Cross-tissue human fibroblast atlas reveals myofibroblast subtypes with distinct roles in immune modulation [scRNA-seq]
Single-cell RNA-seq · human · 38 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
- 38 samples: 38 samples, each with a distinct title — no replicated groupsGroups 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
- Cross-tissue human fibroblast atlas reveals myofibroblast subtypes with distinct roles in immune modulationCancer cell · 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 GSE246215_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 PRJNA1032060. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 38 sample titles: 38 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.
- GSM7864982 Stomach fibroblast, scRNA-seq [GC-P180118]
- GSM7864983 Stomach fibroblast, scRNA-seq [GC-P180308]
- GSM7864984 Stomach fibroblast, scRNA-seq [GC-P180611]
- GSM7864985 Stomach fibroblast, scRNA-seq [GC-P181019]
- GSM7864986 Stomach fibroblast, scRNA-seq [GC-P181025]
- GSM7864987 Stomach fibroblast, scRNA-seq [GC-P181227]
- GSM7864988 Stomach fibroblast, scRNA-seq [GC-P190103]
- GSM7864989 Stomach fibroblast, scRNA-seq [GC-P190123]
- GSM7864990 Stomach fibroblast, scRNA-seq [GC-P190124]
- GSM7864991 Stomach fibroblast, scRNA-seq [GC-P190118]
- GSM7864992 Liver fibroblast, scRNA-seq [HCC-P20171220]
- GSM7864993 Liver fibroblast, scRNA-seq [HCC-P20180108]
- GSM7864994 Lung fibroblast, scRNA-seq [NSCLC-P1]
- GSM7864995 Lung fibroblast, scRNA-seq [NSCLC-P10]
- GSM7864996 Lung fibroblast, scRNA-seq [NSCLC-P14]
- GSM7864997 Lung fibroblast, scRNA-seq [NSCLC-P24]
- GSM7864998 Lung fibroblast, scRNA-seq [NSCLC-P15]
- GSM7864999 Lung fibroblast, scRNA-seq [NSCLC-P16]
- GSM7865000 Lung fibroblast, scRNA-seq [NSCLC-P18]
- GSM7865001 Lung fibroblast, scRNA-seq [NSCLC-P28]
- GSM7865002 Lung fibroblast, scRNA-seq [NSCLC-P23]
- GSM7865003 Lung fibroblast, scRNA-seq [NSCLC-P6]
- GSM7865004 Lung fibroblast, scRNA-seq [NSCLC-P12]
- GSM7865005 Lung fibroblast, scRNA-seq [NSCLC-P22]
- GSM7865006 Lung fibroblast, scRNA-seq [NSCLC-P25]
- GSM7865007 Breast fibroblast, scRNA-seq [TNBC-P007]
- GSM7865008 Breast fibroblast, scRNA-seq [TNBC-P013]
- GSM7865009 Breast fibroblast, scRNA-seq [TNBC-P018]
- GSM7865010 Breast fibroblast, scRNA-seq [TNBC-P002]
- GSM7865011 Breast fibroblast, scRNA-seq [TNBC-P004]
- GSM7865012 Breast fibroblast, scRNA-seq [TNBC-P016]
- GSM7865013 Breast fibroblast, scRNA-seq [TNBC-P017]
- GSM7865014 Breast fibroblast, scRNA-seq [TNBC-P001]
- GSM7865015 Breast fibroblast, scRNA-seq [TNBC-P020]
- GSM7865016 Breast fibroblast, scRNA-seq [TNBC-P022]
- GSM7865017 Breast fibroblast, scRNA-seq [TNBC-P023]
- GSM7865018 Breast fibroblast, scRNA-seq [TNBC-P024]
- GSM7865019 Breast fibroblast, scRNA-seq [TNBC-P021]
- GSE246219 Cross-tissue human fibroblast atlas reveals myofibroblast subtypes with distinct roles in immune modulation 80 samples shares myofibroblast, cross-tissue, fibroblast, modulation, subtypes, distinct
- GSE298908 Positionally distinct interferon-stimulated dermal immune-acting fibroblasts promote neutrophil recruitment in Sweet's syndrome [RNA-seq] 113 samples shares fibroblast, distinct
- GSE275145 Creeping fat-derived mechanosensitive fibroblasts drive intestinal fibrosis in Crohn’s disease strictures [Human scRNA-seq] 29 samples shares fibroblast, scrna-seq
- GSE309392 Cancer-associated fibroblasts promote esophageal carcinogenesis and lung metastasis by CDH11-mediated extracellular matrix environment remodeling [scRNA-Seq] 23 samples shares fibroblast, scrna-seq
- GSE292979 Characterisation of a pathogenic non-migratory fibroblast population in systemic sclerosis skin [scRNA-Seq] 15 samples shares fibroblast, scrna-seq
- GSE275648 Role of Cancer-Associated Fibroblasts in Prognostic Stratification of Advanced-Stage Gastric Cancer through Interacting with Endothelial and Malignant Epithelial Cells [AGC scRNA-seq] 11 samples shares fibroblast, scrna-seq
- GSE294277 Integrative single cell RNA-sequencing and spatial transcriptomics uncovers distinct macrophage-fibroblast cross-talk in human hip synovium between patients with femoroacetabular impingement and osteoarthritis 22 samples shares fibroblast, distinct
- GSE273682 Sustained and specific multiplexed immune checkpoint modulation in CAR T cells induced by targeted epigenome editing 15 samples shares modulation
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.