1. Computational and Systems Biology

Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data

  1. Idan Hekselman
  2. Assaf Vital
  3. Maya Ziv-Agam
  4. Lior Kerber
  5. Ido Yairi
  6. Esti Yeger-Lotem  Is a corresponding author
  1. Ben-Gurion University of the Negev, Israel
https://doi.org/10.7554/eLife.84613
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  1. Idan Hekselman
  2. Assaf Vital
  3. Maya Ziv-Agam
  4. Lior Kerber
  5. Ido Yairi
  6. Esti Yeger-Lotem
(2024)
Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data
eLife 13:e84613.
https://doi.org/10.7554/eLife.84613
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Abstract

Mendelian diseases tend to manifest clinically in certain tissues, yet their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in Mendelian diseases. Overall, we inferred the likely affected cell types for 328 diseases. We corroborated our findings by literature text-mining, expert validation, and recapitulation in mouse corresponding tissues. Based on these findings, we explored characteristics of disease-affected cell types, showed that diseases manifesting in multiple tissues tend to affect similar cell types, and highlighted cases where gene functions could be used to refine inference. Together, these findings expand the molecular understanding of disease mechanisms and cellular vulnerability.

Data availability

All data generated or analyzed during this study are included in the manuscript, in supporting files. All data generated or analyzed during this study are included in the manuscript, in supporting files. The Source Data file contains data used to generate all figures. Additional data and code to redo analysis are available at GitHub https://github.com/hekselman/PrEDiCT, and Dryad https://datadryad.org/stash/share/5j6T7Duzcbyx3jNVL_irARhUphpUeW0vuYGTQHofozM .

The following previously published data sets were used

Article and author information

Author details

  1. Idan Hekselman

    Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0529-7838

  2. Assaf Vital

    Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
    Competing interests
    The authors declare that no competing interests exist.

  3. Maya Ziv-Agam

    Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
    Competing interests
    The authors declare that no competing interests exist.

  4. Lior Kerber

    Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0005-0583-5618

  5. Ido Yairi

    Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
    Competing interests
    The authors declare that no competing interests exist.

  6. Esti Yeger-Lotem

    Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
    For correspondence
    estiyl@bgu.ac.il
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8279-7898

Funding

Israel Science Foundation (317/19)

  • Esti Yeger-Lotem

Israel Science Foundation (401/22)

  • Esti Yeger-Lotem

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

Reviewing Editor

  1. Bogdan Pasaniuc

Version history

  1. Preprint posted: October 31, 2022 (view preprint)
  2. Received: November 1, 2022
  3. Accepted: December 8, 2023
  4. Accepted Manuscript published: January 10, 2024 (version 1)
  5. Version of Record published: January 31, 2024 (version 2)

Copyright

© 2024, Hekselman 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. Idan Hekselman
  2. Assaf Vital
  3. Maya Ziv-Agam
  4. Lior Kerber
  5. Ido Yairi
  6. Esti Yeger-Lotem
(2024)
Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data
eLife 13:e84613.
https://doi.org/10.7554/eLife.84613

Share this article

https://doi.org/10.7554/eLife.84613

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