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Mesenchymal stromal cell reprogramming and aberrant inflammatory signalling in myelofibrosis: insights from single-cell RNA sequencing

GSE241825 Homo sapiens (human) Single-cell RNA-seq 21 samples Submitted 2024/06/01 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 21 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Topic
Myeloproliferative neoplasmsThis series is part of our curated briefing on it.
Samples
21 samples: 20 distinct titles, 1 of them repeatedGroups 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.
Summary (written by the submitters)
Myelofibrosis (MF) is a myeloproliferative neoplasm driven by mutations that impact the JAK/STAT signaling in blood cells, eventually causing fibrosis, splenomegaly, and reduced lifespan. To better understand MF, we performed scRNAseq profiling of collagenase-digested bone marrow biopsy samples to identify disrupted cellular pathways for potential treatment.
Read the full summary ↓
Comparative analysis of 9 MF samples vs. 7 normal controls vs. 4 MF patients after allogeneic transplant showed striking differences in cell type composition and transcriptional profiles. Of note, Compositional comparisons identified the non-hematopoietic stromal cell fraction as harboring the biggest differences with, surprisingly, a proportional relative depletion of mesenchymal stromal cells (MSCs) and enrichment of endothelial cells (ECs) in MF vs. normal controls. Concomitant transcriptional differences were observed between MSCs recovered from MF vs. normal control sample, characterised by reduced expression of hematopoietic support factor genes (eg, KITLG, CXCL12, ANGPTL2, NCAM2, CSF1) and increased expression of extracellular matrix (ECM) genes (eg, FN1, COL1A2, COL3A1, DCN, SPARC), features which suggest an explanation for the increased frequency of graft failure following allograft in MF versus other hematologic malignancies. Single-Cell rEgulatory Network Inference and Clustering (SCENIC) indicated transcription factors with either increased (eg, NR2F2, RUNX2, FOXP2) or decreased (eg, CEBPA, FOXC1, and PPARG) transcriptional activity as candidate master transcriptional regulators of the observed MSC reprogramming in MF. Within the hematopoietic fraction, and agnostic of JAK inhibitor treatment status, comparison of MF vs. control cells unexpectedly revealed significant upregulation of interferon response genes, especially in CD14+ monocytes, but also in neutrophils and their progenitors, centred on expression of interferon-stimulated gene factor 3 (ISGF3) genes IRF9 and STAT1/2. Comparison of MF samples according to ruxolitinib treatment status indicated that ruxolitinib induced downregulation of an NFκB-centered inflammatory program, particularly in neutrophils. Finally, ligand-receptor analysis using NicheNet identified TGFB1, IL1B, and TNF as top predicted ligands for induction of the MF signature in MSCs. While myeloid cells were top producers of these factors, their expression did not vary between MF vs. controls, suggesting other mechanisms, such as prolonged retention of ligand in the ECM, are important. In summary, our comparative scRNA analysis of BM cells from MF patients and controls – the largest reported to date – reveals (i) transcriptional reprogramming of MSCs leading to reduced expression of hematopoietic support factor and increased expression of ECM genes; and (ii) increased expression of interferon and NFkB inflammatory pathways in myeloid cells.
This dataset
Open GSE241825 in finder → Find more human RNA-seq datasets → Myeloproliferative neoplasms landscape →
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 GSE241825_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 PRJNA1010190 and SRA study SRP457292. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
20 distinct titles, 1 of them repeated
1193, ×2

How we read this: Read from 21 sample titles: 20 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
More Myeloproliferative neoplasms datasets

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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.