Spatiotemporal Single-Cell Roadmap of Human Skin Wound Healing (Single-cell-Wound)
Single-cell RNA-seq · human · 12 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Homo sapiens (human)
- Samples
- 12 samples: 3 × Donor5, Wound vs 3 × Donor4, Wound vs 3 × Donor3, WoundGroups 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
- Epigenetic memory of radiotherapy in dermal fibroblasts impairs wound repair capacity in cancer survivorsNature communications · 2024
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE241132_RAW.tar (it can run to tens of gigabytes).
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: 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 PRJNA1006449. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 9 samples.
How we read this: 3 replicated groups read from 12 sample titles; they account for 9 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.
- GSM7717079 Donor5, Skin
- GSM7717080 Donor5, Wound1 Donor5, Wound
- GSM7717081 Donor5, Wound7 Donor5, Wound
- GSM7717082 Donor5, Wound30 Donor5, Wound
- GSM7717083 Donor4, Skin
- GSM7717084 Donor4, Wound1 Donor4, Wound
- GSM7717085 Donor4, Wound7 Donor4, Wound
- GSM7717086 Donor4, Wound30 Donor4, Wound
- GSM7717087 Donor3, Skin
- GSM7717088 Donor3, Wound1 Donor3, Wound
- GSM7717089 Donor3, Wound7 Donor3, Wound
- GSM7717090 Donor3, Wound30 Donor3, Wound
- GSE266265 Endometrial Insights: Unmasking Endometriosis through Single-Cell Profiling and AI-Based Prediction 60 samples shares single-cell, skin
- GSE334588 Single-cell transcriptomic atlas of adult dermatomyositis, cutaneous lupus erythematosus and healthy skin 48 samples shares single-cell, skin
- GSE307215 Spatiotemporal Single-Cell Profiling Reveals T Cell Clonal Dynamics and Phenotypic Plasticity in Human Graft-versus-Host Disease 104 samples shares spatiotemporal, single-cell
- GSE279392 Single-cell RNA and TCR sequencing from skin, blood and cerebral spinal fluid (CSF) samples from Degos disease patients 116 samples shares single-cell, skin
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
- GSE227835 Single-cell RNA-seq data of human PBMC from Myasthenia Gravis patients 40 samples shares single-cell
- GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 samples shares single-cell
- GSE263733 Single-cell transcriptome analysis reveals subtype-specific clonal evolution and microenvironmental changes in liver metastasis of pancreatic adenocarcinoma and their clinical implications 218 samples shares single-cell
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.