A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases

Identifying biomarkers and therapeutically actionable targets for complex diseases is of critical importance, yet is complicated by the involvement of thousands of variably expressed genes across multiple cell types. The emergence of single-cell RNA-sequencing (scRNA-seq) now enables comprehensive characterization of these changes, but clinical translation of this data remains a challenge. Here, we construct network models of disease-associated cell types and interactions from scRNA-Seq data which we term Multicellular Disease Models (MCDMs). Using MCDMs derived from joints and adjacent lymph nodes in a murine model of arthritis (RA), we identify nine cell types that are highly interconnected and enriched for multiple diverse pathways associated with RA. We find that the network centrality of MCDM cell types correlates with the enrichment of GWAS hits (Pearson r = 0.85, p = 3.0 × 10 -2 ), and thus could potentially be used to prioritize cell types and genes for therapeutics and diagnostics. We validate this hypothesis via a therapeutic study of a candidate drug in the mouse disease model of RA. However, to show general applicability, we successfully extend our framework to a large-scale diagnostic study of patients with 13 different autoimmune, allergic, infectious, malignant, endocrine, metabolic, and cardiovascular diseases. Overall, our results show that MCDM s have the potential to help identify therapeutically actionable hubs for human disease.