Single-cell epigenetic, transcriptional, protein, and TCR states of HIV-1-infected cells in gut
Single-cell RNA-seq · human · 117 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
- 117 samples: 117 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
- Transcription factor BACH2 shapes tissue-resident memory T cell programs to promote HIV-1 persistenceImmunity · 2025
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 GSE299348_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 PRJNA1272648 and SRA study SRP590303. 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 117 sample titles: 117 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.
- GSM9037718 RNA-Seq of PLWH008_A_CD3
- GSM9037719 RNA-Seq of PLWH008_B_CD3
- GSM9037720 RNA-Seq of PLWH012_A_CD3
- GSM9037721 RNA-Seq of PLWH012_B_CD3
- GSM9037722 RNA-Seq of PLWH015_A_CD3
- GSM9037723 RNA-Seq of PLWH015_B_CD3
- GSM9037724 RNA-Seq of PLWH017_A_CD3
- GSM9037725 RNA-Seq of PLWH017_B_CD3
- GSM9037726 RNA-Seq of PLWH023_A_CD3
- GSM9037727 RNA-Seq of PLWH023_B_CD3
- GSM9037728 RNA-Seq of PLWH027_A_CD3
- GSM9037729 RNA-Seq of PLWH027_B_CD3
- GSM9037730 RNA-Seq of PLWH029_A_CD3
- GSM9037731 RNA-Seq of PLWH029_B_CD3
- GSM9037732 RNA-Seq of PLWH035_A_CD3
- GSM9037733 RNA-Seq of PLWH035_B_CD3
- GSM9037734 RNA-Seq of PLWH037_A_CD3
- GSM9037735 RNA-Seq of PLWH037_B_CD3
- GSM9037736 RNA-Seq of PLWH040_A_CD3
- GSM9037737 RNA-Seq of PLWH040_B_CD3
- GSM9037738 RNA-Seq of HD361_A_CD3
- GSM9037739 RNA-Seq of HD361_B_CD3
- GSM9037740 RNA-Seq of HD361_C_CD3
- GSM9037741 RNA-Seq of HD351_A_whole
- GSM9037742 RNA-Seq of HD351_B_whole
- GSM9037743 RNA-Seq of HD351_C_whole
- GSM9037744 RNA-Seq of HD357_A_whole
- GSM9037745 RNA-Seq of HD357_B_whole
- GSM9037746 RNA-Seq of HD357_C_whole
- GSM9037747 RNA-Seq of HD360_A_whole
- GSM9037748 RNA-Seq of HD360_B_whole
- GSM9037749 RNA-Seq of HD360_C_whole
- GSM9037750 RNA-Seq of HD363_A_whole
- GSM9037751 RNA-Seq of HD363_B_whole
- GSM9037752 RNA-Seq of HD363_C_whole
- GSM9037753 RNA-Seq of 035_037_040_pooled_whole
- GSM9037754 RNA-Seq of 008_012_015_pooled_whole
- GSM9037755 RNA-Seq of 017_023_027_029_pooled_whole
- GSM9037756 RNA-Seq of 361_pooled_whole
- GSM9037757 ATAC-Seq of PLWH008_A_CD3
+ 77 more — browse all 117 samples with per-sample file links →
- GSE279392 Single-cell RNA and TCR sequencing from skin, blood and cerebral spinal fluid (CSF) samples from Degos disease patients 116 samples shares tcr, single-cell
- GSE307670 Epigenetic reprogramming of stromal cell states underpins pathologic tissue niches in Crohn’s disease 148 samples shares epigenetic, states
- GSE283621 Single-cell multi-omics analysis identifies metabolism-linked epigenetic reprogramming as a driver of therapy-resistant medulloblastoma 79 samples shares single-cell, epigenetic
- GSE327819 Single-molecule, single-cell profiling of linked chromatin states [Bulk_CoCUT&Tag] 35 samples shares single-cell, states
- GSE324206 Spatially-Resolved Multiomic Atlas of Leiomyosarcoma Identifies Two Clinically Relevant Epigenetically-Driven Cell States [scMultiomic] 32 samples shares epigenetic, states
- GSE315928 Differential T cell clonal dynamics underlie outcomes to frontline chemoimmunotherapy in advanced gastric cancer [paired scRNA/TCR-seq] 94 samples shares tcr
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
- GSE279619 SETD2 loss-of-function uniquely sensitizes cells to epigenetic targeting of NSD1-directed H3K36 methylation. 124 samples shares epigenetic
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.