Single-cell RNA sequencing comparison of CD4+, CD8+ and T-cell receptor γδ+ cutaneous T-cell lymphomas reveals subset-specific molecular phenotypes
Single-cell RNA-seq · human · 44 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)
- Samples
- 44 samples: 44 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 RNA sequencing comparison of CD4+, CD8+ and T-cell receptor γδ+ cutaneous T-cell lymphomas reveals subset-specific molecular phenotypesThe British journal of dermatology · 2025
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE266862_RAW.tar (it can run to tens of gigabytes).
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 PRJNA1108611. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 44 sample titles: 44 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.
- GSM8255139 Healthy control skin, P112, scRNA-seq
- GSM8255140 Healthy control skin, P115, scRNA-seq
- GSM8255141 Healthy control skin, P116, scRNA-seq
- GSM8255142 Healthy control skin, P121, scRNA-seq
- GSM8255143 CD4 MF, P76, scRNA-seq
- GSM8255144 CD4 MF, P171, scRNA-seq
- GSM8255145 CD4 MF, P204, scRNA-seq
- GSM8255146 CD4 MF, P312, scRNA-seq
- GSM8255147 CD4 MF, P311, scRNA-seq
- GSM8255148 CD4 MF, P303, scRNA-seq
- GSM8255149 CD4 MF, PGS, scRNA-seq
- GSM8255150 Berti’s Lymphoma, P218, scRNA-seq
- GSM8255151 Berti’s Lymphoma, P195, scRNA-seq
- GSM8255152 Berti’s Lymphoma, P230, scRNA-seq
- GSM8255153 Berti’s Lymphoma, P95, scRNA-seq
- GSM8255154 Gammadelta MF, P127, scRNA-seq
- GSM8255155 Gammadelta MF, P71, scRNA-seq
- GSM8255156 Gammadelta MF, P318A, scRNA-seq
- GSM8255157 Gammadelta MF, P318B, scRNA-seq
- GSM8255158 Gammadelta MF, P97, scRNA-seq
- GSM8255159 Gammadelta MF, P124, scRNA-seq
- GSM8255160 Gammadelta MF, P202, scRNA-seq
- GSM8255161 Healthy control skin, P112, scTCR-seq
- GSM8255162 Healthy control skin, P115, scTCR-seq
- GSM8255163 Healthy control skin, P116, scTCR-seq
- GSM8255164 Healthy control skin, P121, scTCR-seq
- GSM8255165 CD4 MF, P76, scTCR-seq
- GSM8255166 CD4 MF, P171, scTCR-seq
- GSM8255167 CD4 MF, P204, scTCR-seq
- GSM8255168 CD4 MF, P312, scTCR-seq
- GSM8255169 CD4 MF, P311, scTCR-seq
- GSM8255170 CD4 MF, P303, scTCR-seq
- GSM8255171 CD4 MF, PGS, scTCR-seq
- GSM8255172 Berti’s Lymphoma, P218, scTCR-seq
- GSM8255173 Berti’s Lymphoma, P195, scTCR-seq
- GSM8255174 Berti’s Lymphoma, P230, scTCR-seq
- GSM8255175 Berti’s Lymphoma, P95, scTCR-seq
- GSM8255176 Gammadelta MF, P127, scTCR-seq
- GSM8255177 Gammadelta MF, P71, scTCR-seq
- GSM8255178 Gammadelta MF, P318A, scTCR-seq
+ 4 more — browse all 44 samples with per-sample file links →
- GSE308745 The Cd4+ T cell population Partners with Tpex CD8 T cells to mediate Antitumor Immunity in the Tumor Microenvironment.[PB cohort2] 87 samples shares cd4, cd8
- GSE253723 Reverse translational discovery reveals dysregulation of CD4+ and CD8+ resident memory T, myeloid, and stromal cells in steroid-refractory, checkpoint inhibitor colitis 12 samples shares cd4, cd8
- GSE328275 Single-cell RNA sequencing of CD45+ immune cells across primary tumor, sentinel tumor-draining lymph node, and axillary lymph node in treatment-naive triple-negative breast cancer 28 samples shares cd4, single-cell
- GSE272005 Genomic and single-cell landscape of transformed cutaneous T-cell lymphoma 32 samples shares single-cell, cutaneous
- GSE334588 Single-cell transcriptomic atlas of adult dermatomyositis, cutaneous lupus erythematosus and healthy skin 48 samples shares single-cell, cutaneous
- GSE272993 Combination anti-PD-1 and anti-CTLA-4 therapy generates waves of clonal responses that include progenitor-exhausted CD8 T cells [dataset 1] 360 samples shares cd8
- GSE298811 Immune dysregulation and stem-like CD8+ T cell enrichment in type 1 diabetes pancreatic lymph nodes 57 samples shares cd8
- GSE279781 CD137 agonism enhances anti-PD1 induced activation of clonally expanded CD8+ T cells in a neoadjuvant pancreatic cancer clinical trial 30 samples shares cd8
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.