Abstract
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.
Highlights
Single-cell Hi-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
We developed Single-Nucleus Analysis Pipeline for Hi-C (SnapHiC), a computational framework customized for scHi-C data to identify chromatin loops at high resolution and accuracy from a small number of cells
We found that most chromatin loops were cell type-specific (Supplementary Table 4), and the anchors of cell type-specific loops showed significantly higher ATAC-seq and H3K27ac ChIP–seq signals in the matched cell type compared to those in different cell types (Fig. 2b)
Summary
Percentage of cells with significantly higher normalized contact frequency (% of outlier cells). Results on different numbers of mES cells demonstrated that SnapHiC consistently identified more loops and achieved greater F1 scores than the other methods, with higher recall rates and equivalent or slightly lower precision rates (Extended Data Fig. 5). The genes whose promoters linked to cell type-specific loops showed significantly higher expression levels in the matched cell type (Fig. 2b and Supplementary Table 5). They were associated with gene ontology terms[19] related to cell type-specific biological processes (Extended Data Fig. 10a). Application of SnapHiC to sn-m3C-seq data from human prefrontal cortical cells reveals cell type-specific loops, which can predict putative target genes of noncoding GWAS SNPs. SnapHiC has the potential to facilitate the study of cell type-specific chromatin spatial organization in complex tissues. Received: 16 December 2020; Accepted: 30 June 2021; Published online: 26 August 2021
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