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Cytokine-linked remodeling of the immune microenvironment is a hallmark of transformation in follicular lymphoma [scRNA-seq]

GSE303544 Homo sapiens (human) Single-cell RNA-seq 9 samples Submitted 2026/08/04 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 9 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
Follicular lymphomaThis series is part of our curated briefing on it.
Samples
9 samples: 8 × Pool of 10 FL/tFL samples, poolGroups 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)
Across cancer, one of the most frequent examples of histologic transformation is the evolution of follicular lymphoma (FL) to an aggressive large cell lymphoma. Despite recent progress, understanding of the molecular and cellular underpinnings of transformation remains incomplete. Here, we dissect the interplay of tumor and microenvironment cell populations across transformation through a multimodal investigation of 85 FL and transformed FL samples, including single-cell and bulk RNA-sequencing alongside spatial transcriptomics and proteomics.
Read the full summary ↓
Upon transformation, GPNMB+ macrophages and fibroblasts with matrix-remodeling phenotypes increased while lymph-node organizing populations (e.g. follicular dendritic cells, CCL21-expressing fibroblastic reticular cells) were lost. Transformation was marked by altered spatial distribution of cytokines, impacting macrophage differentiation and T cell infiltration. This detection of secreted stromal and macrophage signals was further evident by non-invasive plasma proteomics. Taken together, our data reveal expansion of macrophages and fibroblasts as key features of transformation with potential diagnostic and therapeutic implications.
This dataset
Open GSE303544 in finder → Find more human RNA-seq datasets → Follicular 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 GSE303544_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 PRJNA1295780. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 × Pool of 10 FL/tFL samples, pool

Supports a between-group comparison across 8 samples.

How we read this: 1 replicated groups read from 9 sample titles; they account for 8 of them. 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 Follicular lymphoma datasets

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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.