GEO series

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

GSE305970 Homo sapiens (human) Single-cell RNA-seq 273 samples Submitted 2026/07/29 Platform GPL34281

At a glance

Single-cell RNA-seq · human · 273 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: 273 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 GSE305970 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 GSE305970_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 PRJNA1308548 and SRA study SRP610255. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

A cohort of 273 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 AB14953 … AB18840 and carry no group information.

Samples in this study

GSM9190572 AB14953
GSM9190573 AB14954
GSM9190574 AB14955
GSM9190575 AB14957
GSM9190576 AB14959
GSM9190577 AB14962
GSM9190578 AB14964
GSM9190579 AB14966
GSM9190580 AB14967
GSM9190581 AB14968
GSM9190582 AB14970
GSM9190583 AB14973
GSM9190584 AB14974
GSM9190585 AB14975
GSM9190586 AB14978
GSM9190587 AB14980
GSM9190588 AB14981
GSM9190589 AB14982
GSM9190590 AB14984
GSM9190591 AB14986
GSM9190592 AB14987
GSM9190593 AB14988
GSM9190594 AB14989
GSM9190595 AB14991
GSM9190596 AB14992
GSM9190597 AB14995
GSM9190598 AB14996
GSM9190599 AB15101
GSM9190600 AB15102
GSM9190601 AB15104
GSM9190602 AB15105
GSM9190603 AB15106
GSM9190604 AB15107
GSM9190605 AB15108
GSM9190606 AB15109
GSM9190607 AB15110
GSM9190608 AB15111
GSM9190609 AB15112
GSM9190610 AB15113
GSM9190611 AB15115

+ 233 more — browse all 273 samples with per-sample file links →

More Multiple myeloma datasets

GSE161195 Single cell RNA-seq of relapsed and refractory multiple myeloma patients defines new resistance pathways and targeted treatments 213 samples same disease briefing · shares multiple
GSE148715 “Direct to Drug” screening as a precision medicine tool in multiple myeloma 38 samples same disease briefing · shares multiple
GSE167968 RNA-seq of Multiple Myeloma 33 samples same disease briefing · shares multiple
GSE179453 Expression data from control-knockdown (CTR-KD) and MAST4-KD human multiple myeloma (MM) cell lines 6 samples same disease briefing · shares multiple
GSE178442 Genome-wide maps of transcription in DEAF1 overexpression(OE) and wildtype(WT) cells in Multiple Myeloma 4 samples same disease briefing · shares multiple
GSE155631 Oncogenic mechanisms of DIS3 mutations in Multiple Myeloma 62 samples same disease briefing · shares multiple
GSE167969 ATAC-seq and RNA-Seq of Multiple Myeloma 46 samples same disease briefing · shares multiple
GSE167042 Splicing modulation increases BCL2 dependence and sensitizes Multiple Myeloma cells to Venetoclax 4 samples same disease briefing · shares multiple

Search all human 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.