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绘制不同组织环境中的染色质结构，但仍然缺乏从 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 (SnapHiC) 的单核分析管道，这是一种可以从 scHi-C 数据中以高分辨率和准确度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 与许多用于绘制染色质环和大量 Hi-C 相互作用的计算工具进行基准测试。我们通过分析来自 2,869 个人类前额叶皮层细胞的单核甲基 3C-seq 数据进一步证明了它的用途，这些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疾病相关的非编码序列变体的推定靶基因。