Precisely defining disease variant effects in CRISPR-edited single cells

• 成果类型: Article
• 署名作者: Baglaenko, Yuriy; Mu, Zepeng; Curtis, Michelle; Mire, Hafsa M.; Jayanthi, Vidyashree; Al Suqri, Majd; Liu, Cassidy; Agnew, Ryan; Nathan, Aparna; Mah-Som, Annelise Yoo; Liu, David R.; Newby, Gregory A.; Raychaudhuri, Soumya
• 署名单位: Harvard University; Harvard University Medical Affiliates; Brigham & Women's Hospital; Harvard University; Harvard University Medical Affiliates; Brigham & Women's Hospital; Harvard University; Harvard Medical School; Harvard University; Harvard University Medical Affiliates; Brigham & Women's Hospital; Harvard University; Massachusetts Institute of Technology (MIT); Broad Institute; Cincinnati Children's Hospital Medical Center; Cincinnati Children's Hospital Medical Center; University System of Ohio; University of Cincinnati; University System of Ohio; University of Cincinnati; Harvard University; Harvard Medical School; Harvard University; Massachusetts Institute of Technology (MIT); Broad Institute; Harvard University; Harvard University; Howard Hughes Medical Institute; Harvard Medical School; Johns Hopkins University; Johns Hopkins Medicine; Johns Hopkins University
• 刊物名称: NATURE
• ISSN/ISSBN: 0028-0836; 1476-4687
• DOI: 10.1038/s41586-025-09313-3
• 发表日期: 2025-10-02
• 关键词: association analyses identify; expression; RISK; loci; cd45

摘要

Genetic studies have identified thousands of individual disease-associated non-coding alleles, but the identification of the causal alleles and their functions remains a critical bottleneck(1). CRISPR-Cas editing has enabled targeted modification of DNA to introduce and test disease alleles. However, the combination of inefficient editing, heterogeneous editing outcomes in individual cells and nonspecific transcriptional changes caused by editing and culturing conditions limits the ability to detect the functional consequences of disease alleles(2,3). To overcome these challenges, we present a multi-omic single-cell sequencing approach that directly identifies genomic DNA edits, assays the transcriptome and measures cell-surface protein expression. We apply this approach to investigate the effects of gene disruption, deletions in regulatory regions, non-coding single-nucleotide polymorphism alleles and multiplexed editing. We identify the effects of individual single-nucleotide polymorphisms, including the state-specific effects of an IL2RA autoimmune variant in primary human T cells. Multimodal functional genomic single-cell assays, including DNA sequencing, enable the identification of causal variation in primary human cells and bridge a crucial gap in our understanding of complex human diseases.