Research Article

Microbiology and Infectious Disease

Single cell analysis reveals human cytomegalovirus drives latently infected cells towards an anergic-like monocyte state

Department of Molecular Genetics, Weizmann Institute of Science, Israel
Department of Medicine, Addenbrooke's Hospital, University of Cambridge, United Kingdom
Sydney Cellular Therapies Laboratory, Westmead, Australia
Blood and Bone Marrow Transplant Unit, Westmead Hospital, Australia
Discipline of Infectious Diseases and Immunology, Faculty of Medicine and Health, Charles Perkins Centre, University of Sydney, Australia

Jan 22, 2020

Open access
Copyright information

Abstract
eLife digest
Introduction
Results
Discussion
Materials and methods
Data availability
References
Article and author information
Metrics

Abstract

Human cytomegalovirus (HCMV) causes a lifelong infection through establishment of latency. Although reactivation from latency can cause life-threatening disease, our molecular understanding of HCMV latency is incomplete. Here we use single cell RNA-seq analysis to characterize latency in monocytes and hematopoietic stem and progenitor cells (HSPCs). In monocytes, we identify host cell surface markers that enable enrichment of latent cells harboring higher viral transcript levels, which can reactivate more efficiently, and are characterized by reduced intrinsic immune response that is important for viral gene expression. Significantly, in latent HSPCs, viral transcripts could be detected only in monocyte progenitors and were also associated with reduced immune-response. Overall, our work indicates that regardless of the developmental stage in which HCMV infects, HCMV drives hematopoietic cells towards a weaker immune-responsive monocyte state and that this anergic-like state is crucial for the virus ability to express its transcripts and to eventually reactivate.

eLife digest

Most people around the world unknowingly carry the human cytomegalovirus, as this virus can become dormant after infection and hide in small numbers of blood stem cells (which give rise to blood and immune cells). Dormant viruses still make their host cells read their genetic information and create viral proteins – a process known as gene expression – but they do not use them to quickly multiply. However, it is possible for the cytomegalovirus to reawaken at a later stage and start replicating again, which can be fatal for people with weakened immune systems. It is therefore important to understand exactly how the virus can stay dormant, and how it reactivates.

Only certain infected cells allow dormant viruses to later reactivate; in others, it never starts to multiply again. Techniques that can monitor individual cells are therefore needed to understand how the host cells and the viruses interact during dormant infection and reactivation.

To investigate this, Shnayder et al. infected blood stem cells in the laboratory and used a method known as single-cell RNA analysis, which highlights all the genes (including viral genes) that are expressed in a cell. This showed that in certain cells, the virus dampens the cell defenses, leading to a higher rate of viral gene expression and, in turn, easier reactivation.

Further experiments showed that the blood stem cells that expressed the viral genes were marked to become a type of immune cells known as monocytes. In turn, these infected monocytes were shown to be less able to defend the body against infection, suggesting that latent human cytomegalovirus suppresses the body’s innate immune response.

The reactivation of human cytomegalovirus is a dangerous issue for patients who have just received an organ or blood stem cells transplant. The study by Shnayder et al. indicates that treatments that boost innate immunity may help to prevent the virus from reawakening, but more work is needed to test this theory.

Introduction

Human cytomegalovirus (HCMV) is a prevalent pathogen of the beta-herpesvirus family, infecting the majority of the human population worldwide (Staras et al., 2006). Following primary infection, HCMV persists through the lifetime of the host by establishing latency. In the latent state, no viral progeny is produced but the virus maintains the capacity to reactivate. Reactivation in immunocompromised individuals, such as transplant recipients and HIV patients, leads to severe illness and mortality (Crough and Khanna, 2009). Despite the significant health burden that accompanies HCMV reactivation from latency, to date there is no treatment that targets the latent stage and the processes governing latency and reactivation are far from fully understood.

