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Mapping single-cell atlases throughout Metazoa unravels cell type evolution.

Alexander J Tarashansky1, Jacob M Musser2, Margarita Khariton1

  • 1Department of Bioengineering, Stanford University, Stanford, United States.

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Summary

We developed SAMap, a new method to compare cell atlases across species, identifying homologous cell types and revealing ancient cell families. This advances our understanding of cellular diversity origins and evolution.

Keywords:
cell type evolutioncomputational biologycontractile cellsevolutionary biologygene expression programgene orthologymouseplanariansingle-cell atlasstem cellssystems biologyxenopuszebrafish

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Area of Science:

  • Evolutionary biology
  • Comparative genomics
  • Single-cell transcriptomics

Background:

  • Comparing single-cell transcriptomic atlases across species is crucial for understanding cellular diversity origins.
  • Gene histories and expression program diversification complicate cross-species comparisons.

Purpose of the Study:

  • To develop a method for mapping cell atlas manifolds across species.
  • To identify homologous cell types and shared expression programs in distant species.
  • To investigate gene evolution, including orthologs and paralogs, across animal phyla.

Main Methods:

  • Building upon the self-assembling manifold (SAM) algorithm.
  • Developing SAMap for cross-species cell atlas manifold mapping.
  • Applying SAMap to compare diverse animal species from sponge to mouse.

Main Results:

  • SAMap successfully identifies homologous cell types across distant species, even with distinct germ layer origins.
  • The study found evidence suggesting paralog substitution is a more common evolutionary mechanism than previously thought.
  • Ancient contractile and stem cell families were identified, potentially originating early in animal evolution.

Conclusions:

  • SAMap is a powerful tool for cross-species cell atlas comparison and annotation.
  • Gene duplication and divergence (paralogs) play a significant role in evolutionary innovation.
  • Comparative single-cell transcriptomics reveals deep evolutionary histories of fundamental cell types.