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scHiCAR: a tri-modal single-cell genomics technology for integrated transcriptome, epigenome, and 3D genome analysis in complex tissues [cellline_scHiCAR]

GSE267117 Homo sapiens (human) Single-cell RNA-seq 34 samples Submitted 2025/08/30 Platform GPL31277
At a glance

Single-cell RNA-seq · human · 34 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
34 samples: 32 × human H1, GM12878 and mouse ESC, DNA, batch1, vs 2 × human H1, GM12878 and mouse ESC, RNA, batch2,Groups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI.
Summary (written by the submitters)
The 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.
Read the full summary ↓
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.
This dataset
Open GSE267117 in finder → Find more datasets →
Download · which file do I need?

Direct links to NCBI, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need.

All supplementary files ↗
3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE267117_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

Whole study · RAW.tar ↓

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1109789. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
32 × human H1, GM12878 and mouse ESC, DNA, batch1, vs 2 × human H1, GM12878 and mouse ESC, RNA, batch2,

Supports a between-group comparison across 34 samples.

How we read this: 2 replicated groups read from 34 sample titles; they account for 34 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
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