A resource and computational approach for quantifying gene editing allelism at single-cell resolution

Abstract

CRISPR-Cas9-based gene editing is a powerful approach to developing gene and cell therapies for several diseases. Engineering cell therapies requires accurate assessment of gene editing allelism because editing patterns can vary across cells leading to genotypic heterogeneity. This can hinder development of robust cell therapies. Droplet-based targeted single-cell DNA sequencing (scDNAseq) has been used to genotype targeted loci across thousands of cells enabling high-throughput assessment of gene editing efficiency. Here, we constructed a "ground truth" gene editing single-cell DNAseq atlas, along with an artifact-aware computational workflow called GUMM (Genotyping Using Mixture Models) to systematically infer single-cell allelism from these data. This resource was created by expanding CRISPR-Cas9-edited HL-60 clones that harbored distinct insertion-deletion (indel) profiles in CLEC12A and mixing them at pre-defined ratios to create artificial cocktails that mimic the potential editing diversity of a CRISPR-Cas9 experiment. This enabled assessment of technical artifacts that confound interpretation of allelism in the readouts of gene edited cells. GUMM was able to accurately genotype cells and infer the original clonal composition of the artificial cocktails even in the presence of artifacts.