Single-cell transcriptomics of infant and adult T cells across human tissues
Single-cell RNA-seq · human · 168 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
- 168 samples: 5 conditions, mostly with about 14 replicates eachGroups 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
- Transcriptional control of T cell tissue adaptation and effector function in infants and adultsbioRxiv : the preprint server for biology · 2025
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 GSE195844_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 PRJNA802269 and SRA study SRP357629. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 85 samples.
How we read this: 5 replicated groups read from 168 sample titles; they account for 85 of them. 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.
- GSM5851936 DF001 DF
- GSM5851937 DF002 DF
- GSM5851938 DF003 DF
- GSM5851939 DF004 DF
- GSM5851940 DF005 DF
- GSM5851941 DF006 DF
- GSM5851942 DF008 DF
- GSM5851943 DP101 DP
- GSM5851944 DP102 DP
- GSM5851945 DP103 DP
- GSM5851946 DP104 DP
- GSM5851947 DP105 DP
- GSM5851948 DP106 DP
- GSM5851949 DP107 DP
- GSM5851950 DP108 DP
- GSM5851951 DP109 DP
- GSM5851952 DP110 DP
- GSM5851953 PP001 PP
- GSM5851954 PP002 PP
- GSM5851955 PP003 PP
- GSM5851956 PP004 PP
- GSM5851957 PP005 PP
- GSM5851958 PP006 PP
- GSM5851959 PP009 PP
- GSM5851960 PP010 PP
- GSM5851961 PP011 PP
- GSM5851962 PP012 PP
- GSM5851963 PP013 PP
- GSM5851964 PP014 PP
- GSM5851965 PP015 PP
- GSM5851966 PP016 PP
- GSM5851967 PP017 PP
- GSM5851968 PP018 PP
- GSM5851969 PP019 PP
- GSM5851970 PP020 PP
- GSM5851971 DH001 DH
- GSM5851972 DH002 DH
- GSM5851973 DH003 DH
- GSM5851974 DH004 DH
- GSM5851975 DH005 DH
+ 128 more — browse all 168 samples with per-sample file links →
- GSE233321 Single-cell map of the healthy human immune system across the lifespan reveals unique infant immune signatures 118 samples shares single-cell, infant, across
- 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 single-cell, across
- GSE334588 Single-cell transcriptomic atlas of adult dermatomyositis, cutaneous lupus erythematosus and healthy skin 48 samples shares single-cell, adult
- GSE318490 Single-cell RNA-seq of tumor samples from six advanced HGSOC patients across treatment stages (pre-NACT, post-NACT and platinum-sensitive recurrence) 10 samples shares single-cell, across
- GSE311423 Single-nucleus Multiome Profiling of Infant and Adult Human Spleen 10 samples shares infant, adult
- GSE332623 Immunological Differences in Atopic Dermatitis Across Age Groups: Insights from Single-Cell Multi-Omics 54 samples shares single-cell, across
- GSE280982 Single-cell sequencing across radiation treatment timepoints in head and neck cancer 49 samples shares single-cell, across
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
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.