ENAGenomicsDiao Lab, Cell Biology, Duke Universityhttps://www.ebi.ac.uk/ena/browser/view/PRJNA1109789The three-dimensional (3D) organization of cis-regulatory elements (CREs) plays a central role in transcription control. However, capturing transcriptome, epigenome, and 3D genome from the same single cells remains technically challenging. Here, we present scHiCAR (single-cell Hi-C with ATAC and RNA-seq), a combinatorial barcoding-based method that simultaneously profiles mRNA, open chromatin, and chromosome-conformation-capture from the same cells. Compared to existing single-cell 3D genome methods, scHiCAR more efficiently enriches long-range cis-interactions anchored at candidate CREs (cCREs). Applied to 1.62 million mouse brain cells and complemented with a deep learning-based loop caller, scHiCAR accurately defines cell-type-specific transcriptomes, accessible cCREs, and 5kb-resolution enhancer-promoter pairs across 22 brain cell types. scHiCAR also performs robustly in challenging tissues such as skeletal muscle, enabling tri-modal single-cell level analysis of gene regulation dynamics during muscle stem cell regeneration. scHiCAR offers a scalable, efficient, and cost-effective platform for studying gene regulatory landscapes in complex tissues at single-cell resolution. Overall design: We developed a new method, scHiCAR, which enables high-throughput detection of the transcriptome, epigenome, and 3D genome at single-cell resolution. We validated its performance in three cell lines: human H1 and GM12878, and mouse embryonic stem cells (mESCs).ENAfalsescHiCAR: a tri-modal single-cell genomics technology for integrated transcriptome, epigenome, and 3D genome analysis in complex tissues [cellline_scHiCAR]scHiCAR: a tri-modal single-cell genomics technology for integrated transcriptome, epigenome, and 3D genome analysis in complex tissues [cellline_scHiCAR]2025-09-242025-09-08PRJNA1109789GSE267117