← BioTransfer GEO Dataset Finder
GEO series

Single-cell tracking reveals tumor-reactive T cell plasticity during melanoma TIL therapy

GSE335480 Homo sapiens (human) Single-cell RNA-seq 38 samples Submitted 2026/07/10 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 38 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
MelanomaThis series is part of our curated briefing on it.
Samples
38 samples: 38 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)
Adoptive cell therapy (ACT) with tumor-infiltrating lymphocytes (TILs) can induce durable responses in metastatic melanoma, yet the clonal and transcriptional dynamics that govern tumor-reactive T cell fate during ex vivo expansion and after transfer remain poorly understood.
Read the full summary ↓
Here, we performed longitudinal single-cell RNA and T cell receptor (TCR) sequencing across five timepoints, from baseline tumors through a two-phase ex vivo expansion to post-infusion peripheral blood and tumor biopsies, in seven melanoma patients, generating a high-resolution map of both CD8+ and CD4+ tumor-reactive T cell plasticity. During early expansion (preREP), tumor-reactive CD8+ T cells were reinvigorated from an exhausted state and acquired distinct HLA-II-high or KLF2-high transcriptional profiles. In-depth characterization of the CD4+ compartment revealed lineage-dependent reinvigoration. After transfer, both CD8+ and CD4+ tumor-reactive clones acquired stem-like profiles and tissue-homing markers before re-infiltrating tumor lesions. Mechanistically, we identify two processes linked to treatment failure in non-responders: de novo expansion of immunosuppressive regulatory T cells, and co-transfer of Type 17 T cells. These data provide a comprehensive longitudinal framework of T cell plasticity during TIL-ACT.
This dataset
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.

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 GSE335480_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1478614. Searching any of these in the dataset finder brings you back here.

Study design
38 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 9 samples read as cases, 1 as controls.

How we read this: Read from 38 sample titles: 38 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 Melanoma 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.