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Single-cell transcriptomics of pediatric Burkitt lymphoma

GSE240252 Homo sapiens (human) Single-cell RNA-seq 30 samples Submitted 2024/10/24 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 30 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
Burkitt lymphomaThis series is part of our curated briefing on it.
Samples
30 samples: 30 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
Single-cell transcriptomics of pediatric Burkitt lymphoma reveals intra-tumor heterogeneity and markers of therapy resistanceLeukemia · 2025
Summary (written by the submitters)
Burkitt lymphoma (BL) is the most frequent B-cell lymphoma in pediatric patients. While most patients are currently cured, a fraction of them are resistant to therapy. In order to investigate BL heterogeneity and the features distinguishing the non-responders (NR) to therapy, we analyzed pediatric EBV-negative BL specimens collected at diagnosis by single-cell (sc)-transcriptomics.
Read the full summary ↓
Analysis of the non-tumor component revealed a predominance of immune cell infiltrates and a small representation of fibroblasts that was enriched in NR. Tumor cells displayed patient-specific features, as well as subpopulations that were common to most patients and expressed transcripts related to cell cycle, signaling pathways and cell-of-origin signatures.
Published in
Single-cell transcriptomics of pediatric Burkitt lymphoma reveals intra-tumor heterogeneity and markers of therapy resistance
Corinaldesi C, Holmes AB, Martire G et al. · Leukemia 2025 · PMID 39424708 · doi:10.1038/s41375-024-02431-3
This dataset
Open GSE240252 in finder → Find more human RNA-seq datasets → Burkitt 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 GSE240252_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 PRJNA1003044 and SRA study SRP453875. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39424708) ↗
Study design
30 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 30 sample titles: 30 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
More Burkitt lymphoma datasets

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.