Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants [RNAseq_mutants]

Predicting the pathogenic effect of rare genetic variants is hampered by the limited cohort of diagnosed patients and the difficulty in evaluating the effect of existing and novel variants. To address this issue, we leveraged a protein Multiplexed Assay of Variants Effect (MAVE) to functionally assess the effect of ~2,300 missense variants of the TP63 gene, some of which cause autosomal dominant developmental disorders. The activity of each variant was measured using an optimized fibroblast-to-keratinocyte conversion protocol, and a subset of mutants was validated using a wide range of functional tests. To expand MAVEs to any disease-driving gene without a specific predefined assay, we developed SCRAMseq (Single Cell RNAseq Associated with MAVEs by sequencing), which can retrieve each variant and characterize the functional consequences at the single-cell level through full-length scRNAseq. The dataset generated and validated here reclassified hundreds of variants present in the general population and definitively classified a Variant of Unknown Significance as pathogenic in a patient with ectodermal dysplasia. This work provides a robust and easy-to-use workflow to dissect the effect of gene variants for any possible disease-driving gene.