GEO series

Single cell transcriptomic analysis of acute and early ART-treated HIV-1-infected CD4+T cells from a reservoir-marking humanized mouse model

GSE236107 Homo sapiens (human) Single-cell RNA-seq 14 samples Submitted 2024/12/31 Platform GPL25526Platform GPL24676

At a glance

Single-cell RNA-seq · human · 14 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: 14 samples: 13 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.

Summary (written by the submitters)

Human immunodeficiency virus (HIV-1) infects and depletes CD4+ T helper cells and can persist within these cells in a latent state during treatment with effective antiretroviral therapy (ART). A latent viral reservoir represents a major barrier to a cure for HIV-1 infection. A longstanding obstacle to understanding the reservoir has been the inability to identify and characterize latent cells without activating them and disrupting their resting state.

Read the full summary ↓

To address this challenge, we have developed an HIV-1-induced lineage tracing (HILT) humanized mouse model that irreversibly marks HIV-infected cells. The system employs a genetically encoded Cre-lox switch transduced into hematopoietic stem cells which are transplanted into immunodeficient mice. Infection of CD4+ T cells in HILT mice with a Cre-expressing HIV clone irreversibly marks infected cells by switching dsRedExpress2 to EGFP expression, a phenotype that is maintained in cell harboring proviruses that are transcriptionally silent during ART. Single cell RNA sequencing (scRNAseq) of infected T cells in the presence or absence of ART reveals HIV infection in diverse CD4+ T cell transcriptional lineages indicating that infected cells with active or suppressed transcription exist in many different T cell lineages. We examined genetic pathways that are more frequently altered in infected cells when compared with uninfected bystander cells. A comparison of pathways that are regulated in opposite directions in acute versus early ART-treated (persistent) infection identified EIF2α-, sirtuin-, and ILK-signaling pathways which contain known transcriptional regulators of HIV transcription. Lastly, we find that resveratrol, a drug that impact sirtuin pathways can influence latency establishment in an acute infection setting.

This dataset

Open GSE236107 in finder → Find more 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 GSE236107_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 PRJNA989047 and SRA study SRP446498. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

13 distinct titles, 1 of them repeated

Mouse_A3_dsRed ×2

How we read this: Read from 14 sample titles: 13 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

GSM7518379 Mouse_A1_Mixed
GSM7518380 Mouse_A2_dsRed
GSM7518381 Mouse_A2_GFP
GSM7518382 Mouse_A3_dsRed1
GSM7518383 Mouse_A3_dsRed2
GSM7518384 Mouse_A3_GFP
GSM7518385 Mouse_T1_dsRed
GSM7518386 Mouse_T1_GFP
GSM7518387 Mouse_T2_GFP
GSM7518388 Mouse_T3_Mixed
GSM7518389 Mouse_U1_dsRed
GSM7518390 Mouse_U1_Unmarked
GSM7518391 Mouse_U2_dsRed
GSM7518392 Mouse_U2_Unmarked

Datasets on a similar topic

GSE239704 NF-κB transcription factors RelA and c-Rel selectively control CD4+ T-cell function in multiple sclerosis and cancer 34 samples shares cd4
GSE292898 Isolation and characterization of an islet-associated CD45- MHC class II+ cells using single cell RNA sequencing. 11 samples shares cd4
GSE249159 scRNA-seq of CD4+ T cells from mouse allergic airway inflammation model and human patients with eosinophilic chronic rhinosinusitis (ECRS) 10 samples shares cd4
GSE261475 Engineered CD47 protects T cells for enhanced antitumor immunity 8 samples shares cd4
GSE295436 Hepatic leukemia factor directs tissue residency of proinflammatory CD4+ T cells 22 samples shares cd4

Search all RNA-seq datasets in GEO →

Share this dataset

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.