HCMV has a wide cell tropism within its human host (Mocarski and Shenk, 2013), with most cell types supporting lytic replication (Sinzger et al., 2008a). In contrast, latent infection has so far been characterized in cells of the early myeloid lineage, including CD34+ hematopoietic stem and progenitor cells (HSPCs) and CD14+ monocytes in vivo (Mendelson et al., 1996; Taylor-Wiedeman et al., 1991; von Laer et al., 1995). Since CD14+ monocytes are short-lived cells it has been proposed that the latent reservoir resides in hematopoietic stem cells (HSCs) (Slobedman et al., 2010) and that latent monocytes support viral spread and persistence within the infected host (Stevenson et al., 2014). Latent cells in HCMV seropositive individuals are scarce and were estimated by PCR-driven in situ hybridization, at 1:10,000 to 25,000 with a copy number of 2 to 13 genomes per infected cell (Slobedman and Mocarski, 1999). Using highly sensitive methodologies, such as digital PCR, viral genomes were detected in less than half of seropositive individuals and viral load was estimated at less than 10 genomes in 10,000 cells in most individuals (Jackson et al., 2017; Parry et al., 2016). Due to the scarcity of HCMV-infected cells in the natural context, in vitro HCMV infection of primary cells, mainly HSPCs and monocytes were developed as models. The caveat of these systems is their heterogeneity and the possibility that they may represent dynamic differentiation states. Additionally, although both models are being widely used, the differences between them are not well understood.

It is becoming increasingly evident that the repertoire of viral genes expressed during latent infections is broader than initially appreciated (Cheng et al., 2017; Schwartz and Stern-Ginossar, 2019; Shnayder et al., 2018). Despite low expression of viral transcripts, a number of studies have described infection driven changes in host cells during HCMV latency (Chan et al., 2010; Kew et al., 2017; Reeves et al., 2012; Slobedman et al., 2002; Smith et al., 2007; Smith et al., 2004). Monocyte infection was proposed to promote differentiation to macrophages with specific polarization towards genes that mark M1 phenotype with some atypical attributes (Chan et al., 2008; Smith et al., 2004). On the other hand it was shown that the viral homolog of human interleukin-10, encoded by UL111A, polarizes monocytes into an anti-inflammatory M2 subset (Avdic et al., 2013). The UL7 viral protein was found to bind Fms-like tyrosine kinase three receptor (Flt-3R), inducing differentiation of HSPCs to monocytes and of monocytes to macrophages (Crawford et al., 2018). Finally, analysis of HCMV- infected HSPCs revealed reprogramming of HSPCs into immune-suppressive monocytes (Zhu et al., 2018). Thus, although it is clear that latent HCMV infection affects the differentiation state of infected HSPCs and monocytes, the nature of these effects is still enigmatic and controversial.

The studies to date examining host responses to latent HCMV infection focused on differences between infected and uninfected cells or on the effect of specific viral transcripts. Since the experimental systems for HCMV latency rely on primary immune cell populations, which are heterogeneous, and since infection is variable within the culture, it is likely that analyses of bulk populations could miss important signatures. Single cell-RNA-seq (scRNA-seq) provides a unique opportunity to depict viral and host heterogeneity simultaneously and thus to uncover functional connections between the cellular environment and viral gene expression. Indeed, several recent works that applied single cell transcriptomics, revealed novel insights into the complexity of the host response and cellular permissiveness for a number of well-studied viruses (Douam et al., 2017; Drayman et al., 2019; Galinato et al., 2018; Rato et al., 2017; Russell et al., 2018; Steuerman et al., 2018; Wyler et al., 2019; Zanini et al., 2018).

Using single cell RNA data, we analyzed host determinants that are associated with HCMV latency. In CD14+ monocytes, we identified two cellular cell surface markers, MHCII and its chaperon CD74, whose expression is inversely-correlated with viral transcript levels. We demonstrate these markers allow separating between cells harboring higher and lower viral transcript levels, that these differences are induced by HCMV infection and that the cells exhibiting higher viral transcript levels support more efficient reactivation of HCMV from latency. Using these markers, we show that latently infected cells display an intrinsic weaker immune response state that is important for the viral ability to express its genes and reactivate. Furthermore, analysis of 7500 infected HSPCs revealed very heterogeneous populations, but viral transcripts were only detected in cells expressing monocyte lineage markers, such as CD14. Remarkably, also in these HSPC-derived monocytes, higher viral transcript levels were associated with lower expression of CD74 and reduced immune response gene signature. Taken together, our findings highlight cell surface proteins associated with viral transcript levels and establish that both HSPC and monocyte infection models lead to establishment of HCMV latency in a similar anergic-like state of monocytic cells.

