Perivascular fibrosis is mediated by a KLF10-IL-9 signaling axis in CD4+ T cells
Single-cell RNA-seq · mouse · 27 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Mus musculus (mouse)
- Samples
- 27 samples: 6 conditions, mostly with about 6 replicates eachGroups 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
- Perivascular Fibrosis Is Mediated by a KLF10-IL-9 Signaling Axis in CD4+ T CellsCirculation research · 2022
Read the full summary ↓
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 GSE199288_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 PRJNA819170 and SRA study SRP365568. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 27 samples.
How we read this: 6 replicated groups read from 27 sample titles; they account for 27 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.
- GSM5968725 01_Nonstripped_aorta_AO_2 01_Nonstripped_aorta_AO
- GSM5968726 01_Nonstripped_aorta_AO_3 01_Nonstripped_aorta_AO
- GSM5968727 01_Nonstripped_aorta_AO_5 01_Nonstripped_aorta_AO
- GSM5968728 01_Nonstripped_aorta_AO_7 01_Nonstripped_aorta_AO
- GSM5968729 01_Nonstripped_aorta_AO_8 01_Nonstripped_aorta_AO
- GSM5968730 01_Nonstripped_aorta_AO_10 01_Nonstripped_aorta_AO
- GSM5968731 02_Nonstripped_aorta_A401 02_Nonstripped_aorta_A
- GSM5968732 02_Nonstripped_aorta_A402 02_Nonstripped_aorta_A
- GSM5968733 02_Nonstripped_aorta_A406 02_Nonstripped_aorta_A
- GSM5968734 02_Nonstripped_aorta_A407 02_Nonstripped_aorta_A
- GSM5968735 02_Nonstripped_aorta_A307 02_Nonstripped_aorta_A
- GSM5968736 02_Nonstripped_aorta_A308 02_Nonstripped_aorta_A
- GSM5968737 03_Aotric_fibroblast_1463 03_Aotric_fibroblast
- GSM5968738 03_Aotric_fibroblast_1464 03_Aotric_fibroblast
- GSM5968739 03_Aotric_fibroblast_1465 03_Aotric_fibroblast
- GSM5968740 03_Aotric_fibroblast_2443 03_Aotric_fibroblast
- GSM5968741 03_Aotric_fibroblast_2445 03_Aotric_fibroblast
- GSM5968742 03_Aotric_fibroblast_2447 03_Aotric_fibroblast
- GSM5968743 04_IL9_blocker_nonstripped_aorta_IL9-Blocker_2631 04_IL9_blocker_nonstrip…
- GSM5968744 04_IL9_blocker_nonstripped_aorta_IL9-Blocker_2633 04_IL9_blocker_nonstrip…
- GSM5968745 04_IL9_blocker_nonstripped_aorta_IL9-Blocker_2634 04_IL9_blocker_nonstrip…
- GSM5968746 04_IL9_blocker_nonstripped_aorta_IL9-Blocker_2642 04_IL9_blocker_nonstrip…
- GSM5968747 04_IL9_blocker_nonstripped_aorta_IL9-Blocker_2645 04_IL9_blocker_nonstrip…
- GSM5974284 05_single cell_Cre_1 05_single cell_Cre
- GSM5974285 05_single cell_Cre_2 05_single cell_Cre
- GSM5974286 05_single cell_TKO_1 05_single cell_TKO
- GSM5974287 05_single cell_TKO_2 05_single cell_TKO
- GSE300922 Xin-Zi-Sheng-Wan Decoction Alleviates Hyperuricemia-Induced Renal Injury by Modulating SPP1-CD44-Mediated Macrophage Infiltration and SRC/FAK/β-Catenin Signaling Pathway 12 samples shares cd4, mediated
- GSE289821 A pathogenic CD4 T cell phenotype in experimental uveitis shares common features with other immune mediated inflammatory diseases [EAU] 24 samples shares cd4, mediated
- GSE289820 A pathogenic CD4 T cell phenotype in experimental uveitis shares common features with other immune mediated inflammatory diseases 12 samples shares cd4, mediated
- GSE229916 Pericytes and perivascular fibroblasts contribute to central nervous system fibrosis in a region dependent manner 832 samples shares perivascular, fibrosis
- GSE328634 Asiatic acid from Centella asiatica alleviates renal fibrosis: coordinated modulation of the gut–kidney axis and retinol metabolism 15 samples shares fibrosis, axis
- GSE333386 PARK7-Mediated Delactylation of SF3B Reprograms RNA Splicing to Attenuate Fibrosis and Promote Anti-Tumor Immunity 9 samples shares fibrosis, mediated
- GSE222996 The immune landscape of the “deregulated host response” in sepsis: newly emerging MDSCs with high expression of CD47 leads to B cell suppression via App-CD74 signaling [RNA-seq] 61 samples shares cd4
- GSE235113 Tumor cell intrinsic type I interferon signaling dictates CD47-SIRPα blockade immunotherapy via metabolic reprograming. 22 samples shares cd4
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.