Fibroblast

Download processed single-cell RNA-seq data for stromal fibroblasts

Dataset Information

Cell Type

Fibroblasts

Total Cells

154,074 cells

Sample Count

366 samples

Subpopulation Count

15 subpopulations

Download Resources

Raw Data Files

Complete single-cell RNA sequencing data in standard 10X Genomics format

10X Raw Data Package

File: Fibroblast_10x_raw_package.zip

Format: ZIP

Description: Contains barcodes/features/matrix/metadata in standard 10X format

Download
Zenodo

Barcode File

File: Fibroblast_barcodes.tsv.gz

Format: TSV.GZ

Description: Contains cell barcode information for each cell in the dataset.

Feature File

File: Fibroblast_features.tsv.gz

Format: TSV.GZ

Description: Gene features and annotations including gene symbols and IDs.

Matrix File

File: Fibroblast_matrix.mtx.gz

Format: MTX.GZ

Description: Sparse expression matrix containing gene expression counts.

Metadata File

File: Fibroblast_metadata.csv.gz

Format: CSV.GZ

Description: Cell-level metadata including annotations and sample information.

Marker Genes

Marker lists and published genesets are uploaded in Zenodo：scHNDB-Cell Type-Marker Genes（DOI 10.5281/zenodo.19535753）

All markers (each subpopulation)

File: Fibroblast_Myocyte_all_Markers_of_each_subpopulation.xls

Description: All markers of each subpopulation

Download (China Mirror OSS)

Top10 markers (each subpopulation)

File: Fibroblast_Myocyte_top10_Markers_of_each_subpopulation.xls

Description: Top10 marker of each subpopulation

Download (China Mirror OSS)

Published reference gene sets

File: Fibroblast_ref_geneset.zip

Description: Published gene sets (ZIP)

Download (China Mirror OSS)

Key Marker Genes for Fibroblasts

  • Pan fibroblast markers: COL1A1, COL1A2, DCN
  • Cancer-associated fibroblasts: FAP, PDGFRA, ACTA2
  • Normal fibroblasts: LUM, DPT, C7
  • Myofibroblasts: TAGLN, MYL9, ACTA2
  • ECM production: COL3A1, FN1, VIM

Usage Instructions

These files are compatible with standard single-cell analysis tools. To load the data:

In R (Seurat):

library(Seurat)
data <- Read10X(data.dir = "path/to/extracted/files")
seurat_obj <- CreateSeuratObject(counts = data)

In Python (Scanpy):

import scanpy as sc
adata = sc.read_10x_mtx('path/to/extracted/files')