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

Golumbeanu, Monica; Cristinelli, Sara; Rato, Sylvie; Muñoz, Miguel; Cavassini, Matthias; Beerenwinkel, Niko; Ciuffi, Angela

doi:10.1016/j.celrep.2018.03.102

Cited by 95 publications

(110 citation statements)

References 52 publications

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“…Furthermore, these investigators identified a transcriptional signature that distinguished these clusters. This signature contains some of the same genes we observe as being associated with HIV gene expressing in our study, such as IL-32, GAPDH, HLA-E, and CD96, but also many different genes (Golumbeanu et al, 2018). …”

Section: Discussionmentioning

confidence: 57%

“…; Bolton et al, 2017; Martrus et al, 2016). Two recent publications have also examined HIV latency using scRNA-seq of a primary cell model (Golumbeanu et al, 2018) and from sorted patient samples (Cohn et al, 2018), yielding important insights. In particular, Golumbeanu et al (2018) observed two clusters of cells in a primary cell model system that exhibited distinct transcriptional phenotypes, associated with different levels of viral gene expression.…”

Section: Discussionmentioning

confidence: 99%

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Single-Cell Analysis of Quiescent HIV Infection Reveals Host Transcriptional Profiles that Regulate Proviral Latency

et al. 2018

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SUMMARY A detailed understanding of the mechanisms that establish or maintain the latent reservoir of HIV will guide approaches to eliminate persistent infection. We used a cell line and primary cell models of HIV latency to investigate viral RNA (vRNA) expression and the role of the host transcriptome using single-cell approaches. Single-cell vRNA quantitation identified distinct populations of cells expressing various levels of vRNA, including completely silent populations. Strikingly, single-cell RNA-seq of latently infected primary cells demonstrated that HIV downregulation occurred in diverse transcriptomic environments but was significantly associated with expression of a specific set of cellular genes. In particular, latency was more frequent in cells expressing a transcriptional signature that included markers of naive and central memory T cells. These data reveal that expression of HIV proviruses within the latent reservoir are influenced by the host cell transcriptional program. Therapeutic modulation of these programs may reverse or enforce HIV latency.

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Section: Discussionmentioning

confidence: 57%

Section: Discussionmentioning

confidence: 99%

Single-Cell Analysis of Quiescent HIV Infection Reveals Host Transcriptional Profiles that Regulate Proviral Latency

et al. 2018

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“…6C). infected human primary CD4+ T cells [11,16,81,82]. scRNA-Seq, however, has been technically challenging, requiring single cell isolation, RNA amplification and complex data analysis.…”

Section: High Transcriptional Variability Of Individual Vdnas In the mentioning

confidence: 99%

Cell-to-cell and genome-to-genome variability of Adenovirus transcription tuned by the cell cycle

