GEO series

Single cell characterization of the gastrointestinal HIV reservoir reveals heterogeneous cellular phenotypes.

GSE313894 Homo sapiens (human) Single-cell RNA-seq 68 samples Submitted 2025/12/17 Platform GPL24676Platform GPL34281

At a glance

Single-cell RNA-seq · human · 68 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: 68 samples: 48 distinct titles, 11 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.

Summary (written by the submitters)

Human gastrointestinal (GI) tissues are a major site of HIV-1 viral persistence, but the nature of the GI reservoir remains poorly described. To characterize the GI HIV reservoir, we profiled cells from GI tissue and matched peripheral blood mononuclear cells from ten people with HIV (PWH) on antiretroviral therapy (ART) using single cell RNA sequencing.

Read the full summary ↓

We identified distinct compartment-specific patterns of gene expression, highlighting key differences between blood and colon CD4 T cell populations. vRNA+ cells from both blood and GI tissue were heterogeneous and found in multiple subtypes of CD4 T cells, although vRNA+ cells were particularly enriched in cells with Th17 or Treg17 phenotypes. Transcriptomic comparison of HIV vRNA+ and vRNA- T cells revealed 116 differentially expressed genes that were associated with HIV infection including ZBED2, MAF and IL17F. These data provide novel information regarding the GI-resident HIV reservoir and suggest that compartment-specific patterns of gene expression are associated with HIV infection.

This dataset

Open GSE313894 in finder → Find more human RNA-seq datasets →

Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE313894_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA1387217 and SRA study SRP655567. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

48 distinct titles, 11 of them repeated

Donor_2,Colon,total,PMAiIL2, ×5 Donor_7,Colon,CD4,PMAiIL2, ×4 Donor_10,Colon,total,PMAiIL2, ×4 Donor_2,Colon,total,Untreated, ×3 Donor_5,Colon,CD4,PMAiIL2, ×3 Donor_3,Colon,total,Untreated, ×2 Donor_3,Colon,total,PMAiIL2, ×2 Donor_4,Colon,CD4,PMAiIL2, ×2 +3 more

How we read this: Read from 68 sample titles: 48 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.

Samples in this study

GSM9377203 Donor_1,Colon,total,Untreated,rep1
GSM9377204 Donor_1,Colon,total,PMAiIL2,rep1
GSM9377205 Donor_1,PBMC,total,Untreated,rep1
GSM9377206 Donor_1,PBMC,total,PMAiIL2,rep1
GSM9377207 Donor_2,Colon,total,Untreated,rep1
GSM9377208 Donor_2,Colon,total,Untreated,rep2
GSM9377209 Donor_2,Colon,total,Untreated,rep3
GSM9377210 Donor_2,Colon,total,PMAiIL2,rep1
GSM9377211 Donor_2,Colon,total,PMAiIL2,rep2
GSM9377212 Donor_2,Colon,total,PMAiIL2,rep3
GSM9377213 Donor_2,Colon,total,PMAiIL2,rep4
GSM9377214 Donor_2,Colon,total,PMAiIL2,rep5
GSM9377215 Donor_2,PBMC,total,Untreated,rep1
GSM9377216 Donor_2,PBMC,total,PMAiIL2,rep1
GSM9377217 Donor_3,Colon,total,Untreated,rep1
GSM9377218 Donor_3,Colon,total,Untreated,rep2
GSM9377219 Donor_3,Colon,total,PMAiIL2,rep1
GSM9377220 Donor_3,Colon,total,PMAiIL2,rep2
GSM9377221 Donor_3,PBMC,total,Untreated,rep1
GSM9377222 Donor_3,PBMC,total,PMAiIL2,rep1
GSM9377223 Donor_4,Colon,CD4,Untreated,rep1
GSM9377224 Donor_4,Colon,CD4,PMAiIL2,rep1
GSM9377225 Donor_4,Colon,CD4,PMAiIL2,rep2
GSM9377226 Donor_4,PBMC,CD4,Untreated,rep1
GSM9377227 Donor_4,PBMC,CD4,Untreated,rep2
GSM9377228 Donor_4,PBMC,CD4,PMAiIL2,rep1
GSM9377229 Donor_5,Colon,CD4,Untreated,rep1
GSM9377230 Donor_5,Colon,CD4,PMAiIL2,rep1
GSM9377231 Donor_5,Colon,CD4,PMAiIL2,rep2
GSM9377232 Donor_5,Colon,CD4,PMAiIL2,rep3
GSM9377233 Donor_5,PBMC,CD4,Untreated,rep1
GSM9377234 Donor_5,PBMC,CD4,PMAiIL2,rep1
GSM9377235 Donor_6,Colon,CD4,Untreated,rep1
GSM9377236 Donor_6,Colon,CD4,PMAiIL2,rep1
GSM9377237 Donor_6,Colon,CD4,PMAiIL2,rep2
GSM9377238 Donor_6,PBMC,CD4,Untreated,rep1
GSM9377239 Donor_6,PBMC,CD4,PMAiIL2,rep1
GSM9377240 Donor_7,Colon,CD4,Untreated,rep1
GSM9377241 Donor_7,Colon,CD4,PMAiIL2,rep1
GSM9377242 Donor_7,Colon,CD4,PMAiIL2,rep2

+ 28 more — browse all 68 samples with per-sample file links →

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