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Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma [Part 3 of 4: Illumina NovaSeq 6000; MARS_v2]

GSE305969 Homo sapiens (human) Single-cell RNA-seq 874 samples Submitted 2026/07/21 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 874 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
Multiple myelomaThis series is part of our curated briefing on it.
Samples
874 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)
Multiple myeloma (MM) is a plasma cell malignancy characterized by substantial genomic and biological heterogeneity, limiting disease classification and development of more targeted therapies. Here, we present a population-level single-cell atlas of MM and detailed clinical annotations from 342 patients representing MM disease and treatment continuum.
Read the full summary ↓
Our analysis identifies five distinct malignant transcriptional archetypes (MM1–MM5), and a proliferative module variably expressed across archetypes. This proliferation signature emerges as a context-dependent program with independent prognostic value across archetypes. Combined archetype classification with proliferation scoring defines therapeutic resistance, genomic instability, and clinical outcomes. External validation using the CoMMpass cohort confirms the biological robustness and prognostic relevance of this stratification across platforms. Leveraging the resolution of our single-cell atlas, we applied a computational pipeline for therapeutic target discovery based on malignancy, cell-type specificity and tissue restriction. This approach highlights FCRL2 as a promising MM-specific therapeutic target, with selective expression in malignant plasma and B cells at both transcriptomic and protein levels. This integrative analysis provides a clinically actionable blueprint for patient-specific stratification and precision medicine in plasma cell malignancies.
This dataset
Open GSE305969 in finder → Find more human RNA-seq datasets → Multiple myeloma 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 GSE305969_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 PRJNA1308554 and SRA study SRP610239. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
A cohort of 874 individual samples

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

How we read this: Sample titles run AB10068 … AB13239 and carry no group information.

Samples in this study

+ 834 more — browse all 874 samples with per-sample file links →

More Multiple myeloma datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.