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Capybara: A computational tool to measure cell identity and fate transitions

View ORCID ProfileWenjun Kong, View ORCID ProfileYuheng C. Fu, View ORCID ProfileSamantha A. Morris
doi: https://doi.org/10.1101/2020.02.17.947390
Wenjun Kong
1Department of Developmental Biology, Washington University School of Medicine in St. Louis. 660 S. Euclid Avenue, Campus Box 8103, St. Louis, MO 63110, USA
2Department of Genetics, Washington University School of Medicine in St. Louis. 660 S. Euclid Avenue, Campus Box 8103, St. Louis, MO 63110, USA
3Center of Regenerative Medicine. Washington University School of Medicine in St. Louis. 660 S. Euclid Avenue, Campus Box 8103, St. Louis, MO 63110, USA
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  • ORCID record for Wenjun Kong
Yuheng C. Fu
1Department of Developmental Biology, Washington University School of Medicine in St. Louis. 660 S. Euclid Avenue, Campus Box 8103, St. Louis, MO 63110, USA
2Department of Genetics, Washington University School of Medicine in St. Louis. 660 S. Euclid Avenue, Campus Box 8103, St. Louis, MO 63110, USA
3Center of Regenerative Medicine. Washington University School of Medicine in St. Louis. 660 S. Euclid Avenue, Campus Box 8103, St. Louis, MO 63110, USA
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Samantha A. Morris
1Department of Developmental Biology, Washington University School of Medicine in St. Louis. 660 S. Euclid Avenue, Campus Box 8103, St. Louis, MO 63110, USA
2Department of Genetics, Washington University School of Medicine in St. Louis. 660 S. Euclid Avenue, Campus Box 8103, St. Louis, MO 63110, USA
3Center of Regenerative Medicine. Washington University School of Medicine in St. Louis. 660 S. Euclid Avenue, Campus Box 8103, St. Louis, MO 63110, USA
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  • For correspondence: s.morris@wustl.edu
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Summary

Transitions in cell identity are fundamental to development, reprogramming, and disease. Single-cell technologies enable the dissection of tissue composition on a cell-by-cell basis in complex biological systems. However, highly-sparse single-cell RNA-seq data poses challenges for cell-type identification algorithms based on bulk RNA-seq. Single-cell analytical tools are also limited, where they require prior biological knowledge and typically classify cells in a discrete, categorical manner. Here, we present a computational tool, ‘Capybara,’ designed to measure cell identity as a continuum, at single-cell resolution. This approach enables the classification of discrete cell entities but also identifies cells harboring multiple identities, supporting a metric to quantify cell fate transition dynamics. We benchmark the performance of Capybara against other existing classifiers and demonstrate its efficacy to annotate cells and identify critical transitions within a well-characterized differentiation hierarchy, hematopoiesis. Our application of Capybara to a range of reprogramming strategies reveals previously uncharacterized regional patterning and identifies a putative in vivo correlate for an engineered cell type that has, to date, remained undefined. These findings prioritize interventions to increase the efficiency and fidelity of cell engineering strategies, showcasing the utility of Capybara to dissect cell identity and fate transitions. Capybara code and documentation are available at https://github.com/morris-lab/Capybara.

Footnotes

  • https://github.com/morris-lab/Capybara

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 17, 2020.
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Capybara: A computational tool to measure cell identity and fate transitions
Wenjun Kong, Yuheng C. Fu, Samantha A. Morris
bioRxiv 2020.02.17.947390; doi: https://doi.org/10.1101/2020.02.17.947390
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Capybara: A computational tool to measure cell identity and fate transitions
Wenjun Kong, Yuheng C. Fu, Samantha A. Morris
bioRxiv 2020.02.17.947390; doi: https://doi.org/10.1101/2020.02.17.947390

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