Aviv Regev*, Ruslan Medzhitov*, Mor Nitzan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageAmerican English
Article number112412
JournalCell Reports
Volume42
Issue number5
DOIs
StatePublished - 30 May 2023

Bibliographical note

Publisher Copyright:
© 2023

Keywords

  • CP: Developmental biology
  • Pareto optimality
  • division of labor
  • enterocytes
  • fibroblasts
  • lateral inhibition
  • macrophages
  • morphogens
  • self-organization

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