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Mapping single-cell atlases throughout Metazoa unravels cell type evolution
Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases. Yet, comparison between distant relatives is hindered by complex gene histories and diversifications in expression programs. Previously,...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139856/ https://www.ncbi.nlm.nih.gov/pubmed/33944782 http://dx.doi.org/10.7554/eLife.66747 |
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author | Tarashansky, Alexander J Musser, Jacob M Khariton, Margarita Li, Pengyang Arendt, Detlev Quake, Stephen R Wang, Bo |
author_facet | Tarashansky, Alexander J Musser, Jacob M Khariton, Margarita Li, Pengyang Arendt, Detlev Quake, Stephen R Wang, Bo |
author_sort | Tarashansky, Alexander J |
collection | PubMed |
description | Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases. Yet, comparison between distant relatives is hindered by complex gene histories and diversifications in expression programs. Previously, we introduced the self-assembling manifold (SAM) algorithm to robustly reconstruct manifolds from single-cell data (Tarashansky et al., 2019). Here, we build on SAM to map cell atlas manifolds across species. This new method, SAMap, identifies homologous cell types with shared expression programs across distant species within phyla, even in complex examples where homologous tissues emerge from distinct germ layers. SAMap also finds many genes with more similar expression to their paralogs than their orthologs, suggesting paralog substitution may be more common in evolution than previously appreciated. Lastly, comparing species across animal phyla, spanning sponge to mouse, reveals ancient contractile and stem cell families, which may have arisen early in animal evolution. |
format | Online Article Text |
id | pubmed-8139856 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-81398562021-05-24 Mapping single-cell atlases throughout Metazoa unravels cell type evolution Tarashansky, Alexander J Musser, Jacob M Khariton, Margarita Li, Pengyang Arendt, Detlev Quake, Stephen R Wang, Bo eLife Computational and Systems Biology Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases. Yet, comparison between distant relatives is hindered by complex gene histories and diversifications in expression programs. Previously, we introduced the self-assembling manifold (SAM) algorithm to robustly reconstruct manifolds from single-cell data (Tarashansky et al., 2019). Here, we build on SAM to map cell atlas manifolds across species. This new method, SAMap, identifies homologous cell types with shared expression programs across distant species within phyla, even in complex examples where homologous tissues emerge from distinct germ layers. SAMap also finds many genes with more similar expression to their paralogs than their orthologs, suggesting paralog substitution may be more common in evolution than previously appreciated. Lastly, comparing species across animal phyla, spanning sponge to mouse, reveals ancient contractile and stem cell families, which may have arisen early in animal evolution. eLife Sciences Publications, Ltd 2021-05-04 /pmc/articles/PMC8139856/ /pubmed/33944782 http://dx.doi.org/10.7554/eLife.66747 Text en © 2021, Tarashansky et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Computational and Systems Biology Tarashansky, Alexander J Musser, Jacob M Khariton, Margarita Li, Pengyang Arendt, Detlev Quake, Stephen R Wang, Bo Mapping single-cell atlases throughout Metazoa unravels cell type evolution |
title | Mapping single-cell atlases throughout Metazoa unravels cell type evolution |
title_full | Mapping single-cell atlases throughout Metazoa unravels cell type evolution |
title_fullStr | Mapping single-cell atlases throughout Metazoa unravels cell type evolution |
title_full_unstemmed | Mapping single-cell atlases throughout Metazoa unravels cell type evolution |
title_short | Mapping single-cell atlases throughout Metazoa unravels cell type evolution |
title_sort | mapping single-cell atlases throughout metazoa unravels cell type evolution |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139856/ https://www.ncbi.nlm.nih.gov/pubmed/33944782 http://dx.doi.org/10.7554/eLife.66747 |
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