State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China.
Ludwig Institute for Cancer Research, La Jolla, CA, USA.
Nat Methods. 2021 Sep;18(9):1056-1059. doi: 10.1038/s41592-021-01231-2. Epub 2021 Aug 26.
Single-cell Hi-C (scHi-C) analysis has been increasingly used to map chromatin architecture in diverse tissue contexts, but computational tools to define chromatin loops at high resolution from scHi-C data are still lacking. Here, we describe Single-Nucleus Analysis Pipeline for Hi-C (SnapHiC), a method that can identify chromatin loops at high resolution and accuracy from scHi-C data. Using scHi-C data from 742 mouse embryonic stem cells, we benchmark SnapHiC against a number of computational tools developed for mapping chromatin loops and interactions from bulk Hi-C. We further demonstrate its use by analyzing single-nucleus methyl-3C-seq data from 2,869 human prefrontal cortical cells, which uncovers cell type-specific chromatin loops and predicts putative target genes for noncoding sequence variants associated with neuropsychiatric disorders. Our results indicate that SnapHiC could facilitate the analysis of cell type-specific chromatin architecture and gene regulatory programs in complex tissues.
单细胞 Hi-C(scHi-C)分析已越来越多地用于在不同组织环境中绘制染色质结构,但仍缺乏从 scHi-C 数据中以高分辨率定义染色质环的计算工具。在这里,我们描述了用于 Hi-C 的单核分析管道(SnapHiC),这是一种可以从 scHi-C 数据中以高分辨率和准确性识别染色质环的方法。使用来自 742 个小鼠胚胎干细胞的 scHi-C 数据,我们将 SnapHiC 与为从批量 Hi-C 中映射染色质环和相互作用而开发的许多计算工具进行了基准测试。我们进一步通过分析来自 2869 个人类前额叶皮层细胞的单核甲基化 3C-seq 数据来证明其用途,该数据揭示了细胞类型特异性染色质环,并预测了与神经精神障碍相关的非编码序列变异的潜在靶基因。我们的结果表明,SnapHiC 可以促进对复杂组织中细胞类型特异性染色质结构和基因调控程序的分析。