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

Hereditary diseases manifest clinically in certain tissues, however 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 1,459 hereditary diseases. Overall, we identified 114 cell types affected by 1,140 diseases. We corroborated our findings by literature text-mining and recapitulation in mouse corresponding tissues. Based on these findings, we explored features of disease-affected cell types and cell classes, highlighted cell types affected by mitochondrial diseases and heritable cancers, and identified diseases that perturb intercellular communication. This study expands our understanding of disease mechan..., , , # Title of Dataset: Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data

This README file was generated on January 26, 2024 by Idan Hekselman.

[Access this dataset on Dryad]([doi:10.5061/dryad.9w0vt4bm7]\(https://doi.org/10.5061/dryad.9w0vt4bm7\))

This dataset accompanies this [GitHub repository](https://github.com/hekselman/PrEDiCT), which includes codes to redo analyses applied in [Hekselman et al. 2024](https://doi.org/10.7554/eLife.84613). The dataset includes files, each of which is a Seurat object in R. Object name indicates the name of the tissue (bone marrow, lung, skeletal muscle, spleen, trachea, and tongue) and the species it includes (Human is not mentioned, and mouse is mentioned). For further information on data and relevant codes, please refer to [Hekselman et al. 2022](https://github.com/hekselman/PrEDiCT) .

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