GEO series

Wnt-dependent spatiotemporal reprogramming of bone marrow niches drives fibrosis

GSE282516 Mus musculus (mouse) RNA-seq (gene expression) 17 samples Submitted 2026/02/06 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 17 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Mus musculus (mouse)
Samples: 17 samples: 17 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: Wnt-dependent spatiotemporal reprogramming of bone marrow niches drives fibrosisHemaSphere · 2026

Summary (written by the submitters)

Bone marrow fibrosis is the most extensive matrix remodeling of the microenvironment, and can include de novo formation of bone (osteosclerosis). Spatiotemporal information on the contribution of distinct bone marrow niche populations to this process is incomplete. We demonstrate that fibrosis-inducing hematopoietic cells cause profibrotic reprogramming of perivascular CXCL12 abundant reticular (CAR) progenitor cells resulting in loss of their hematopoiesis-support and upregulation of osteogenic and pro-apoptotic programs.

Read the full summary ↓

In turn, peritrabecular osteolineage cells (OLCs) are activated in an injury-specific, Wnt-dependent manner, comparable to skeletal repair. OLCs fuel bone marrow fibrosis through their expansion and skewed differentiation, resulting in osteosclerosis and expansion of Ly6a+ fibroblasts. Ncam1 expression marks peritrabecular OLCs and their expansion into the central marrow is specific for fibrosis in mice and patients. Peritrabecular stromal b-catenin expression is linked to fibrosis in patients and inhibition of Wnt signaling reduces bone marrow fibrosis and osteosclerosis, possibly being a clinically relevant therapeutic target.

Published in

Wnt-dependent spatiotemporal reprogramming of bone marrow niches drives fibrosis

Banjanin B, Nagai J, Mun Y et al. · HemaSphere 2026 · PMID 41726391 · doi:10.1002/hem3.70309

This dataset

Open GSE282516 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 GSE282516_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1189074 and SRA study SRP546787. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41726391) ↗

Study design

17 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 17 sample titles: 17 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

GSM8645372 Gli1,replicate 1,scRNAseq
GSM8645373 Gli1,replicate 2,scRNAseq
GSM8645374 Gli1,replicate 3,scRNAseq
GSM8645375 Gli1,replicate 4,scRNAseq
GSM8645376 Gli1,replicate 5,scRNAseq
GSM8645377 Grem1,replicate 1,scRNAseq
GSM8645378 Grem1,replicate 2,scRNAseq
GSM8645379 Grem1,replicate 3,scRNAseq
GSM8645380 Grem1,replicate 4,scRNAseq
GSM8645381 Pdgfrb,replicate 1,scRNAseq
GSM8645382 Pdgfrb,replicate 2,scRNAseq
GSM8645383 Pdgfrb,replicate 3,scRNAseq
GSM8645384 replicate 1,Visium10x
GSM8645385 replicate 2,Visium10x
GSM8645386 replicate 3,Visium10x
GSM8645387 Tpo 1,scRNAseq
GSM8645388 Tpo_PT 1,scRNAseq

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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.