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Pericardial Fluid of Patients with Coronary Artery Disease Can Drive Fibrosis via TGF-Beta Signalling Pathway

GSE262161 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2024/12/06 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 24 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
24 samples: 17 distinct titles, 1 of them repeatedGroups 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
Pericardial Fluid of Patients With Coronary Artery Disease Can Drive Fibrosis Via TGF-Beta PathwayJACC. Basic to translational science · 2024
Summary (written by the submitters)
Pericardial fluid (PF) helps maintain homeostatic conditions that facilitate optimal heart function. We have yet to understand how cardiovascular pathologies affect the profile of PF. We hypothesized that human PF contains immune markers that can be affected by different pathologies and contribute to inflammatory-mediated processes, such as fibrosis.
Read the full summary ↓
PF was collected from patients undergoing cardiac surgery. Multiplex analysis was performed to determine the composition of inflammatory markers and soluble mediators. A gel contraction assay was used to investigate the fibrotic potential of PF. RNA-sequencing was used to elucidate the mechanisms driving the pro-fibrotic properties of PF. With the results of these multiple approaches we show that the acellular component of PF has pro-fibrotic characteristics and suggest putative mechanisms that can explain such observations.
Published in
Pericardial Fluid of Patients With Coronary Artery Disease Can Drive Fibrosis Via TGF-Beta Pathway
Fatehi Hassanabad A, Belke DD, Gordon PMK et al. · JACC. Basic to translational science 2024 · PMID 39619141 · doi:10.1016/j.jacbts.2024.06.007
This dataset
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.

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 GSE262161_RAW.tar (it can run to tens of gigabytes).

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.

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1090494 and SRA study SRP497041. Searching any of these in the dataset finder brings you back here.

Study design
17 distinct titles, 1 of them repeated
Myocardian fibroblast, cultured, subject tp ×8

How we read this: Read from 24 sample titles: 17 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
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