Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data.
Idan HekselmanAssaf VitalMaya Ziv-AgamLior KerberIdo YairiEsti Yeger-LotemPublished in: eLife (2024)
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.
Keyphrases
- single cell
- rna seq
- cell therapy
- gene expression
- systematic review
- endothelial cells
- high throughput
- stem cells
- cell proliferation
- climate change
- binding protein
- machine learning
- dna methylation
- artificial intelligence
- copy number
- big data
- deep learning
- genome wide identification
- smoking cessation
- induced pluripotent stem cells