GEO series

Spatial transcriptomics unveils novel potential disease mechanisms associated with the microenvironment in Multiple Myeloma

GSE269875 Homo sapiens (human) Spatial transcriptomics 15 samples Submitted 2025/06/09 Platform GPL30172Platform GPL30173

At a glance

Spatial transcriptomics · human · 15 samples

What it is: Spatial transcriptomicsGene activity measured with its position in a tissue section.
Organism: Homo sapiens (human)
Topic: Multiple myelomaThis series is part of our curated briefing on it.
Samples: 15 samples
Data you can get: A processed table (series matrix) and supplementary files from NCBI.

Summary (written by the submitters)

Understanding the spatial organization of the bone marrow (BM) microenvironment at single cell resolution and the niche specific transcriptional regulatory programs remains a challenge. Using formalin-fixed paraffin-embedded (FFPE) samples from our recently described MIcγ1 multiple myeloma (MM) mouse model (Larrayoz et al, Nat Med. 2023), we performed spatial transcriptional profiling of healthy and multiple myeloma BM using the Visium Spatial Gene Expression analysis (10x Genomics).

Read the full summary ↓

A custom data-analysis framework that combines spatial with single-cell transcriptomic profiles allowed us to identify cell type composition and establish specific cell relations within the bone marrow. As a result, we defined the spatial distribution of transcriptionally heterogenous MM plasma cell (MM-PC) groups. Furthermore, we spatially defined transcriptional programs involved in the pathogenesis of MM within the BM microenvironment that were associated with MM-PC density gradient, such as the spatial location of T cells with effector and exhausted phenotypes. Application of spatial transcriptomics to FFPE human BM biopsies with varying degrees of PC infiltration validated findings from our murine model. The application of spatial transcriptomics to fully mineralized tissues provides a comprehensive spatial overview of the BM microenvironment allowing the identification of deregulated mechanism involved in the pathogenesis of diseases such as MM.

This dataset

Open GSE269875 in finder → Find more datasets → Multiple myeloma landscape →

Download · which file do I need?

Direct links to NCBI, 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.

All supplementary files ↗

3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE269875_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

Whole study · RAW.tar ↓

What the files usually look like: Spatial studies usually ship Space Ranger style folders (count matrix plus tissue images). Look for them among the supplementary files.

Also filed as BioProject PRJNA1124054. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41954292) ↗

Samples in this study

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+ 15 more — browse all 15 samples with per-sample file links →

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GSE167969 ATAC-seq and RNA-Seq of Multiple Myeloma 46 samples same disease briefing · shares multiple, myeloma
GSE148715 “Direct to Drug” screening as a precision medicine tool in multiple myeloma 38 samples same disease briefing · shares multiple, myeloma
GSE167968 RNA-seq of Multiple Myeloma 33 samples same disease briefing · shares multiple, myeloma
GSE179453 Expression data from control-knockdown (CTR-KD) and MAST4-KD human multiple myeloma (MM) cell lines 6 samples same disease briefing · shares multiple, myeloma
GSE167042 Splicing modulation increases BCL2 dependence and sensitizes Multiple Myeloma cells to Venetoclax 4 samples same disease briefing · shares multiple, myeloma
GSE178442 Genome-wide maps of transcription in DEAF1 overexpression(OE) and wildtype(WT) cells in Multiple Myeloma 4 samples same disease briefing · shares multiple, myeloma

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