Single-Cell RNA-Seq Reveals Transcriptional Heterogeneity in Latent and Reactivated HIV-Infected Cells
Monica Golumbeanu,
Sara Cristinelli,
Sylvie Rato,
Miguel Munoz,
Matthias Cavassini,
Niko Beerenwinkel,
Angela Ciuffi
Affiliations
Monica Golumbeanu
Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland; SIB Swiss Institute of Bioinformatics, Basel 4058, Switzerland
Sara Cristinelli
Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne 1011, Switzerland
Sylvie Rato
Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne 1011, Switzerland
Miguel Munoz
Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne 1011, Switzerland
Matthias Cavassini
Service of Infectious Diseases, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne 1011, Switzerland
Niko Beerenwinkel
Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland; SIB Swiss Institute of Bioinformatics, Basel 4058, Switzerland; Corresponding author
Angela Ciuffi
Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne 1011, Switzerland; Corresponding author
Summary: Despite effective treatment, HIV can persist in latent reservoirs, which represent a major obstacle toward HIV eradication. Targeting and reactivating latent cells is challenging due to the heterogeneous nature of HIV-infected cells. Here, we used a primary model of HIV latency and single-cell RNA sequencing to characterize transcriptional heterogeneity during HIV latency and reactivation. Our analysis identified transcriptional programs leading to successful reactivation of HIV expression. : HIV latency hampers HIV cure. The shock-and-kill strategy aims at reactivating HIV expression to purge the latent reservoir of HIV-infected cells. However, latently infected cells do not respond equally to stimulation. Golumbeanu et al. use single-cell RNA-seq to characterize cell heterogeneity and identify transcriptional features leading to reactivation success. Keywords: scRNA-seq, transcriptome, heterogeneity, inducible cell, signature, single cell, HIV, latency, reactivation, cure