Results

Expression of host genes correlates with viral transcript levels

We have previously performed single cell RNA sequencing (scRNA-seq) on HCMV- infected CD14+ monocytes as a model for studying HCMV latency (Shnayder et al., 2018). We used the Massively Parallel RNA Sequencing (MARS-seq) platform (Jaitin et al., 2014; Paul et al., 2015) to analyze experimentally infected primary CD14+ monocytes at different days post infection (dpi). This approach provided high coverage of cellular and viral transcriptomes, spanning expression of more than 11,000 genes in 3,655 cells. We previously used this dataset to examine the viral transcriptome during HCMV latency, showing it largely mirrors a late lytic viral program, albeit at much lower levels of expression (Shnayder et al., 2018).

An inherent advantage of scRNA-seq is the ability to track viral and host expression within the same cell, thus allowing analysis of viral-host interactions while keeping information on cell-to-cell and infection heterogeneity. Viral transcript levels in the majority of the infected monocyte population was low to undetectable and projection of the cells using t-distributed stochastic neighbor embedding (t-SNE) demonstrated that the cell distribution is determined mainly according to host gene expression variations (Shnayder et al., 2018). Nevertheless, the cells are organized according to viral transcript levels (Shnayder et al., 2018) and Figure 1A), suggesting that much of the differences in host gene expression are associated with variation in viral transcript levels. We therefore calculated Spearman correlation coefficient between the expression of each host gene and the total number of viral transcripts across all cells (Figure 1B). 319 cellular genes showed significant positive or negative correlations with viral gene expression (Z score >2, Figure 1—source data 1). Since a key challenge in transcriptome analysis is to connect between snapshots of gene expression profiles and a functional outcome, we first focused on identifying cell surface markers that exhibited strong association with viral gene expression in the scRNA-seq data as these may allow us to enrich for cells with higher viral transcript levels within the population. Among the strongest co-varying genes were genes encoding for MHC class II (variants HLA-DRB1/HLA-DPB1/HLA-DRA/HLA-DQB1/HLA-DPA1/HLA-DQA1/HLA-DMA/HLA-DRB5/HLA-DQA2 and HLA-DMB) and the transcript of CD74, an MHCII chaperon as well as a cell surface receptor on its own (Bergmann, 2012). Indeed, across the infected monocyte population, the expression level of MHC class II (MHCII, Figure 1C) and CD74 (Figure 1D) showed clear inverse-correlation to viral transcript levels (Figure 1A).

Figure 1 with 2 supplements see all

Download asset Open asset

Cellular gene expression co-vary with viral transcript levels.

(A) t-SNE plot of 3416 latently infected CD14+ monocytes based on host and viral gene expression (Shnayder et al., 2018), colored by percentage of HCMV reads per cell. (B) Distribution of Z scores of the Spearman correlation coefficients between host transcript levels and total HCMV transcript levels across a single population of infected CD14+ monocytes. Black dots mark cells surface markers coding transcripts, blue marks CD74 and red marks transcripts of MHCII (isoforms HLA-RB1/HLA-DPB1/HLA-DRA/HLA-DQB1/HLA-DPA1/HLA-DQA1/HLA-DMA/HLA-DRB5/HLA-DQA2 and HLA-DMB, left to right), gray dots mark all other transcripts. (**C and D**) t-SNE plots of monocytes as presented in A colored coded by their expression levels of MHCII (HLA-RB1/HLA-DPB1/HLA-DRA/HLA-DQB1/HLA-DPA1/HLA-DQA1/HLA-DMA/HLA-DRB5/HLA-DQA2 and HLA-DMB) (C) or CD74 (D) transcripts. (E) Percentage of viral reads measured in all single cells by days post infection (dpi). Error bars represent standard deviation across the single cells.

