GEO series

Chromatin Remodeling Drives Immune-Fibroblast Cell Communication in Heart Failure (scRNA-Seq)

GSE221698 Mus musculus (mouse) Single-cell RNA-seq 22 samples Submitted 2024/07/03 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 22 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 22 samples: 9 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.

Summary (written by the submitters)

Chronic inflammation and tissue fibrosis are common stress responses that worsen organ function, yet their crosstalk is poorly understood. Here, we show that conditional deletion of the transcription co-activator Brd4 in Cx3cr1-positive myeloid cells ameliorates heart failure and is associated with a dramatic reduction in fibroblast activation.

Read the full summary ↓

We identified a specific BRD4-occupied enhancer in Cx3cr1-positive cells that controls expression of Interleukin-1 beta (Il1b), and show that secreted IL1B activates a p65/RELA-dependent enhancer downstream of MEOX1, driving a profibrotic response in human cardiac fibroblasts. In vivo, antibody-mediated IL1B neutralization prevented stress-induced expression of MEOX1, inhibited fibroblast activation, and improved cardiac function in heart failure. The elucidation of BRD4-dependent crosstalk between a specific immune cell subset and fibroblasts through IL1B provides new therapeutic strategies for heart disease and other disorders of chronic inflammation and maladaptive tissue remodeling.

This dataset

Open GSE221698 in finder → Find more mouse 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 GSE221698_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 PRJNA915389 and SRA study SRP414685. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

9 conditions, mostly in duplicate

scRNA-Seq Sham ×3 scRNA-Seq TAC ×3 scRNA-Seq TAC_JQ1 ×3 scRNA-Seq CD45pos_TAC_WT ×2 scRNA-Seq CD45pos_TAC_BRD4KO ×2 scRNA-Seq TAC_WT ×2 scRNA-Seq TAC_BRD4KO ×2 scRNA-Seq TAC_IgG_Ab ×2 +2 more

Supports a between-group comparison across 21 samples.

How we read this: 9 replicated groups read from 22 sample titles; they account for 21 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

GSM6893256 scRNA-Seq Sham_rep1 scRNA-Seq Sham
GSM6893257 scRNA-Seq Sham_rep2 scRNA-Seq Sham
GSM6893258 scRNA-Seq Sham_rep3 scRNA-Seq Sham
GSM6893259 scRNA-Seq TAC_rep1 scRNA-Seq TAC
GSM6893260 scRNA-Seq TAC_rep2 scRNA-Seq TAC
GSM6893261 scRNA-Seq TAC_rep3 scRNA-Seq TAC
GSM6893262 scRNA-Seq TAC_JQ1_rep1 scRNA-Seq TAC_JQ1
GSM6893263 scRNA-Seq TAC_JQ1_rep2 scRNA-Seq TAC_JQ1
GSM6893264 scRNA-Seq TAC_JQ1_rep3 scRNA-Seq TAC_JQ1
GSM6893265 scRNA-Seq CD45+ Sham
GSM6893266 scRNA-Seq CD45pos_TAC_WT_rep1 scRNA-Seq CD45pos_TAC_WT
GSM6893267 scRNA-Seq CD45pos_TAC_WT_rep2 scRNA-Seq CD45pos_TAC_WT
GSM6893268 scRNA-Seq CD45pos_TAC_BRD4KO_rep1 scRNA-Seq CD45pos_TAC_B…
GSM6893269 scRNA-Seq CD45pos_TAC_BRD4KO_rep2 scRNA-Seq CD45pos_TAC_B…
GSM6893270 scRNA-Seq TAC_WT_rep1 scRNA-Seq TAC_WT
GSM6893271 scRNA-Seq TAC_WT_2 scRNA-Seq TAC_WT
GSM6893272 scRNA-Seq TAC_BRD4KO_rep1 scRNA-Seq TAC_BRD4KO
GSM6893273 scRNA-Seq TAC_BRD4KO_rep2 scRNA-Seq TAC_BRD4KO
GSM6893274 scRNA-Seq TAC_IgG_Ab1 scRNA-Seq TAC_IgG_Ab
GSM6893275 scRNA-Seq TAC_IgG_Ab2 scRNA-Seq TAC_IgG_Ab
GSM6893276 scRNA-Seq TAC_antiIL1B_Ab1 scRNA-Seq TAC_antiIL1B_…
GSM6893277 scRNA-Seq TAC_antiIL1B_Ab2 scRNA-Seq TAC_antiIL1B_…

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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.