A distinct subset of stem-cell memory is poised for the cytotoxicity program in CD4+ T cells in humans
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 290 samples
- What it is
- ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
- Organism
- Homo sapiens (human)
- Samples
- 290 samples: 290 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
- A distinct subset of stem-cell memory is poised for the cytotoxicity program in CD4(+) T cells in humansScience advances · 2026
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 GSE288989_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.
Also filed as BioProject PRJNA1220165 and SRA study SRP566937. 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 290 sample titles: 290 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.
- GSM8779910 bulkATAC_D1_CD4_Naive
- GSM8779911 bulkATAC_D2_CD4_Naive
- GSM8779912 bulkATAC_D3_CD4_Naive
- GSM8779913 bulkATAC_D4_CD4_Naive
- GSM8779914 bulkATAC_D5_CD4_Naive
- GSM8779915 bulkATAC_D6_CD4_Naive
- GSM8779916 bulkATAC_D7_CD4_Naive
- GSM8779917 bulkATAC_D8_CD4_Naive
- GSM8779918 bulkATAC_D9_CD4_Naive
- GSM8779919 bulkATAC_D10_CD4_Naive
- GSM8779920 bulkATAC_D1_CD4_TEMRA_E
- GSM8779921 bulkATAC_D2_CD4_TEMRA_E
- GSM8779922 bulkATAC_D3_CD4_TEMRA_E
- GSM8779923 bulkATAC_D4_CD4_TEMRA_E
- GSM8779924 bulkATAC_D5_CD4_TEMRA_E
- GSM8779925 bulkATAC_D6_CD4_TEMRA_E
- GSM8779926 bulkATAC_D7_CD4_TEMRA_E
- GSM8779927 bulkATAC_D8_CD4_TEMRA_E
- GSM8779928 bulkATAC_D9_CD4_TEMRA_E
- GSM8779929 bulkATAC_D10_CD4_TEMRA_E
- GSM8779962 bulkRNASeq_D1_CD4_Naive
- GSM8779963 bulkRNASeq_D2_CD4_Naive
- GSM8779964 bulkRNASeq_D3_CD4_Naive
- GSM8779965 bulkRNASeq_D4_CD4_Naive
- GSM8779966 bulkRNASeq_D5_CD4_Naive
- GSM8779967 bulkRNASeq_D6_CD4_Naive
- GSM8779968 bulkRNASeq_D7_CD4_Naive
- GSM8779969 bulkRNASeq_D8_CD4_Naive
- GSM8779970 bulkRNASeq_D9_CD4_Naive
- GSM8779971 bulkRNASeq_D10_CD4_Naive
- GSM8779972 bulkRNASeq_D1_CD4_TSCM
- GSM8779973 bulkRNASeq_D2_CD4_TSCM
- GSM8779974 bulkRNASeq_D3_CD4_TSCM
- GSM8779975 bulkRNASeq_D4_CD4_TSCM
- GSM8779976 bulkRNASeq_D5_CD4_TSCM
- GSM8779977 bulkRNASeq_D6_CD4_TSCM
- GSM8779978 bulkRNASeq_D7_CD4_TSCM
- GSM8779979 bulkRNASeq_D8_CD4_TSCM
- GSM8779980 bulkRNASeq_D9_CD4_TSCM
- GSM8779981 bulkRNASeq_D10_CD4_TSCM
+ 250 more — browse all 290 samples with per-sample file links →
- GSE314342 Genome-scale perturb-seq in primary human CD4+ T cells reveals genes regulating T cell programs and human immune traits. 577 samples shares cd4, program
- GSE320155 scRNA‑seq of human intra‑hepatic CD4⁺ T cells reveals unique Treg transcriptional identities and two steady‑state tissue‑adaptation programs across healthy and HCC liver samples 60 samples shares cd4, program
- GSE290679 Metabolic Adaptations Rewire CD4 T Cells in a Subset-Specific Manner in Human Critical Illness with and without Sepsis 47 samples shares cd4, subset
- GSE298578 Single cell RNA profiling of blood CD4+ T cells identifies distinct helper and dysfunctional regulatory clusters in children with SLE (5' PBMC validation dataset) 14 samples shares cd4, distinct
- 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, memory
- GSE279021 Transcriptional programs of cell identity and p53-induced stress responses are associated with distinctive features of spatial genome organization [SuperSeries] 160 samples shares distinct, program
- GSE332630 Multimodal single-cell analyses reveal distinct fusion-regulated transcriptional programs in Ewing sarcoma 22 samples shares distinct, program
- GSE310664 Analysis of experimental colorectal liver metastases identifies distinct proliferation independent transcriptional programs in micro- versus macrometastases 22 samples shares distinct, program
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.