Figure 1—source data 1 Correlation coefficients and Z scores between host transcript level and total HCMV transcript level across single infected CD14+ monocytes.: https://cdn.elifesciences.org/articles/52168/elife-52168-fig1-data1-v3.xlsx
Download elife-52168-fig1-data1-v3.xlsx

Cell-surface levels of CD74 and MHCII inversely-correlate with viral transcript levels

Viral gene expression levels in latent infection decreases with time in culture, likely due to continuous repression of the viral genome. This time related reduction in viral transcript levels is also apparent in our CD14+ monocytes scRNA-seq data (Figure 1E), thus the increase in CD74 and MHCII expression could be related to time in culture and to only indirectly inversely-correlate with viral transcript levels (Figure 1—figure supplement 1). Indeed, we observed that the expression of both CD74 and MHCII increase over time in culture, both in infected and in uninfected cells (Figure 1—figure supplement 2). We therefore tested whether this inverse-correlation between CD74 and MHCII expression and viral transcript levels is upheld within a single infected population at single time points. To this end, we infected primary CD14+ monocytes with HCMV strain TB40/E-GFP (O'Connor and Murphy, 2012; Sinzger et al., 2008a). At 3 days post infection (dpi) the cells were FACS-sorted according to the cell-surface levels of CD74 and MHCII (Figure 2A and Figure 2—figure supplement 1). RT-qPCR analysis of viral gene expression in these populations confirmed that both CD74^low and MHCII^low monocyte populations express higher levels of viral transcripts compared to their high expressing counterparts (Figure 2B and Figure 2—figure supplement 1). Importantly, the association of higher viral transcript levels with lower cell-surface levels of CD74 was also maintained at 6dpi (Figure 2—figure supplement 2). We next examined if MHCII and CD74 are independent markers or are co-expressed and therefore mark the same population. mRNA expression analysis of CD74 and MHCII cells sorted according to the cell-surface levels of CD74 and MHCII as well as analysis of their co-expression in the scRNA-seq data, confirmed that the two markers are co-expressed and probably can be used to sort similar subpopulations within the infected cell population (Figure 2C and Figure 2—figure supplement 3). We therefore conducted all further experiments using CD74 based sorts. qPCR Analysis of viral DNA levels demonstrated that higher viral transcript levels in CD74^low monocytes are concurrent with higher abundance of viral genomes (Figure 2D) suggesting that differential loads of viral genome templates probably contribute to differential viral transcript levels and therefore to the effect on the host. Finally, we examined CD74 cell surface expression in infected and uninfected cells at different time points post infection but no major changes in CD74 distribution following infection were observed (Figure 2—figure supplement 4).

Figure 2 with 4 supplements see all

Download asset Open asset

CD74 and MHCII cell-surface levels in HCMV- infected monocytes inversely-correlate with viral transcript levels.

(A) HCMV- infected monocytes were FACS sorted according to cell-surface levels of CD74 or MHCII, at 3dpi. High and low gates were determined as highest and lowest 30% of the population, respectively. (**B and C**) Relative expression level of the viral transcripts RNA2.7 and UL138 (B) or MHCII and CD74 transcripts (C), as measured by RT-qPCR in HCMV- infected cells, sorted by either MHCII (left) or CD74 (right) cell-surface levels at 3dpi. (D) Relative abundance of viral DNA in HCMV- infected CD74^high and CD74^low monocytes, at 3dpi as measured by qPCR. Graphs show a representative experiment of 3 biological repeats, error bars reflect standard deviation of 3 measurements. P values as calculated by t-test are indicated.

