美国哈佛医学院Soumya Raychaudhuri课题组发现，来自多模态单细胞数据的组织特异性增强子-基因图谱识别致病等位基因。2024年4月9日，《自然—遗传学》杂志在线发表了这项成果。

研究人员开发了一种非参数统计方法SCENT（单细胞增强子目标基因图谱），它能在单细胞或细胞核多模态RNA测序和ATAC测序数据中，模拟增强子染色质可及性与基因表达之间的关联。研究人员将SCENT应用于9个多模态数据集，其中包括超过120000个单细胞或细胞核，并绘制了23个细胞类型特异性增强子基因图谱。

这些图谱高度富集了1143种疾病和性状的表达定量位点和全基因组关联研究（GWAS）的因果变异。研究人员确定了常见病和罕见病的可能致病基因，并将体细胞突变热点与目标基因联系起来。研究人员证明，将SCENT应用于与疾病相关的人体组织的多模态数据，能以可扩展的方式构建精确的细胞类型特异性增强子基因图谱，这对确定非编码变异的功能至关重要。

据介绍，要将GWAS的基因位点转化为因果变异和基因，需要从疾病相关组织中获得准确的细胞类型特异性增强子基因图谱。绘制增强子基因图谱至关重要，但目前在原代人体组织中采用的实验方法却极具挑战性。

附：英文原文

Title: Tissue-specific enhancer–gene maps from multimodal single-cell data identify causal disease alleles

Author: Sakaue, Saori, Weinand, Kathryn, Isaac, Shakson, Dey, Kushal K., Jagadeesh, Karthik, Kanai, Masahiro, Watts, Gerald F. M., Zhu, Zhu, Brenner, Michael B., McDavid, Andrew, Donlin, Laura T., Wei, Kevin, Price, Alkes L., Raychaudhuri, Soumya

Issue&Volume: 2024-04-09

Abstract: Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer–gene maps from disease-relevant tissues. Building enhancer–gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer–gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer–gene maps, essential for defining noncoding variant function.

DOI: 10.1038/s41588-024-01682-1

Source: https://www.nature.com/articles/s41588-024-01682-1