GEO series

Prrx1 is a Master Transcription Factor of myofibroblastic Cancer-Associated Fibroblasts [Murine_Fibroblast_RNA_seq]

GSE169703 Mus musculus (mouse) Single-cell RNA-seq 21 samples Submitted 2021/12/31 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 21 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: 21 samples
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)

Although cancer associated fibroblasts (CAF) play a critical role in cancer progression, their identities are still unknown. Because cells’ own identity is determined by core regulatory circuit (CRC) comprising master transcription factors (TFs), it is critical to find CAF’s master TF. Therefore, we attempt to find master TF of CAF through the extensive screening of single cell RNA-seq database of cancer tissues, and discovered Prrx1, master regulator of embryonic mesenchymal cells, as candidate for master TF of CAF.

Read the full summary ↓

Prrx1 was abundantly expressed in CAFs with significant correlation with unfavorable clinical outcomes. Furthermore, Prrx1 in fibroblasts dramatically enhanced tumor progression and metastasis in fibroblast-specific Prrx1 inducible mice. On the other hand, tumor formation was severely inhibited in conditional Prrx1 knock-down mice. Finally, we confirmed that Prrx1 is a master TF of CAF and normal fibroblast executing wound healing program using ChIP-SEQ, CRC modeling, and in vivo validation. Furthermore we also revealed that targeting Prrx1 in fibroblast alone induced complete remission of chemotherapy-resistant cancer in preclinical mouse experiments.

This dataset

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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 GSE169703_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: 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 PRJNA717730 and SRA study SRP312315. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 35589735) ↗

Samples in this study

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+ 21 more — browse all 21 samples with per-sample file links →

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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.