Changes in CD74 and MHCII expression are induced by infection

There are two alternative explanations for the inverse-correlation between viral transcript levels and CD74 cell-surface levels, several days post infection with HCMV. The first possibility is that viral entry is more efficient in CD74^low monocytes compared to CD74^high monocytes, leading to more incoming viral genomes and higher viral transcript levels. In this case, differences in viral levels between CD74^high and CD74^low monocytes should be evident immediately following viral entry to the cells. An alternative option is that the differential expression of CD74 is driven by HCMV infection. In this case, the viral DNA and RNA levels in early stages of infection should be independent of CD74 cell-surface levels, and at later time points, higher load of virus leads to the observed differences in CD74 expression. To test these possibilities, uninfected freshly isolated CD14+ monocytes were FACS sorted based on CD74 cell-surface levels and then infected separately with TB40E-GFP. At 8 and 72 hr post infection (hpi) viral DNA and RNA were analyzed by qPCR. We confirmed that indeed the CD74^high and CD74^low sorted cells exhibited differences in CD74 transcript levels negating the possibility that the separation is only due to variations associated with the cell surface staining (Figure 3A). No significant differences between viral DNA load (Figure 3B) or viral transcript levels (Figure 3C) in CD74^high and CD74^low monocytes were observed at either 8 or 72hpi, indicating there are no major differences in the efficiency of viral entry between the two populations. Taken together, these results indicate that the observed variation in CD74 cell-surface levels is induced following HCMV infection.

Figure 3

Download asset Open asset

Changes in CD74 expression are induced by infection.

Uninfected primary monocytes were FACS sorted according to cell-surface levels of CD74. Equivalent numbers of CD74^high and CD74^low cells were infected with HCMV and differences in CD74 RNA levels and in viral DNA and RNA levels between these two cell populations were assessed by qPCR. (A) Relative CD74 transcript levels in CD74^high and CD74^low cells at 8hpi. (B) Relative abundance of viral DNA in CD74^high and CD74^low cells at 8hpi and 72hpi. (C) Relative expression level of the viral transcripts UL22A and RNA2.7 in CD74^high and CD74^low cells as measured at 8hpi and 72hpi. Graphs show a representative experiment of 3 biological repeats, error bars reflect standard deviation of 3 measurements. P values were calculated by t-test.

CD74^low monocytes reactivate more efficiently

An important characteristic that defines latent infection is the ability of the virus to reactivate. Therefore, a key challenge in gene expression analysis is to connect between snapshots of gene expression profiles and the infection status of the cells, which is defined by this functional outcome. The identification of cellular cell surface markers that inversely-correlate with viral transcript levels provides a handle to connect between gene expression and the ability of the virus to reactivate. To test the association between viral transcript levels and reactivation efficiency, HCMV- infected primary CD14+ monocytes were sorted by CD74 cell-surface levels, and viral reactivation was induced by two complementary methods; cytokine driven differentiation to dendritic cells (DCs) (Reeves et al., 2005), followed by incubation with an indicator fibroblasts monolayer, or long term co-culturing with fibroblasts. Quantification of GFP positive plaques showed that reactivation was significantly more frequent in CD74^low cells compared to CD74^high cells, in both protocols (Figure 4A and B). Lysed samples of monocytes plated onto indicator fibroblasts produced no plaques, confirming that these infected cells did not produce any detectable infectious virus, consistent with latency prior to reactivation induction. These results demonstrate a functional difference in reactivation efficiency associated with cell-surface CD74 levels as well as with viral transcript levels and genome load.

Figure 4

Download asset Open asset

CD74^low infected monocytes reactivate more efficiently.

HCMV- infected CD14+ Monocytes were sorted according to cell-surface levels of CD74 at 3dpi. Equivalent numbers of cells were plated and (A) differentiated into DCs followed by co-culture with fibroblasts to quantify reactivation events or (B) directly co-cultured with fibroblasts to induce reactivation. Data ais shown as number of infectious centers formed by CD74^high vs. CD74^low cells. Means and error bars (reflecting standard deviation) were generated from 4 independent experiments (A) and 3 independent experiments (B). Dot colors indicate sets from the same experiment. P values as calculated by t-test are indicated.

