GEO series

Identifying Coordinated Gene Regulatory Networks That Direct Cardiomyocyte Development

GSE245499 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 48 samples Submitted 2024/09/06 Platform GPL24676Platform GPL20795

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 48 samples

What it is: ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism: Homo sapiens (human)
Samples: 48 samples: 19 conditions, mostly in duplicateGroups 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: Single-cell multi-modal integrative analyses highlight functional dynamic gene regulatory networks directing human cardiac developmentCell genomics · 2024

Summary (written by the submitters)

Illuminating the precise stepwise genetic programs directing cardiac development provides insights into the mechanisms of congenital heart disease and strategies for cardiac regenerative therapies. Here, we integrated in vitro and in vivo human single-cell multi-omic studies with high-throughput functional genomic screening to reveal dynamic, cardiac-specific gene regulatory networks and transcriptional regulators during human cardiomyocyte development.

Read the full summary ↓

Interrogating developmental trajectories reconstructed from single-cell data unexpectedly revealed divergent cardiomyocyte lineages with distinct gene programs based on developmental signaling pathways. High-throughput functional genomic screens identified key transcription factors from inferred gene regulatory networks (GRNs) that were functionally relevant for cardiomyocyte lineages derived from each pathway. Notably, we discovered a critical HSF1-mediated cardiometabolic GRN controlling cardiac mitochondrial/metabolic function and cell survival, also observed in fetal human cardiomyocytes. Overall, these multi-modal genomic studies enabled the systematic discovery and validation of coordinated gene regulatory networks and transcriptional regulators controlling the development of distinct human cardiomyocyte populations.

Published in

Single-cell multi-modal integrative analyses highlight functional dynamic gene regulatory networks directing human cardiac development

Holman AR, Tran S, Destici E et al. · Cell genomics 2024 · PMID 39437788 · doi:10.1016/j.xgen.2024.100680

This dataset

Open GSE245499 in finder → Find more human 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 GSE245499_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1028604 and SRA study SRP466604. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39437788) ↗

Study design

19 conditions, mostly in duplicate

D0, ×4 2D D25, TNNT2-, ×3 2D D25, TNNT2+, ×3 3D D25, TNNT2-, ×3 3D D25, TNNT2+, ×3 3D, KO-1, ×3 3D, WT, ×3 3D, KO-2, ×3 +11 more

Supports a between-group comparison across 48 samples.

How we read this: 19 replicated groups read from 48 sample titles; they account for 48 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

GSM7843895 D0, rep1 D0,
GSM7843896 D0, rep2 D0,
GSM7843897 2D D25, TNNT2-, rep1 2D D25, TNNT2-,
GSM7843898 2D D25, TNNT2-, rep2 2D D25, TNNT2-,
GSM7843899 2D D25, TNNT2-, rep3 2D D25, TNNT2-,
GSM7843900 2D D25, TNNT2+, rep1 2D D25, TNNT2+,
GSM7843901 2D D25, TNNT2+, rep2 2D D25, TNNT2+,
GSM7843902 2D D25, TNNT2+, rep3 2D D25, TNNT2+,
GSM7843903 3D D25, TNNT2-, rep1 3D D25, TNNT2-,
GSM7843904 3D D25, TNNT2-, rep2 3D D25, TNNT2-,
GSM7843905 3D D25, TNNT2-, rep3 3D D25, TNNT2-,
GSM7843906 3D D25, TNNT2+, rep1 3D D25, TNNT2+,
GSM7843907 3D D25, TNNT2+, rep2 3D D25, TNNT2+,
GSM7843908 3D D25, TNNT2+, rep3 3D D25, TNNT2+,
GSM7843909 3D, KO-1, rep1 3D, KO-1,
GSM7843910 3D, KO-1, rep2 3D, KO-1,
GSM7843911 3D, KO-1, rep3 3D, KO-1,
GSM7843912 3D, WT, rep1 3D, WT,
GSM7843913 3D, WT, rep2 3D, WT,
GSM7843914 3D, WT, rep3 3D, WT,
GSM7843915 3D, KO-2, rep1 3D, KO-2,
GSM7843916 3D, KO-2, rep2 3D, KO-2,
GSM7843917 3D, KO-2, rep3 3D, KO-2,
GSM7843918 3D, KO-3, rep1 3D, KO-3,
GSM7843919 3D, KO-3, rep2 3D, KO-3,
GSM7843920 3D, KO-3, rep3 3D, KO-3,
GSM7843921 D0, rep1 D0,
GSM7843922 D0, rep2 D0,
GSM7843923 2D D2, rep1 2D D2,
GSM7843924 2D D2, rep2 2D D2,
GSM7843925 2D D5, rep1 2D D5,
GSM7843926 2D D5, rep2 2D D5,
GSM7843927 2D D7, rep1 2D D7,
GSM7843928 2D D7, rep2 2D D7,
GSM7843929 2D D15, rep1 2D D15,
GSM7843930 2D D15, rep2 2D D15,
GSM7843931 2D D25, rep1 2D D25,
GSM7843932 2D D25, rep2 2D D25,
GSM7843933 3D D3, rep1 3D D3,
GSM7843934 3D D3, rep2 3D D3,

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

GSE314625 WSTF deficiency reprograms regulatory networks by linking locus‑specific chromatin remodeling to altered isoform expression and misdirected signaling [mRNA-seq] 18 samples shares regulatory, networks, direct
GSE303901 An unbiased survey of distal element-gene regulatory interactions with direct-capture targeted Perturb-seq 97 samples shares regulatory, direct
GSE256068 Identification of gene regulatory networks affected across drug-resistant epilepsies 162 samples shares regulatory, networks
GSE319266 Identification of target genes and regulatory networks for bone mineral density GWAS loci through systematic targeting and inhibition [hFOB BMD STING-seq] 24 samples shares regulatory, networks
GSE234727 Inorganic Ions Activate Lineage-Specific Gene Regulatory Networks to Drive Stem Cells Differentiation 15 samples shares regulatory, networks
GSE299850 KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease 36 samples shares regulatory, networks
GSE299831 KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease [RNA-seq] 12 samples shares regulatory, networks
GSE288996 Simultaneous single cell CRISPR, RNA, and ATAC-seq enables multiomic CRISPR screens to identify gene regulatory relationships 15 samples shares regulatory

Search all human RNA-seq datasets in GEO →

Share this dataset

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.