Single-cell analysis of a mutant library generated using CRISPR-guided deaminase in human melanoma cells

Abstract

CRISPR-based screening methods using single-cell RNA sequencing (scRNA-seq) technology enable comprehensive profiling of gene perturbations from knockout mutations. However, evaluating substitution mutations using scRNA-seq is currently limited. We combined CRISPR RNA-guided deaminase and scRNA-seq technology to develop a platform for introducing mutations in multiple genes and assessing the mutation-associated signatures. Using this platform, we generated a library consisting of 420 sgRNAs, performed sgRNA tracking analysis, and assessed the effect size of the response to vemurafenib in the human melanoma cell line, which has been well-studied via knockout-based drop-out screens. However, a substitution mutation library screen has not been applied and transcriptional information for mechanisms of action was not assessed. Our platform permits discrimination of several candidate mutations that function differently from other mutations by integrating sgRNA candidates and gene expression readout. We anticipate that our platform will enable highthroughput analyses of the mechanisms related to a variety of biological events.

Robust gene expression programs underlie recurrent cell states and phenotype switching in melanoma

“…Also in pancreatic cancer, a subset of antigenpresenting ductal cells was recently detected, expressing major histocompatibility complex class II genes and complement pathway components 61 . And finally, gene sets related to immune responses were observed as marker genes for BRAFi-resistant melanoma cells in a functional genomics screen 62 . Altogether, this makes these specific transcription factors attractive candidates for targeted melanoma therapy, or cancer in general.…”

Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation

“…Although it is possible to perform multiplexed assays on multiple genes in a single experiment (Despre ´s et al, 2020;Jun et al, 2020;Hanna et al, 2021;Cuella-Martin et al, 2021), we are not yet able to probe all possible variants in all proteins by experiments. Thus, computational methods are important to predict and understand variant effects, and in some cases they may be even be more accurate than MAVEs for this purpose (Jepsen et al, 2020;Frazer et al, 2021).…”

Predicting and interpreting large scale mutagenesis data using analyses of protein stability and conservation

“…Although it is possible to perform multiplexed assays on multiple genes in a single experiment ( Després et al, 2020; Jun et al, 2020; Hanna et al, 2021; Cuella-Martin et al, 2021 ), we are still far from able to probe all possible variants in all proteins by experiments. Thus, computational methods are important to predict and understand variant effects, and in some cases they may be even be more accurate than MAVEs for this purpose ( Jepsen et al, 2020; Frazer et al, 2020 ).…”