GEO series

Competing dynamic gene regulatory networks involved in fibroblast reprogramming to haematopoietic progenitor cells [RNA-Seq]

GSE287483 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2025/03/26 Platform GPL19057

At a glance

Single-cell RNA-seq · mouse · 12 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: 12 samples: 12 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: Competing dynamic gene regulatory networks involved in fibroblast reprogramming to hematopoietic progenitor cellsStem cell reports · 2025

Summary (written by the submitters)

Direct reprogramming of somatic cells offers a potentially safer therapeutic approach to generate patient-specific haematopoietic cells. However, this strategy is limited by stochasticity of reprogramming. Investigating the gene-regulatory networks involved during reprogramming would help generate functionally relevant cells in adequate numbers.

Read the full summary ↓

To address this, we developed an inducible system to reprogram fibroblasts to HSPCs by ectopically expressing the two transcription factors SCL and LMO2. Transcriptome and epigenome analysis at different stages of reprogramming revealed uniform silencing of fibroblast genes and upregulation of the haemogenic endothelial program. Integrated analysis suggested that TFs FLI1, GATA1/2, KLF14 are direct targets of SCL/LMO2, which subsequently induce the haematopoietic programme. Single cell RNA-Seq revealed conflicting and competing fate decisions at intermediate stages of reprogramming. Inhibiting signalling pathways associated with competing neuronal fate significantly enhanced haematopoietic reprogramming efficiency. In conclusion, this study identifies early/intermediate reprogramming events and associated pathways that were targeted to improve reprogramming efficiency.

Published in

Competing dynamic gene regulatory networks involved in fibroblast reprogramming to hematopoietic progenitor cells

Shafiq S, Hamashima K, Guest LA et al. · Stem cell reports 2025 · PMID 40185089 · doi:10.1016/j.stemcr.2025.102473

This dataset

Open GSE287483 in finder → Find more mouse RNA-seq datasets →

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 GSE287483_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 PRJNA1213081 and SRA study SRP558518. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40185089) ↗

Study design

12 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 12 sample titles: 12 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.

Samples in this study

GSM8746408 Replicate 3 Day 14 Dox c-KIT
GSM8746409 Replicate 2 Day 2 Dox
GSM8746410 Replicate 3 Day 4 Dox
GSM8746411 Replicate 1 Day 2 Dox
GSM8746412 Replicate 1 Day 4 Dox
GSM8746413 Replicate 3 Day 2 Dox
GSM8746414 Replicate 2 Day 14 Dox c-KIT
GSM8746415 Replicate 1 Day 2 Control
GSM8746416 Replicate 2 Day 4 Dox
GSM8746417 Replicate 2 Day 2 Control
GSM8746418 Replicate 1 Day 14 Dox c-KIT
GSM8746419 Replicate 3 Day 2 Control

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Search all mouse RNA-seq datasets in GEO →

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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.