GEO series

Longitudinal Analysis of Multiple Myeloma: Therapeutic Response and Immune Microenvironment Dynamics in the Context of Tumor Heterogeneity [CITE-Seq]

GSE309593 Homo sapiens (human) Single-cell RNA-seq 91 samples Submitted 2026/07/15 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 91 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: 91 samples: 91 samples, each with a distinct title — no replicated groupsGroups 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)

Multiple myeloma (MM) is a hematological malignancy characterized by an expansion of malignant plasma cells in the bone marrow. For newly diagnosed MM (NDMM), standard three-drug treatment regimens—such as the combination of bortezomib, lenalidomide, and dexamethasone (VRd)—and the more recent inclusion of a fourth drug, anti-CD38 antibody immunotherapy, have significantly improved patient outcomes by targeting multiple mechanisms of tumorigenesis.

Read the full summary ↓

This treatment approach typically involves several cycles of VRd induction therapy, followed by high dose melphalan (HDM) and autologous stem cell transplant (ASCT), and maintenance therapy. Treatment responses vary widely, with some patients requiring longer and more intense therapy and others exhibiting more rapid responses and longer relapse-free remission.This cycle of remission, maintenance, and eventual relapse persists despite advancements in treatment modalities, including immunotherapy, leaving MM incurable. This study characterizes the tumor state and immune microenvironment in MM patients across variable graded patient responses. Using a multimodal, longitudinal approach, we investigate immune and microenvironmental cellular changes during treatment in NDMM patients and in a second cohort of RRMM patients. For the NDMM cohort, longitudinal blood and bone marrow aspirate samples were collected at diagnosis, throughout induction therapy, following autologous stem cell transplant (ASCT), and at post-transplant intervals of 60 days, 90 days, and 1 and 2 years. Multi-omic profiling of these samples magnifies the individual heterogeneity between tumor and therapeutic response. The longitudinal aspect of the study allowed us to focus on individual-level longitudinal responses instead of cross-patient effects, enabling detailed insights into tumor and microenvironment dynamics. Using Olink proteomic analysis of plasma and bone marrow interstitial fluid (BMIF), and single-cell analyses (3’ CITE-seq, scRNA-seq, flow cytometry) of peripheral blood mononuclear cells (PBMC) and bone marrow mononuclear cells (BMMC) we longitudinally characterized tumor, the immune microenvironment, and therapeutic responses. This revealed distinct immune states and pathways associated with therapeutic response, and tumor survival, highlighting potential targets and therapeutic strategies to improve patient outcomes.

This dataset

Open GSE309593 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 GSE309593_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 PRJNA1336306. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

91 samples, each with a distinct title — no replicated groups

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

How we read this: Read from 91 sample titles: 91 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

GSM9269865 BMC02420-002 MM Pre-Treatment
GSM9269866 BMC02421-002 MM Pre-Treatment
GSM9269867 BMC02422-002 MM End Induction 1st Draw
GSM9269868 BMC02423-001 MM Post Transplant 90 Days
GSM9269869 BMC02424-001 MM Pre-Treatment
GSM9269870 BMC02425-001 MM End Induction 1st Draw
GSM9269871 BMC02426-002 MM Post Transplant 90 Days
GSM9269872 BMC02427-003 MM Pre-Treatment
GSM9269873 BMC02428-001 MM End Induction 1st Draw
GSM9269874 BMC02429-001 MM Post Transplant 90 Days
GSM9269875 BMC02430-003 MM Pre-Treatment
GSM9269876 BMC02431-002 MM Post Transplant 90 Days
GSM9269877 BMC02432-002 MM Pre-Treatment
GSM9269878 BMC02433-002 MM End Induction 1st Draw
GSM9269879 BMC02434-001 MM Pre-Treatment
GSM9269880 BMC02435-002 MM Post Transplant 90 Days
GSM9269881 BMC02436-003 MM Pre-Treatment
GSM9269882 BMC02437-002 MM Pre-Treatment
GSM9269883 BMC02438-002 MM End Induction 1st Draw
GSM9269884 BMC02439-004 MM Pre-Treatment
GSM9269885 BMC02441-003 MM Pre-Treatment
GSM9269886 BMC02594-006 MM Pre-Treatment
GSM9269887 BMC02595-009 MM End Induction 1st Draw
GSM9269888 BMC02596-008 MM Post Transplant 90 Days
GSM9269889 BMC02597-003 MM End Induction 1st Draw
GSM9269890 BMC02599-003 MM Post Transplant 90 Days
GSM9269891 BMC02600-006 MM End Induction 1st Draw
GSM9269892 BMC02601-003 MM Post Transplant 90 Days
GSM9269893 BMC02603-006 MM Pre-Treatment
GSM9269894 BMC02605-005 MM End Induction 1st Draw
GSM9269895 BMC02611-009 MM Pre-Treatment
GSM9269896 BMC02613-010 MM Pre-Treatment
GSM9269897 BMC02617-002 MM Post Transplant 90 Days
GSM9269898 BMC02618-008 MM Post Transplant 90 Days
GSM9269899 BMC02619-003 MM End Induction 1st Draw
GSM9269900 BMC02620-002 MM Post Transplant 90 Days
GSM9269901 BMC04431-002 MM Pre-Treatment
GSM9269902 BMC04434-007 MM Post Transplant 1 year
GSM9269903 BMC04435-009 MM End Induction 1st Draw
GSM9269904 BMC04436-007 MM End Induction 1st Draw

+ 51 more — browse all 91 samples with per-sample file links →

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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, myeloma
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

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.