Joint reconstruction of cis-regulatory interaction networks across multiple tissues using single-cell chromatin accessibility data

Abstract

The rapid accumulation of single-cell chromatin accessibility data offers a unique opportunity to investigate common and specific regulatory mechanisms across different cell types. However, existing methods for cis-regulatory network reconstruction using single-cell chromatin accessibility data were only designed for cells belonging to one cell type, and resulting networks may be incomparable directly due to diverse cell numbers of different cell types. Here, we adopt a computational method to jointly reconstruct cis-regulatory interaction maps (JRIM) of multiple cell populations based on patterns of co-accessibility in single-cell data. We applied JRIM to explore common and specific regulatory interactions across multiple tissues from single-cell ATAC-seq dataset containing ~80,000 cells across 13 mouse tissues. Reconstructed common interactions among 13 tissues indeed relate to basic biological functions, and individual cis-regulatory network shows strong tissue specificity and functional relevance. More importantly, tissue-specific regulatory interactions are mediated by coordination of histone modifications and tissue related TFs, and many of them reveal novel regulatory mechanisms (e.g., a kidney-specific promoter-enhancer loop of clock-controlled gene Gys2). Cis-regulatory elements (CREs) are a key class of regulatory DNA sequences and typically regulate the transcription of target genes by binding to transcription factors (TFs) (1,2). Lots of efforts have been made to characterize combinatorial patterns and systematically define CREs (3-6). Furthermore, interactions between CREs are vital components of genetic regulatory networks, controlling cell type-specific biological processes (7-9). However, linking CREs to their target genes is still a challenging problem due to their distal distance (in some cases hundreds of kilobases) and complicated regulatory mechanisms. Moreover, direct DNA contacts could be inferred using computational methods (e.g., CHiCAGO (10)) from data of the chromosome conformation capture (3C) technique and its variants such as ChIA-PET (11), Hi-C (12) and capture Hi-C (13).But thus far these types of data are only available for a few cell types. As a result, several computational methods have been developed to estimate cis-regulatory DNA interactions using epigenetic data (14-18). For example, Corces et al. (14) proposed a computational strategy based on the correlation of transcriptional expression and chromatin accessibility to identify promoter-enhancer interactions. Cao et al. (15) adopted a random forest model to reconstruct enhancer-target networks using both histone modification data and chromatin accessibility data. However, the above methods were all designed to infer CREs interactions from bulk sequencing data. The limited number of samples per cell type make it hard to generate robust genome-wide cis-regulatory interaction maps.Fortunately, the advent of single-cell ATAC-seq technology has enabled the genome-wide profiling of chromatin accessibility at single-cell resolut...

simCAS: an embedding-based method for simulating single-cell chromatin accessibility sequencing data

“…scCAS data provides a perspective to investigate the interactions of chromatin accessible sites, and several computational methods (Dong and Zhang, 2021;Li, et al, 2020), such as Cicero (Pliner, et al, 2018), have been proposed to predict the interactions between peaks, namely cis-regulatory interactions. However, in the absence of fine-grained labels which contain correlations among all the chromatin sites, quantitative evaluation of the corresponding methods presents a unique challenge.…”

A Systematic Evaluation of the Computational Tools for Ligand-receptor-based Cell-Cell Interaction Inference

“…Cell-Cell interactions (CCIs) are essential for multicellular organisms to develop tissue structure and regulate individual cell processes [1][2][3][4]. They also contribute to maintaining intercellular relationships and coordinating diverse biological processes, such as development, differentiation and inflammation [5][6][7].…”