Dataset of "Single-Cell RNA-Seq Reveals Transcriptional Heterogeneity in Latent and Reactivated HIV-infected Cells"

Detailed quantitative analysis of GFP expression in SAHA and TCR-treated cells & Computational analysis of bulk and single-cell RNA-Seq data.

Detailed quantitative analysis of GFP expression in SAHA and TCR-treated cells.

Cells were prepared for single cell analysis at the Genome Technology Facility (GTF) of the University of Lausanne. Cells were loaded on Fluidigm C1 IFC plates (5-10 μm), with run ID smart33, smart34 and smart35, corresponding to untreated, SAHA- and TCR-treated conditions respectively. After single cell capture on the Fluidigm C1 IFC plate, each chamber was inspected visually by microscopy and pictures were captured with a Zeiss Axiovert 200 M fluorescence microscope equipped with a Roper Scientific CoolSnap HQ camera using a Plan-Neofluar 10X lens (smart34 run) or 20X lens (for smart35 run). For each capture chamber, pictures in bright field and FITC channel were taken with the MetaMorph 6.3 software. Picture analysis was then performed using ImageJ 1.50b software (open access software: website). Brightness and contrast were adjusted for qualitative assessment of the pictures.

Computational analysis of bulk and single-cell RNA-Seq data.

Upon bulk or single cell isolation, RNA extraction and library preparation was performed according to Illumina protocols. Bulk and single-cell RNA-Seq data analysis are detailed here.

Linked to the paper published in Cell Reports (doi:10.1016/j.celrep.2018.03.102): 

Single-Cell RNA-Seq Reveals Transcriptional Heterogeneity in Latent and Reactivated HIV-infected Cells

Despite effective treatment, HIV can persist in latent reservoirs, which represent a major obstacle towards 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.