CD74 can be used to enrich for HCMV harboring CD14+ monocytes from viremic patients

Based on our experimental infection results, we next tested whether CD74 expression on CD14+ monocytes could be used as a cell surface marker that will allow enrichment of cells that contain HCMV genomes in healthy seropositive individuals. We used digital droplet PCR (ddPCR) to detect viral genome loads in CD14+ monocytes from seven seropositive donors. Despite using a highly sensitive platform (Figure 5—figure supplement 1) and detection of several positive events, testing 100,000–400,000 cells per donor did not provide us with levels of detection and reproducibility that allowed relative quantification (Figure 5—figure supplement 2A and B and Supplementary file 1). Recently, it was suggested that latent HCMV resides in a unique B7H4-positive monocyte subset (Zhu et al., 2018). To examine whether we can enrich for a cell population in which we can detect robust levels of HCMV genomes, we collected CD14+ monocytes from 5 healthy seropositive individuals. In all five donors we could not detect a distinct population of B7H4-positive cells (Figure 5—figure supplement 2C). We nevertheless sorted the top 2% and the bottom 70% B7H4 stained cells and measured HCMV genome loads by ddPCR but did not detect higher levels of HCMV genomes in the top 2% B7H4 cells (Figure 5—figure supplement 2D). We therefore next analyzed CD14+ monocytes from seven hematopoietic stem cell transplant (HSCT) recipient samples in which HCMV viremia was detected, for the presence of viral genomes. In three of the tested samples no virus could be detected in CD14+ monocytes (Figure 5—figure supplement 2E). Monocytes from the four additional samples, in which viral genomes were detected (Figure 5—figure supplement 2E), were sorted according to CD74 cell-surface levels (Figure 5A). In agreement with our results in the experimental infection model, CD74 ^low monocytes were significantly enriched for the virus compared to CD74^high monocytes, illustrating that also in this natural infection setting, CD74 expression can be used to enrich for monocytes carrying viral genomes (Figure 5B and C).

Figure 5 with 2 supplements see all

Download asset Open asset

CD74 cell-surface levels allow enrichment of CD14+ monocytes carrying HCMV genomes from viremic patients.

CD14+ monocytes from HSCT recipients with HCMV reactivation were sorted according to CD74 cell-surface levels. In total, 4 samples were tested, originating from 3 different donors (samples 1 and 2 were collected at different time points from the same donor), and viral genome abundance was measured by ddPCR. (A) A representative FACS sort of the cells. The highest and lowest 30% of the population were collected as CD74^high and CD74^low samples. (B) ddPCR results of two representative replicates from a single sample, separated by yellow vertical line. Upper panel shows detection of viral DNA, lower panel reflects detection of host genomes. The magenta line marks the threshold. (C) Quantification of viral genomes in CD74^high and CD74^low cells from four different samples, presented as copies per 10,000 cells. Graph reflects mean and 95% CV of poisson distribution, calculated from 5 technical replicates for each donor. P values as calculated by Fisher test are indicated.

Cells with higher viral load express lower immune-responsive gene signatures

To further characterize the differences in host pathways associated with the variations in viral transcript levels we conducted RNA-seq on latently infected monocytes, sorted according to their CD74 cell-surface levels. As was seen for single viral genes (Figure 2B), we found that on a genome-wide level, viral gene expression is higher in CD74^low cells compared to CD74^high cells (Figure 6A and Figure 6—figure supplement 1A). Furthermore, in accordance with our previous findings (Shnayder et al., 2018) the viral gene expression profile in these cells was correlated with late lytic profile (Figure 6—figure supplement 1B), but viral transcript levels were very low (~0.1% of mRNA reads originated from the virus). We performed differential gene expression analysis comparing between the CD74^low and CD74^high populations. This analysis revealed 113 differentially expressed cellular genes (FDR < 0.05, Figure 6—source data 1). Gene set enrichment analysis (GSEA) show that compared to CD74^low,CD74^high monocytes, which exhibit lower viral transcript levels, are enriched for many immune response related pathways (Figure 6B and Figure 6—source data 2) including adaptive immune response and response to interferon gamma (Figure 6C). Since these cells were extracted from the same culture, these results suggest that monocytes carrying higher viral load are driven towards an intrinsic anergic-like phenotype. Reassuringly, many of the cellular genes and pathways exhibiting higher expression in CD74^high cells were also inversely-correlated with viral transcript levels in the scRNA-seq data (Figure 6D, Figure 6—figure supplement 2 and Supplementary file 2). The CD74^low monocyte population, which exhibits higher viral transcript levels, was enriched in genes that are related to transcription and virus life cycle (Figure 6B and Figure 6—source data 2). These pathways were not significantly correlated with viral expression levels in the single cell dataset, perhaps due to the sparser nature of our single cell measurements, which due to the sampling of limited number of cells may not detect some weaker effects.

