GiRAFR improves gRNA detection and annotation in single-cell CRISPR screens.
Qian YuPaulien Van MinselEva GalleBernard ThienpontPublished in: Communications biology (2023)
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.
Keyphrases
- single cell
- rna seq
- genome wide
- high throughput
- dna methylation
- genome editing
- crispr cas
- induced apoptosis
- copy number
- wild type
- cell cycle arrest
- gene expression
- cell death
- signaling pathway
- oxidative stress
- endoplasmic reticulum stress
- transcription factor
- climate change
- quality improvement
- label free
- data analysis
- real time pcr