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Technological Convergence: Highlighting the Power of CRISPR Single-Cell Perturbation Toolkit for Functional Interrogation of Enhancers

Overview
Journal Cancers (Basel)
Publisher MDPI
Specialty Oncology
Date 2023 Jul 29
PMID 37509229
Authors
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Abstract

Higher eukaryotic enhancers, as a major class of regulatory elements, play a crucial role in the regulation of gene expression. Over the last decade, the development of sequencing technologies has flooded researchers with transcriptome-phenotype data alongside emerging candidate regulatory elements. Since most methods can only provide hints about enhancer function, there have been attempts to develop experimental and computational approaches that can bridge the gap in the causal relationship between regulatory regions and phenotypes. The coupling of two state-of-the-art technologies, also referred to as crisprQTL, has emerged as a promising high-throughput toolkit for addressing this question. This review provides an overview of the importance of studying enhancers, the core molecular foundation of crisprQTL, and recent studies utilizing crisprQTL to interrogate enhancer-phenotype correlations. Additionally, we discuss computational methods currently employed for crisprQTL data analysis. We conclude by pointing out common challenges, making recommendations, and looking at future prospects, with the aim of providing researchers with an overview of crisprQTL as an important toolkit for studying enhancers.

References
1.
Kim H, Kim Y, Lee S, Min S, Bae J, Choi J . SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance. Sci Adv. 2019; 5(11):eaax9249. PMC: 6834390. DOI: 10.1126/sciadv.aax9249. View

2.
Chidley C, Darnell A, Gaudio B, Lien E, Barbeau A, Vander Heiden M . A CRISPRi/a screening platform to study cellular nutrient transport in diverse microenvironments. Nat Cell Biol. 2024; 26(5):825-838. PMC: 11098743. DOI: 10.1038/s41556-024-01402-1. View

3.
Replogle J, Norman T, Xu A, Hussmann J, Chen J, Cogan J . Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing. Nat Biotechnol. 2020; 38(8):954-961. PMC: 7416462. DOI: 10.1038/s41587-020-0470-y. View

4.
Doench J, Fusi N, Sullender M, Hegde M, Vaimberg E, Donovan K . Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol. 2016; 34(2):184-191. PMC: 4744125. DOI: 10.1038/nbt.3437. View

5.
Wessels H, Mendez-Mancilla A, Hao Y, Papalexi E, Mauck 3rd W, Lu L . Efficient combinatorial targeting of RNA transcripts in single cells with Cas13 RNA Perturb-seq. Nat Methods. 2022; 20(1):86-94. PMC: 10030154. DOI: 10.1038/s41592-022-01705-x. View