Figure 6 with 2 supplements see all

Download asset Open asset

HCMV latency in monocytes is associated with reduced immune-response gene signatures.

RNA-seq was performed on HCMV- infected CD14+ Monocytes that were sorted according to cell-surface levels of CD74 at 3dpi. (A) Normalized viral gene expression in CD74^high and CD74^low cells. P value, calculated using likelihood ratio test on logistic regression of viral reads, is indicated. (B) Summary of gene set enrichment analysis (GSEA) of differential expressed genes identified in RNA-seq analysis of CD74^high and CD74^low cells using annotated GO biological processes and Reactome pathways. (C) Representative pathways from GSEA of genes ranked by their differential expression between CD74^high and CD74^low cells. (D) tSNE plot of scRNA-seq of latent monocytes colored by expression level of the pathways shown in C. (E) Monocyte priming gene set from Velten et al. (2017) analyzed on GSEA. Genes are ranked by their differential expression between CD74^high and CD74^low monocytes. (F) tSNE plot of scRNA-seq of latent monocytes colored by the expression level of the monocyte priming gene set from Velten et al. (2017).

Figure 6—source data 1 DE analysis of RNA-seq on CD74^high and CD74^low cells.: https://cdn.elifesciences.org/articles/52168/elife-52168-fig6-data1-v3.xlsx
Download elife-52168-fig6-data1-v3.xlsx
Figure 6—source data 2 GSEA of genes showing higher expression in CD74^high compared to CD74^low monocytes.: https://cdn.elifesciences.org/articles/52168/elife-52168-fig6-data2-v3.xlsx
Download elife-52168-fig6-data2-v3.xlsx

To decipher the differentiation status of latent monocytes we compared our data to a recently published single cell analysis of lineage commitment during hematopoiesis (Velten et al., 2017). The genes that showed elevated expression in the CD74^high cell population compared to the CD74^low population were enriched for genes associated with monocyte lineage priming (Figure 6E), implying that the cells exhibiting higher viral transcript levels express less commitment markers and therefore may represent a less differentiated state. Significantly, this gene signature of monocyte lineage priming was also inversely-correlated with viral transcript levels in the scRNA-seq data (Figure 6F). To further investigate the cell differentiation state, we analyzed gene sets that were previously associated with M1 or M2 polarization (Gerrick et al., 2018) and found that genes that were more highly expressed in CD74^high monocytes were significantly enriched for genes previously associated with M2 phenotype (Pval = 2.25×10⁻⁸). This suggests that the infected CD14+ monocytes in our culture conditions may be polarized towards a cell-state associated with M2 phenotype, however higher viral transcript levels attenuates this differentiation trajectory.

Inhibition of interferon signaling increases viral gene expression and reactivation

Since a main feature of CD74^low monocytes is intrinsic reduced responsiveness to immune signals, we reasoned that this feature could contribute to the higher ability of the virus to express viral transcripts and eventually reactivate. To test this we examined whether inhibition of interferon signaling affects the expression level of viral transcripts and its ability to reactivate in infected CD14+ monocytes. We used ruxolitinib, a potent and selective Janus kinase (JAK) 1 and 2 inhibitor (Lin et al., 2009) that blocks the signaling downstream of interferon receptors. Treatment with ruxolitinib immediately after infection (3hpi) resulted in a considerable reduction in the levels of interferon-induced genes at 3 and 6dpi (Figure 7A) and in increased expression of essentially all viral transcripts (Figure 7B), in an overall uniform manner (Figure 7—figure supplement 1). In order to test how long after infection blocking interferon signaling still affects viral transcript levels, we applied ruxolitinib at different time points along infection and measured viral transcripts by RNA-seq. Interestingly, although the effect was smaller when the inhibitor was added later along infection, blocking interferon signaling still increased viral transcript levels even when added 3dpi (Figure 7C). These results therefore indicate there is continuous expression of viral genes and at 3dpi viral genes are still transcribed. However, the reduced effect of ruxolitinib with time also points that in addition there is gradual repression of viral gene expression as was also captured in our scRNA-seq analysis (Shnayder et al., 2018 and Figure 1E). Importantly, blocking interferon signaling led to more efficient reactivation of the virus (Figure 7D). This demonstrates that interferon signaling has a major role in repression of viral gene expression in infected monocytes and that the anergic-like state of monocytes exhibiting higher viral transcript levels is likely important for latency maintenance and reactivation.

