Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data
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 .
Article and author information
Author details
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.
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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