1. Computational and Systems Biology
  2. Evolutionary Biology

Mapping single-cell atlases throughout Metazoa unravels cell type evolution

  1. Alexander J Tarashansky
  2. Jacob M Musser
  3. Margarita Khariton
  4. Pengyang Li
  5. Detlev Arendt
  6. Stephen R Quake
  7. Bo Wang  Is a corresponding author
  1. Stanford University, United States
  2. European Molecular Biology Laboratory, Germany
https://doi.org/10.7554/eLife.66747
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Cite this article
  1. Alexander J Tarashansky
  2. Jacob M Musser
  3. Margarita Khariton
  4. Pengyang Li
  5. Detlev Arendt
  6. Stephen R Quake
  7. Bo Wang
(2021)
Mapping single-cell atlases throughout Metazoa unravels cell type evolution
eLife 10:e66747.
https://doi.org/10.7554/eLife.66747
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Abstract

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 mouse to sponge, reveals ancient contractile and stem cell families, which may have arisen early in animal evolution.

Data availability

All data analyzed during this study are available through various sources as listed in Supplementary File 1.

The following previously published data sets were used

Article and author information

Author details

  1. Alexander J Tarashansky

    Department of Bioengineering, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.

  2. Jacob M Musser

    Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    Competing interests
    The authors declare that no competing interests exist.

  3. Margarita Khariton

    Bioengineering, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Pengyang Li

    Department of Bioengineering, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. Detlev Arendt

    Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7833-050X

  6. Stephen R Quake

    Bioengineering, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.

  7. Bo Wang

    Bioengineering, Stanford University, Stanford, United States
    For correspondence
    wangbo@stanford.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8880-1432

Funding

Arnold and Mabel Beckman Foundation (Beckman Young Investigator Award)

  • Bo Wang

National Institutes of Health (1R35GM138061)

  • Bo Wang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All experiments with and care of mice are performed in accordance with protocols approved by the Institutional Animal Care and Use Committees (IACUC) of Stanford University (protocol approval number 30366).

Copyright

© 2021, Tarashansky et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Alexander J Tarashansky
  2. Jacob M Musser
  3. Margarita Khariton
  4. Pengyang Li
  5. Detlev Arendt
  6. Stephen R Quake
  7. Bo Wang
(2021)
Mapping single-cell atlases throughout Metazoa unravels cell type evolution
eLife 10:e66747.
https://doi.org/10.7554/eLife.66747

Share this article

https://doi.org/10.7554/eLife.66747

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Further reading

    1. Computational and Systems Biology
    2. Stem Cells and Regenerative Medicine

    Self-assembling manifolds in single-cell RNA sequencing data

    Alexander J Tarashansky, Yuan Xue ... Bo Wang
    Tools and Resources Updated

    Single-cell RNA sequencing has spurred the development of computational methods that enable researchers to classify cell types, delineate developmental trajectories, and measure molecular responses to external perturbations. Many of these technologies rely on their ability to detect genes whose cell-to-cell variations arise from the biological processes of interest rather than transcriptional or technical noise. However, for datasets in which the biologically relevant differences between cells are subtle, identifying these genes is challenging. We present the self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction. We demonstrate its advantages over other state-of-the-art methods with experimental validation in identifying novel stem cell populations of Schistosoma mansoni, a prevalent parasite that infects hundreds of millions of people. Extending our analysis to a total of 56 datasets, we show that SAM is generalizable and consistently outperforms other methods in a variety of biological and quantitative benchmarks.