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Single-cell RNA-seq analysis reveals distinct tumor and immunosuppressive T-cell phenotypes in CLL patients treated with ibrutinib

GSE311112 Homo sapiens (human) Single-cell RNA-seq 105 samples Submitted 2026/01/27 Platform GPL34284
At a glance

Single-cell RNA-seq · human · 105 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
105 samples: 105 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)
The development of Bruton tyrosine kinase inhibitors (BTKi) and their introduction into clinical practice represents a major advance in the treatment of chronic lymphocytic leukemia (CLL). However, monotherapy with ibrutinib or other BTKis generally does not induce complete remissions or undetectable minimal residual disease (MRD) even with extended therapy.
Read the full summary ↓
Therefore, there is a need to understand the differences between ibrutinib sensitive and resistant CLL cells along with the immune microenvironment to identify novel therapeutic approaches for controlling residual disease during BTKi treatment. Here, we investigated the cellular heterogeneity of peripheral blood mononuclear cells from patients with CLL treated with ibrutinib using single-cell RNA sequencing. We identified unique transcriptional heterogeneity within the B cell cluster in the ibrutinib-sensitive and resistant patients. Ibrutinib sensitive cells showed enrichment of B cell populations with upregulation of MHC I molecules and TNF family members. Additionally, we observed that inflammatory response and metabolism related pathways were decreased, whereas cellular response to stress and DNA repair programs were increased in the ibrutinib resistance samples. T cells in ibrutinib-resistant patients showed expansion of Tregs and an exhausted CD8 effector T cell compartment. Furthermore, CD14+ and CD16+ monocytes from ibrutinib resistant patients preferentially expressed a gene expression program of antiviral immunity. At the single-cell level, our findings demonstrate a picture of transcriptional heterogeneity in the tumor compartment and immune milieu. Overall, these findings suggest that T cell exhaustion and monocyte functional polarization may have a crucial role in BTKi resistance.
This dataset
Open GSE311112 in finder → Find more human RNA-seq datasets → Chronic lymphocytic leukemia 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 GSE311112_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 PRJNA1368690 and SRA study SRP648402. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
105 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 105 sample titles: 105 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

+ 65 more — browse all 105 samples with per-sample file links →

More Chronic lymphocytic leukemia 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.