GEO series

Concurrent profiling of multiscale 3D genome organization and gene expression in single mammalian cells

GSE238001 Mus musculus (mouse) Single-cell RNA-seq 18 samples Submitted 2024/03/27 Platform GPL24676Platform GPL25526Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 18 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Mus musculus (mouse)
Samples: 18 samples: 18 samples, each with a distinct title — no replicated groupsGroups 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; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper: GAGE-seq concurrently profiles multiscale 3D genome organization and gene expression in single cellsNature genetics · 2024

Summary (written by the submitters)

The organization of mammalian genomes within the nucleus features a complex, multiscale three-dimensional (3D) architecture. The functional significance of these 3D genome features, however, remains largely elusive due to limited single-cell technologies that can concurrently profile genome organization and transcriptional activities.

Read the full summary ↓

Here, we report GAGE-seq, a highly scalable, robust single-cell co-assay that simultaneously measures 3D genome structure and transcriptome within the same cell. Employing GAGE-seq on mouse brain cortex and human bone marrow CD34+ cells, we comprehensively characterized the intricate relationships between 3D genome and gene expression. We found that these multiscale 3D genome features collectively inform cell type-specific gene expressions, hence contributing to defining cell identity at the single-cell level. Integration of GAGE-seq data with spatial transcriptomic data revealed in situ variations of the 3D genome in mouse cortex. Moreover, our observations of lineage commitment in normal human hematopoiesis unveiled notable discordant changes between 3D genome organization and gene expression, underscoring a complex, temporal interplay at the single-cell level that is more nuanced than previously appreciated. Together, GAGE-seq provides a powerful, cost-effective approach for interrogating genome structure and gene expression relationships at the single-cell level across diverse biological contexts.

Published in

GAGE-seq concurrently profiles multiscale 3D genome organization and gene expression in single cells

Zhou T, Zhang R, Jia D et al. · Nature genetics 2024 · PMID 38744973 · doi:10.1038/s41588-024-01745-3

This dataset

Open GSE238001 in finder → Find more RNA-seq datasets →

Download · which file do I need?

Direct links to NCBI and ENA, 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, or the whole study as GSE238001_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

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 PRJNA997359 and SRA study SRP450826. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38744973) ↗

Study design

18 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 18 sample titles: 18 distinct titles with little repetition. 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

GSM7657691 K562-NIH3T3 scRNA
GSM7657692 K562-NIH3T3 scHi-C
GSM7657693 K562-GM12878 scRNA
GSM7657694 K562-GM12878 scHi-C
GSM7657695 MDSL scRNA
GSM7657696 MDSL scHi-C
GSM7657697 mBC replicate 1 scRNA
GSM7657698 mBC replicate 1 scHi-C
GSM7657699 mBC replicate 2 scRNA
GSM7657700 mBC replicate 2 scHi-C
GSM7657701 mBC replicate 3 scRNA
GSM7657702 mBC replicate 3 scHi-C
GSM7657703 hBM replicate 1 scRNA
GSM7657704 hBM replicate 1 scHi-C
GSM7657705 hBM replicate 2 scRNA
GSM7657706 hBM replicate 2 scHi-C
GSM7657707 hBM replicate 3 scRNA
GSM7657708 hBM replicate 3 scHi-C

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Search all RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.