GiRAFR improves gRNA detection and annotation in single-cell CRISPR screens

Abstract

Novel methods that combine single cell RNA-seq with CRISPR screens enable high-throughput characterization of transcriptional changes caused by genetic perturbations. Dedicated software is however lacking to annotate CRISPR guide RNA (gRNA) libraries and associate them with single cell transcriptomes. Here, we describe a CRISPR droplet sequencing (CROP-seq) dataset. During analysis, we observed that the most commonly used method fails to detect mutant gRNAs. We therefore developed a python tool to identify and characterize intact and mutant gRNAs, called GiRAFR. We show that mutant gRNAs are dysfunctional, and failure to detect and annotate them leads to an inflated estimate of the number of untransformed cells, attenuated downregulation of target genes, as well as an underestimated multiplet frequency. These findings are mirrored in publicly available datasets, where we find that up to 35% of cells are transduced with a mutant gRNA. Applying GiRAFR hence stands to improve the annotation and quality of single cell CRISPR screens.

Guide assignment in single-cell CRISPR screens using crispat

“…Since all assignment methods in crispat are based on the gRNA-cell count matrix, choosing a suitable alignment strategy is crucial. For example, it has been shown previously that mutations can arise in gRNAs which can impair downstream analyses 19 . Alignment strategies that account for such mutations could serve as alternative input to crispat.…”