← BioTransfer GEO Dataset Finder
GEO series

Pathogenesis of Diffuse Large B-cell Lymphoma Proteogenotypes

GSE324977 Homo sapiens (human) Single-cell RNA-seq 53 samples Submitted 2026/03/31 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 53 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Topic
Diffuse large B-cell lymphomaThis series is part of our curated briefing on it.
Samples
53 samples: 53 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.
Paper
Pathogenesis of diffuse large B cell lymphoma proteogenotypesCancer cell · 2026
Summary (written by the submitters)
The clinical and molecular heterogeneity of diffuse large B-cell lymphoma (DLBCL) is incompletely understood. By integrating proteomic, transcriptomic and genomic data from 478 DLBCL tumors, we identify seven DLBCL proteogenotypes (PGs) reflecting specific pathophysiological features and spanning cell-of-origin (COO) as well as genetic subtypes.
Read the full summary ↓
PG4 is associated with poor outcome, independent of known risk factors including COO, genetic features or the international prognostic index. PG4 is enriched for subsets of activated B-cell-like DLBCL, germinal-center-B-cell-like tumors and genetically unclassified cases. It shares a dark-zone related B-cell phenotype and an enrichment of BTG1 and TBL1XR1 mutations. Single-cell sequencing and spatial transcriptomics further reveals enhanced MYC and TCF3/4 transcriptional activity while MYC translocations were absent, as well as exhausted CD8 T cells as common driver mechanisms in PG4. Our proteogenomic framework identifies heretofore unknown biological heterogeneity among DLBCL tumors, including high-risk proteogenomic features that provide a basis for innovative diagnostic and therapeutic approaches.
Published in
Pathogenesis of diffuse large B cell lymphoma proteogenotypes
Enssle JC, Häupl B, Qoku A et al. · Cancer cell 2026 · PMID 42242231 · doi:10.1016/j.ccell.2026.05.008
This dataset
Open GSE324977 in finder → Find more human RNA-seq datasets → Diffuse large B-cell lymphoma 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 GSE324977_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 PRJNA1438157 and SRA study SRP684119. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42242231) ↗
Study design
53 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 53 sample titles: 53 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

+ 13 more — browse all 53 samples with per-sample file links →

More Diffuse large B-cell lymphoma 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.