Gene regulatory landscape dissected by single-cell four-omics sequencing | Nature
Subjects
- Epigenomics
- Gene regulation
Abstract
Cellular diversity is governed not only by the transcriptome but also by multiple layers of epigenomic regulation, including nucleosome occupancy, chromatin states and genome architecture1,2,3. Here, to comprehensively understand how these regulatory modalities converge to shape cellular identity, we developed a single-cell four-omics sequencing method that enables parallel profiling of genome conformation, histone modifications, chromatin accessibility and gene expression within the same cell (CHARM). Applying CHARM to mouse embryonic stem cells and cortical tissues, we reconstructed integrated epigenome profiles, uncovering distinct cell-cycle dynamics of chromatin accessibility and histone modification, and spatial clustering of regulatory elements in three-dimensional nuclear space. Leveraging an interpretable machine learning model, we further identified thousands of enhancer–promoter linkages with high accuracy that modulate gene expression in a cell-type- and subtype-specific manner. Together, CHARM enables integrative dissection of the three-dimensional epigenome at single-cell resolution, providing a versatile platform for decoding the regulatory landscape across diverse cells in complex tissues.
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Fig. 1: Design and validation of CHARM.
Fig. 2: Chromatin accessibility and H3K27me3 exhibit distinct dynamics across the cell cycle.
Fig. 3: Spatial analysis of accessible chromatin, histone modification and gene expression in single cells.
Fig. 4: Single-cell multi-omic analysis reveals coordination between epigenome and transcriptome in the mouse brain.
Fig. 5: Cell-type-specific enhancers modulate transcriptional output.
Data availability
Raw sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession PRJNA1284811. Processed and analysed datasets have been deposited in the NCBI Gene Expression Omnibus (GEO) under accession GSE303006. The mouse reference genome assembly and gene annotation used in this study were GRCm38 with GENCODE release M23 (https://www.gencodegenes.org/mouse/release_M23.html). Phased mouse SNPs were obtained from the Sanger Institute Mouse Genomes Project (https://ftp.ebi.ac.uk/pub/databases/mousegenomes/REL-1505-SNPs_Indels/mgp.v5.merged.indels.dbSNP142.normed.vcf.gz). The mouse brain single-cell ATAC–seq data were obtained from the CELLxGENE data portal (https://cellxgene.cziscience.com/collections/5e469121-c203-4775-962d-dcf2e5d6a472). The mouse brain cortex single-cell RNA atlas were obtained from Allen Institute (https://idk-etl-prod-download-bucket.s3.amazonaws.com/aibs_mouse_ctx-hpf_smart-seq/Seurat.ss.rda). The mESC droplet-based paired-tag processed H3K27ac data were obtained from GEO (GSE224560). Human genetic variants associated with intelligence were obtained from the NHGRI-EBI GWAS Catalog (trait accession EFO_0004337).
Code availability
Code for data preprocessing and analysis is available at https://github.com/skelviper/CHARM. Code and 3D-printable models for the automated liquid-handling workflow used during library preparation are available at https://github.com/skelviper/OT2.
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