ArticleGenomics, proteomics & bioinformatics2024
Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation.
Article in Genomics, proteomics & bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Single-cell analysis of HIV expression and integration sites reveals robust viral expression across diverse chromatin environments.Microbiology spectrum · 2026Article
- Single cell multiomic analysis of the impact of Delta-9-tetrahydrocannabinol on HIV infected CD4 T cells.Journal of cannabis research · 2026Article
- Single-cell characterization of the gastrointestinal HIV reservoir reveals heterogeneous cellular phenotypes.The Journal of clinical investigation · 2026Article
- Astragalus polysaccharide promotes latent HIV-1 reactivation via exosome-mediated modulation of the PI3K/AKT/NF-κB axis.Extracellular vesicles and circulating nucleic acids · 2026Article
- Using single cell technologies to understand HIV latency models.Current opinion in HIV and AIDS · 2025Review
- Article
- Advances in Single-Cell Sequencing for Infectious Diseases: Progress and Perspectives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Advancements in single-cell techniques for examining the HIV reservoir: pathways to a cure.mBio · 2025Review
- Integrator complex subunit 12 knockout overcomes a transcriptional block to HIV latency reversal.eLife · 2025Article
- A targeted CRISPR screen identifies ETS1 as a regulator of HIV-1 latency.PLoS pathogens · 2025Article
- HIV-1 and Artificial Intelligence: From Molecular Insight to Population Impact.Journal of AIDS and HIV treatment · 2025Article
- The role of Tat in HIV latency and reactivation.Frontiers in immunology · 2025Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Despite the success of antiretroviral therapy, human immunodeficiency virus (HIV) cannot be cured because of a reservoir of latently infected cells that evades therapy. To understand the mechanisms of HIV latency, we employed an integrated single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq) approach to simultaneously profile the transcriptomic and epigenomic characteristics of ∼ 125,000 latently infected primary CD4+ T cells after reactivation using three different latency reversing agents. Differentially expressed genes and differentially accessible motifs were used to examine transcriptional pathways and transcription factor (TF) activities across the cell population. We identified cellular transcripts and TFs whose expression/activity was correlated with viral reactivation and demonstrated that a machine learning model trained on these data was 75%-79% accurate at predicting viral reactivation. Finally, we validated the role of two candidate HIV-regulating factors, FOXP1 and GATA3, in viral transcription. These data demonstrate the power of integrated multimodal single-cell analysis to uncover novel relationships between host cell factors and HIV latency.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.