Gene expression profile at single cell level of HIV-1 and HIV-2 specific CD8 T cells
Single-cell RNA-seq · human · 17 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
- 17 samples: 16 distinct titles, 1 of them repeatedGroups 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
- Clonal succession after prolonged antiretroviral therapy rejuvenates CD8(+) T cell responses against HIV-1Nature immunology · 2024
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 GSE270651_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 PRJNA1127628 and SRA study SRP515806. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 17 sample titles: 16 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.
- GSM8349314 HIV2-Donor1
- GSM8349315 HIV2-Donor2
- GSM8349316 PS03-EARLY
- GSM8349317 PS03-INT
- GSM8349318 PS03-LATE
- GSM8349319 PS14-EARLY
- GSM8349320 PS14-INT
- GSM8349321 PS14-LATE
- GSM8349322 PS212-EARLY
- GSM8349323 PS212-INT
- GSM8349324 PS212-LATE
- GSM8349325 PS24EI8-EARLY
- GSM8349326 PS24EI8-INT
- GSM8349327 PS24EI8-LATE
- GSM8349328 PS24FL8-EARLY
- GSM8349329 PS24FL8-INT
- GSM8349330 PS24FL8-LATE
- GSE317789 Single-cell and spatial profiling reveal cDC2A-CXCL13+ CD8+ T-epithelial cell crosstalk and cytotoxicity through TNFRSF9 in cutaneous and mucosal lichen planus 42 samples shares cd8
- GSE223075 Next Generation Sequencing facilitates to identify the differential expression genes of sorted conventional CD8+ T cells and MAIT cells 35 samples shares cd8
- GSE288581 Single Cell Immune Profiling in Ankylosing Spondylitis Reveals Resistance of CD8+ T cells to Immune Exhaustion 30 samples shares cd8
- GSE235090 Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [scRNA_tissue] 192 samples shares cd8
- GSE295725 Multimodal delineation of a layer of effector function among exhausted CD8 T cells in tumors 98 samples shares cd8
- GSE328010 RNA sequencing of naïve, activated and HIV-1 infected CD4+ T cells 9 samples shares hiv-1
- GSE235244 Senescent cancer-associated fibroblasts in pancreatic adenocarcinoma suppress CD8+ T cell activation and inhibit response to immune checkpoint therapy [hPDAC] 8 samples shares cd8
- GSE225063 Blockade of LAG-3 and PD-1 leads to co-expression of cytotoxic and exhaustion gene modules in CD8+ T cells to promote antitumor immunity 256 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.