Discovering cell types underlying rare disease phenotypes using scRNA-seq data from non-diseased tissues

Abstract

Despite their low individual prevalence, rare diseases collectively pose a significant health burden, affecting millions of people worldwide. These conditions often result from single-gene mutations, yet the cellular contexts in which these alterations act remain largely unknown--information crucial for improving diagnosis and treatment. As patient-derived samples are scarce, we use single-cell RNA sequencing (scRNA-seq) data from non-diseased tissues to identify relevant cell populations. We introduce Cell4Rare, a computational framework that integrates these healthy scRNA-seq datasets with known phenotype-associated genes to map disease phenotypes to specific cell types. Applied across diverse tissues and phenotypes, Cell4Rare was validated against literature-based associations, achieving robust performance with an AUC of 0.71. These results highlight the potential of computational analyses of non-diseased scRNA-seq data to uncover the cellular basis of rare disease phenotypes, paving the way for improved diagnostics and therapeutic strategies.