GEO series

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

GSE309595 Homo sapiens (human) Single-cell RNA-seq 270 samples Submitted 2026/07/15 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 270 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: 270 samples: 270 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 GSE309595 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 GSE309595_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 PRJNA1336307. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

270 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 270 sample titles: 270 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

GSM9269956 PB06425-001 MM Post Transplant 1 year
GSM9269957 PB00135-01 MM Post Induction 2-Cycles
GSM9269958 PB00132-03 MM Pre-Treatment
GSM9269959 PB00133-12 MM Pre-Treatment
GSM9269960 PB00233-01 MM Pre-Treatment
GSM9269961 PB00234-01-01 MM Pre-Treatment
GSM9269962 PB00236-12 MM Post Induction 2-Cycles
GSM9269963 PB00237-11 MM Pre-Treatment
GSM9269964 PB00238-02 MM End Induction 1st Draw
GSM9269965 PB00239-02 MM Pre-Treatment
GSM9269966 PB00242-01-02 MM Post Induction 2-Cycles
GSM9269967 PB00243-05 MM Post Induction 2-Cycles
GSM9269968 PB00245-01 MM End Induction 1st Draw
GSM9269969 PB00247-01 MM Pre-Treatment
GSM9269970 PB00248-03 MM End Induction 1st Draw
GSM9269971 PB00249-02 MM End Induction 1st Draw
GSM9269972 PB00251-02 MM Post Transplant 60 Days
GSM9269973 PB01315-07 MM Pre-Treatment
GSM9269974 PB01322-12 MM Post Transplant 60 Days
GSM9269975 PB01328-01 Flu Year 1 Day 7
GSM9269976 PB01329-03 COVID-19 Visit 4 Pre-transplant
GSM9269977 PB01333-01 MM Post Induction 2-Cycles
GSM9269978 PB01357-08 MM End Induction 1st Draw
GSM9269979 PB01364-08 MM End Induction 1st Draw
GSM9269980 PB02050-07 MM Pre-Treatment
GSM9269981 PB02066-01 Flu Year 1 Day 0
GSM9269982 PB02068-06 MM Post Induction 2-Cycles
GSM9269983 PB02073-03 Flu Year 1 Stand-Alone
GSM9269984 PB02076-06 Flu Year 1 Day 7
GSM9269985 PB02079-01 MM Post Transplant 60 Days
GSM9269986 PB02082-04 Flu Year 1 Day 90
GSM9269987 PB02088-03 Flu Year 2 Day 90
GSM9269988 PB02094-03 MM End Induction 1st Draw
GSM9269989 PB02686-005 MM Post Transplant 60 Days
GSM9269990 PB02691-004 MM Post Transplant 1 year
GSM9269991 PB02695-005 Flu Year 1 Day 90
GSM9269992 PB02697-001 MM End Induction 1st Draw
GSM9269993 PB02709-001-003 MM Pre-Treatment
GSM9269994 PB02726-003 MM Pre-Treatment
GSM9269995 PB02728-001 MM Pre-Treatment

+ 230 more — browse all 270 samples with per-sample file links →

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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
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
GSE155631 Oncogenic mechanisms of DIS3 mutations in Multiple Myeloma 62 samples same disease briefing · shares multiple, myeloma
GSE167969 ATAC-seq and RNA-Seq of Multiple Myeloma 46 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

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.