Metabolic Adaptations Rewire CD4 T Cells in a Subset-Specific Manner in Human Critical Illness with and without Sepsis
Single-cell RNA-seq · human · 47 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Homo sapiens (human)
- Samples
- 47 samples: 47 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
- Metabolic adaptations rewire CD4(+) T cells in a subset-specific manner in human critical illness with and without sepsisNature immunology · 2026
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 GSE290679_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 PRJNA1229534. 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 47 sample titles: 47 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.
- GSM8819385 V-0018.2, CI-Sep
- GSM8819386 V-0019.2, CI-NS
- GSM8819387 V-0020.2, CI-NS
- GSM8819388 V-0021.2, CI-Sep
- GSM8819389 V-0022.2, CI-NS
- GSM8819390 V-0023.2, CI-NS
- GSM8819391 V-0026.2, CI-NS
- GSM8819392 V-0027.2, CI-Sep
- GSM8819393 V-0029.2, CI-NS
- GSM8819394 NHC-0001, NHC
- GSM8819395 NHC-0002, NHC
- GSM8819396 NHC-0003, NHC
- GSM8819397 V-0031.2, CI-NS
- GSM8819398 V-0033.2, CI-Sep
- GSM8819399 V-0034.2, CI-Sep
- GSM8819400 V-0035.2, CI-NS
- GSM8819401 V-0036.2, CI-NS
- GSM8819402 V-0038.2, CI-Sep
- GSM8819403 V-0040.2, CI-Sep
- GSM8819404 V-0041.2, CI-Sep
- GSM8819405 V-0042.2, CI-NS
- GSM8819406 V-0043.2, CI-NS
- GSM8819407 NHC-0004, NHC
- GSM8819408 NHC-0005, NHC
- GSM8819409 V-0053.2, CI-Sep
- GSM8819410 V-0055.2, CI-Sep
- GSM8819411 V-0058.2, CI-NS
- GSM8819412 V-0060.2, CI-Sep
- GSM8819413 V-0063.2, CI-NS
- GSM8819414 V-0064.2, CI-Sep
- GSM8819415 V-0067.2, CI-NS
- GSM8819416 V-0068.2, CI-Sep
- GSM8819417 V-0072.2, CI-Sep
- GSM8819418 NHC-0006, NHC
- GSM8819419 NHC-0007, NHC
- GSM8819420 V-0073.2, CI-NS
- GSM8819421 V-0076.2, CI-Sep
- GSM8819422 V-0077.2, CI-NS
- GSM8819423 V-0078.2, CI-NS
- GSM8819424 V-0079.2, CI-Sep
+ 7 more — browse all 47 samples with per-sample file links →
- GSE266862 Single-cell RNA sequencing comparison of CD4+, CD8+ and T-cell receptor γδ+ cutaneous T-cell lymphomas reveals subset-specific molecular phenotypes 44 samples shares cd4, subset-specific
- GSE267774 Specific CD4+ T cell phenotypes associate with bacterial control in people who ‘resist’ infection with Mycobacterium tuberculosis 3076 samples shares cd4
- GSE242232 Autoantigen-specific CD4+ T cells acquire an exhausted phenotype and persist in human antigen-specific autoimmune diseases 928 samples shares cd4
- 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
- GSE314188 Responders to low-dose ATG induce CD4+ T cell exhaustion in type 1 diabetes 514 samples shares cd4
- GSE227915 CD4+ cells and pediatric obesity-related asthma 99 samples shares cd4
- GSE252020 Decoding CD4+ T cell transcriptome in giant cell arteritis: novel pathways and altered cross-talk with monocytes [RNA-seq] 98 samples shares cd4
- GSE282517 RNAseq of CD4 T cells from 83 individuals with musculoskeletal compains 83 samples shares cd4
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.