GiRAFR Improves GRNA Detection and Annotation in Single-cell CRISPR Screens

Overview

Journal Commun Biol

Specialty Biology

Date 2023 Sep 23

PMID 37741886

Authors

Qian Yu

Paulien Van Minsel

Eva Galle

Bernard Thienpont

Affiliations

Soon will be listed here.

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.

Citing Articles

Guide assignment in single-cell CRISPR screens using crispat.

Braunger J, Velten B Bioinformatics. 2024; 40(9).

PMID: 39240651 PMC: 11399228. DOI: 10.1093/bioinformatics/btae535.

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