GEO series

Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma [Part 1 of 4: Illumina NovaSeq 6000; MARS]

GSE305967 Homo sapiens (human) Single-cell RNA-seq 691 samples Submitted 2026/07/21 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 691 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: 691 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 GSE305967 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 GSE305967_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 PRJNA1308550 and SRA study SRP610185. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

A cohort of 691 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 AB10577 … AB14522 and carry no group information.

Samples in this study

GSM9188740 AB10577
GSM9188741 AB10578
GSM9188742 AB10579
GSM9188743 AB10580
GSM9188744 AB10581
GSM9188745 AB10582
GSM9188746 AB10583
GSM9188747 AB10584
GSM9188748 AB10585
GSM9188749 AB10586
GSM9188750 AB10587
GSM9188751 AB10588
GSM9188752 AB10589
GSM9188753 AB10590
GSM9188754 AB10591
GSM9188755 AB10592
GSM9188756 AB10593
GSM9188757 AB10594
GSM9188758 AB10595
GSM9188759 AB10596
GSM9188760 AB10597
GSM9188761 AB10598
GSM9188762 AB10599
GSM9188763 AB10600
GSM9188764 AB10601
GSM9188765 AB10602
GSM9188766 AB10603
GSM9188767 AB10604
GSM9188768 AB10605
GSM9188769 AB10606
GSM9188770 AB10607
GSM9188771 AB10608
GSM9188772 AB10609
GSM9188773 AB10610
GSM9188774 AB10611
GSM9188775 AB10612
GSM9188776 AB10613
GSM9188777 AB10614
GSM9188778 AB10615
GSM9188779 AB10616

+ 651 more — browse all 691 samples with per-sample file links →

More Multiple myeloma datasets

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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.