Suomalainen

Prasad

Kannan

et al. 2020

Preprint

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These authors contributed equally to this work. Abstract In clonal cultures, not all cells are equally susceptible to virus infection. Underlying mechanisms of infection variability are poorly understood. Here, we developed imagebased single cell measurements to scrutinize the heterogeneity of adenovirus (AdV) infection. AdV delivers, transcribes and replicates a linear double-stranded DNA genome in the nucleus. We measured the abundance of viral transcripts by singlemolecule RNA fluorescence in situ hybridization (FISH), and the incoming ethynyldeoxy-cytidine (EdC)-tagged viral genome by copper(I)-catalyzed azide-alkyne cycloaddition (click) reaction. The early transcripts increased from 2-12 hours, the late ones from 12-23 hours post infection (pi), indicating distinct accumulation kinetics. Surprisingly, the expression of the immediate early transactivator gene E1A only moderately correlated with the number of viral genomes in the cell nucleus, although the incoming viral DNA remained largely intact until 7 hours pi. Genome-to-genome heterogeneity was found at the level of viral transcription, as indicated by colocalization with the large intron containing early region E4 transcripts, uncorrelated to the multiplicity of incoming genomes in the nucleus. In accordance, individual genomes exhibited heterogeneous replication activity, as shown by single-strand DNA-FISH and immunocytochemistry. These results indicate that the variability in viral gene expression and replication are not due to defective genomes but due to host cell heterogeneity. By analyzing the cell cycle state, we found that G1 cells exhibited the highest E1A expression, and significantly increased the correlation between E1A expression and viral genome copy numbers. This combined imagebased single molecule procedure is ideally suited to explore the cell-to-cell variability in viral infection, including transcriptional activators and repressors, RNA splicing mechanisms, and the impact of the 3-dimensional nuclear topology on gene regulation. 3 Author SummaryAdenoviruses (AdV) are ubiquitous pathogens in vertebrates. They persist in infected people, and cause unpredictable outbreaks, morbidity and mortality across the globe.Here we report that the common human AdV type C5 (AdV-C5) gives rise to considerable infection variability at the level of single cells in culture, and that a major underlying reason is the cell-to-cell heterogeneity. By combining sensitive single molecule in situ technology for detecting the incoming viral DNA and newly synthesized viral transcripts we show that viral gene expression is heterogeneous between infected human cells, as well as individual genomes. We report a moderate correlation between the number of viral genomes in the nucleus and immediate early E1A transcripts. This correlation is increased in the G1 phase of the cell cycle, where the E1A transcripts were found to be more abundant than in any other cell cycle phase. Our results demonstrate the importance of cell-to-cell variability measurements for understand...

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“…Single cell RNA-seq technology is transforming the study of cellular heterogeneity 1,2 , differentiation 3,4 , and cellular response to stress and stimulation 5,6,7 . Gene expression levels in tens of thousands of single cells are now routinely measured in a single scRNA-seq experiment 8 and more scRNA-seq datasets are becoming available each day.…”

Section: Introductionmentioning

confidence: 99%

Alignment of single-cell RNA-seq samples without over-correction using kernel density matching

Chen

Zhan

et al. 2020

Preprint

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Single-cell RNA sequencing (scRNA-seq) technology is poised to replace bulk cell RNA sequencing for most biological and medical applications as it allows users to measure gene expression levels in a celltype-specific manner. However, data produced by scRNA-seq often exhibit batch effects that can be specific to a cell-type, to a sample, or to an experiment, which prevent integration or comparisons across multiple experiments. Here, we present Dmatch, a method that leverages an external expression atlas of human primary cells and kernel density matching to align multiple scRNA-seq experiments for downstream biological analysis. Dmatch facilitates alignment of scRNA-seq datasets with cell-types that may overlap only partially, and thus allows integration of multiple distinct scRNA-seq experiments to extract biological insights. In simulation, Dmatch compares favorably to other alignment methods, both in terms of reducing sample-specific clustering, and in terms of avoiding over-correction. When applied to scRNA-seq data collected from clinical samples in a healthy individual and five autoimmune disease patients, Dmatch enabled cell-type-specific differential gene expression comparisons across biopsy sites and disease conditions, and uncovered a shared population of pro-inflammatory monocytes across biopsy sites in RA patients.We further show that Dmatch increases the number of eQTLs mapped from population scRNA-seq data.Dmatch is fast, scalable, and improves the utility of scRNA-seq for several important applications. Dmatch is freely available online (https: // qzhan321. github. io/ dmatch/ ).

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

Cited by 95 publications

References 52 publications

Single-Cell Analysis of Quiescent HIV Infection Reveals Host Transcriptional Profiles that Regulate Proviral Latency

Single-Cell Analysis of Quiescent HIV Infection Reveals Host Transcriptional Profiles that Regulate Proviral Latency

Cell-to-cell and genome-to-genome variability of Adenovirus transcription tuned by the cell cycle

Alignment of single-cell RNA-seq samples without over-correction using kernel density matching

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