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Activation and exhaustion of CD8 T cells in patients with chronic lymphocytic leukemia treated with ibrutinib and pembrolizumab [scRNA-seq, scTCR-seq and bulk RNA-seq]

GSE283397 Homo sapiens (human) Single-cell RNA-seq 15 samples Submitted 2025/08/27 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 15 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
Chronic lymphocytic leukemiaThis series is part of our curated briefing on it.
Samples
15 samples: 15 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
Activation and exhaustion of CD8 T cells in patients with chronic lymphocytic leukemia treated with ibrutinib and pembrolizumabJournal of immunology (Baltimore, Md. : 1950) · 2025
Summary (written by the submitters)
Single-agent anti-PD-1 antibodies lack clinical efficacy in patients with chronic lymphocytic leukemia (CLL). Here, we study the peripheral blood samples from high-risk or relapsed/refractory CLL before and after combination therapy of ibrutinib, fludarabine, and pembrolizumab (IFP). We performed bulk RNA-sequencing (bulk RNA-seq) to detect the transcriptomic changes of CLL and T cells during treatment, and demonstrated the immune responses of CD8 T cells to pembrolizumab by single-cell RNA sequencing (scRNA-Seq).
Published in
Activation and exhaustion of CD8 T cells in patients with chronic lymphocytic leukemia treated with ibrutinib and pembrolizumab
Mu R, Hornick KM, Redekar N et al. · Journal of immunology (Baltimore, Md. : 1950) 2025 · PMID 40796313 · doi:10.1093/jimmun/vkaf182
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 GSE283397_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 PRJNA1193581 and SRA study SRP549066. Searching any of these in the dataset finder brings you back here.

Study design
15 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 15 sample titles: 15 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 Chronic lymphocytic leukemia 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.