Expansion of memory Vδ2 T cells following SARS-CoV-2 vaccination revealed by temporal single-cell transcriptomics
Single-cell RNA-seq · human · 60 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
- 60 samples: 60 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
- Expansion of memory Vδ2 T cells following SARS-CoV-2 vaccination revealed by temporal single-cell transcriptomicsNPJ vaccines · 2024
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 GSE260763_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 PRJNA1083421 and SRA study SRP493138. 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 60 sample titles: 60 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.
- GSM8123895 VCOV_s01_P0,scRNA-seq
- GSM8123896 VCOV_s01_P1,scRNA-seq
- GSM8123897 VCOV_s01_P2,scRNA-seq
- GSM8123898 VCOV_s01_P3,scRNA-seq
- GSM8123899 VCOV_s01_P4,scRNA-seq
- GSM8123900 VCOV_s02_P0,scRNA-seq
- GSM8123901 VCOV_s02_P1,scRNA-seq
- GSM8123902 VCOV_s02_P2,scRNA-seq
- GSM8123903 VCOV_s02_P3,scRNA-seq
- GSM8123904 VCOV_s02_P4,scRNA-seq
- GSM8123905 VCOV_s03_P0,scRNA-seq
- GSM8123906 VCOV_s03_P1,scRNA-seq
- GSM8123907 VCOV_s03_P2,scRNA-seq
- GSM8123908 VCOV_s03_P3,scRNA-seq
- GSM8123909 VCOV_s03_P4,scRNA-seq
- GSM8123910 VCOV_s04_P0,scRNA-seq
- GSM8123911 VCOV_s04_P1,scRNA-seq
- GSM8123912 VCOV_s04_P2,scRNA-seq
- GSM8123913 VCOV_s04_P3,scRNA-seq
- GSM8123914 VCOV_s04_P4,scRNA-seq
- GSM8123915 VCOV_s05_P0,scRNA-seq
- GSM8123916 VCOV_s05_P1,scRNA-seq
- GSM8123917 VCOV_s05_P2,scRNA-seq
- GSM8123918 VCOV_s05_P3,scRNA-seq
- GSM8123919 VCOV_s05_P4,scRNA-seq
- GSM8123920 VCOV_s06_P0,scRNA-seq
- GSM8123921 VCOV_s06_P1,scRNA-seq
- GSM8123922 VCOV_s06_P3,scRNA-seq
- GSM8123986 VCOV_s01_P0,γδ scTCR-seq
- GSM8123987 VCOV_s01_P1,γδ scTCR-seq
- GSM8123988 VCOV_s01_P2,γδ scTCR-seq
- GSM8123989 VCOV_s01_P3,γδ scTCR-seq
- GSM8123991 VCOV_s01_P4,γδ scTCR-seq
- GSM8123992 VCOV_s02_P0,γδ scTCR-seq
- GSM8123993 VCOV_s02_P1,γδ scTCR-seq
- GSM8123995 VCOV_s02_II_P1,γδ scTCR-seq
- GSM8123996 VCOV_s02_P2,γδ scTCR-seq
- GSM8123997 VCOV_s02_II_P2,γδ scTCR-seq
- GSM8123999 VCOV_s02_P3,γδ scTCR-seq
- GSM8124000 VCOV_s02_II_P3,γδ scTCR-seq
+ 20 more — browse all 60 samples with per-sample file links →
- GSE303324 SARS-CoV-2 Variant Boosters and Breakthrough Infections Drive Functional Differences in the Memory B Cell Repertoire 55 samples shares sars-cov-2, memory
- GSE253661 Longitudinal single-cell transcriptomics of monocytes, Tfh and gd Tcells following a control human malaria infection. 16 samples shares single-cell, following
- GSE307215 Spatiotemporal Single-Cell Profiling Reveals T Cell Clonal Dynamics and Phenotypic Plasticity in Human Graft-versus-Host Disease 104 samples shares single-cell, temporal
- GSE241132 Spatiotemporal Single-Cell Roadmap of Human Skin Wound Healing (Single-cell-Wound) 12 samples shares single-cell, temporal
- GSE279392 Single-cell RNA and TCR sequencing from skin, blood and cerebral spinal fluid (CSF) samples from Degos disease patients 116 samples shares single-cell
- GSE196198 Longitudinal single-cell multiomics data for different severity of COVID-19 100 samples shares single-cell
- GSE254176 Dynamic changes of immune cells in systemic lupus erythematosus with nephritis using longitudinal peripheral blood single-cell RNA-seq 57 samples shares single-cell
- GSE332623 Immunological Differences in Atopic Dermatitis Across Age Groups: Insights from Single-Cell Multi-Omics 54 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.