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Whole-transcriptome analysis illustrates evolving transcriptional human response to injury in acute wounds and scars

GSE178411 Homo sapiens (human) RNA-seq (gene expression) 108 samples Submitted 2024/06/03 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 108 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: 108 samples: 97 distinct titles, 9 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.

Summary (written by the submitters)

The goal of this study was to use RNA sequencing to identify novel genes involved in wound healing and scar formation in human burn wounds and scars. RNA was isolated from 108 human samples comprising of uninjured skin (n=26), acute burn wounds (n=54), and hypertrophic scars (HTS) (n=30). Genes with at least 1.5-fold change and a p-value less than 0.05 with a false discovery rate of 0.05 were considered differentially expressed.

Read the full summary ↓

Samples were sequenced using Illumina Hi-Seq sequencers, and pathway/Gene Ontology (GO) Enrichment analysis was conducted using iPathwayGuide. Comparing wounds to uninjured skin, we found 9,311 differentially expressed genes, accounting for 1,017 GO terms. Comparing HTS to uninjured skin, we found 7,299 differentially expressed genes, accounting for 1,022 GO terms. By analyzing the whole transcriptome, we characterized dominant genes expressed temporally in wounds and scars. This study reveals the complexity of these processes and helps to bring novel genes to light.

This dataset

Open GSE178411 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 GSE178411_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: 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 PRJNA738896 and SRA study SRP324508. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

97 distinct titles, 9 of them repeated

Patient 42 Late wound ×3 Patient 105 HTS ×3 Patient 30 Late wound ×2 Patient 32 Late wound ×2 Patient 45 Early Wound ×2 Patient 55 Early Wound ×2 Patient 61 Early Wound ×2 Patient 76 Late wound ×2 +1 more

How we read this: Read from 108 sample titles: 97 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

GSM5390523 12-W: Patient 12 Early Wound
GSM5390524 13-W: Patient 13 Late wound
GSM5390525 14-S: Patient 14 Normal scar
GSM5390526 16-D: Patient 15 Normal skin
GSM5390527 17-S: Patient 16 Normal scar
GSM5390528 18-S: Patient 17 HTS
GSM5390529 30-W1: Patient 29 Early Wound
GSM5390530 30-W4: Patient 29 Late wound
GSM5390531 31-D: Patient 30 Normal skin
GSM5390532 31-W1: Patient 30 Late wound
GSM5390533 31-W2: Patient 30 Late wound
GSM5390534 32-D: Patient 31 Normal skin
GSM5390535 33-D: Patient 32 Normal skin
GSM5390536 33-W1: Patient 32 Late wound
GSM5390537 33-WE1: Patient 32 Late wound
GSM5390538 34-D: Patient 33 Normal skin
GSM5390539 35-D: Patient 34 Normal skin
GSM5390540 36-S: Patient 35 HTS
GSM5390541 37-W: Patient 36 Late wound
GSM5390542 38-D: Patient 37 Normal skin
GSM5390543 38-WE: Patient 37 Early Wound
GSM5390544 40-WE: Patient 39 Late wound
GSM5390545 41-D: Patient 40 Normal skin
GSM5390546 41-WE: Patient 40 Early Wound
GSM5390547 42-WE: Patient 41 Early Wound
GSM5390548 43-D: Patient 42 Normal skin
GSM5390549 43-W2: Patient 42 Late wound
GSM5390550 43-WE: Patient 42 Late wound
GSM5390551 43-WE2: Patient 42 Late wound
GSM5390552 44-D: Patient 43 Normal skin
GSM5390553 44-WE: Patient 43 Late wound
GSM5390554 45-S: Patient 44 HTS
GSM5390555 45-W: Patient 44 Chronic wound
GSM5390556 46-D: Patient 45 Normal skin
GSM5390557 46-WE: Patient 45 Early Wound
GSM5390558 46-WE2: Patient 45 Early Wound
GSM5390559 47-S: Patient 46 HTS
GSM5390560 48-WE: Patient 47 Early Wound
GSM5390561 49-S: Patient 48 HTS
GSM5390562 50-D: Patient 49 Normal skin

+ 68 more — browse all 108 samples with per-sample file links →

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GSE246204 miRNA and mRNA Signatures in Human Acute Kidney Injury Tissue [mRNA, minimal change disease] 12 samples shares injury, acute
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GSE246206 miRNA and mRNA Signatures in Human Acute Kidney Injury Tissue 112 samples shares injury, acute

Search all human 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.