Figure 7 with 1 supplement see all

Download asset Open asset

Inhibition of interferon signaling increases viral gene expression and promotes reactivation.

(**A and B**) HCMV- infected monocytes were treated at 3hpi with interferon signaling inhibitor, ruxolitinib (ruxo), or left untreated, and analyzed for gene expression level, by RNA-seq. (A) Expression of interferon-induced genes at 3dpi (left) and 6dpi (right). (B) Expression of viral genes at 3dpi (left) and 6dpi (right). (C) Viral gene expression in HCMV- infected monocytes that were treated with ruxo at 1, 2, 3dpi or left untreated was measured by RNA-seq at 5dpi. P values for (**A-C**), calculated using likelihood ratio test on logistic regression of viral reads, are indicated. (D) HCMV- infected monocytes were treated at 3hpi with interferon signaling inhibitor, ruxolitinib (ruxo), or left untreated, and at 6dpi, equivalent numbers of monocytes were co-cultured with fibroblasts to induce reactivation. Viral reactivation in ruxo treated vs. untreated cells was assessed by count of GFP positive plaques formed on the fibroblasts. Means and error bars (reflecting standard deviation) were calculated from 2 independent biological repeats. P value as calculated using likelihood ratio test on Poisson regression of positive plaque events is indicated.

Viral transcript levels in HCMV-infected CD34+ HSPCs are associated with priming towards the monocyte lineage and reduced immune-response

We have previously also performed scRNA-seq analysis of HCMV- infected CD34+ HSPCs at 4dpi (Shnayder et al., 2018). To gain insight into the effects of HCMV on HSPC differentiation, we inferred differentiation trajectories using Monocle, a strategy that allows placing single cells along a pseudotime continuum based on their gene expression (Trapnell et al., 2014; Figure 8A). The cells expressing HCMV transcripts were clustered in a region that exhibited the latest pseudotime (i.e the most differentiated state, Figure 8B). Remarkably, although we infected bone marrow derived CD34+ HSPCs, viral transcripts were expressed only in cells expressing monocyte lineage markers (Velten et al., 2017), such as IRF7, IRF8 (Figure 8C) and CD14 (Figure 8D). Taking into account that it was previously shown that HCMV infects multipotent hematopoietic stem cells (Goodrum et al., 2004), these results indicate that HCMV induces differentiation of infected HSPCs towards the monocyte lineage. Interestingly, more detailed analysis of the distinct group of cells that expressed monocyte markers, revealed that this group could be split into two close yet distinct clusters, with viral reads being detected in much more cells in one of the clusters. Interestingly, the cluster showing higher viral transcript levels exhibited lower expression of CD74 (Pval = 1.73×10⁻⁹⁵, Figure 8E and F) as well as gene signatures associated with lower immune response to interferon gamma and lower adaptive immune response (Pval <0.001 and Pval = 0.003, respectively, Figure 8G and H). Thus, cells within the HSPC population that exhibit the highest viral gene expression belong to the CD14+ monocyte cell lineage and are associated with similar anergic-like signatures as infected monocytes.

Figure 8

Download asset Open asset

Viral transcript levels in HCMV-infected CD34+ HSPCs is associated with priming towards the monocyte lineage and with reduced immune-response.

Pseudotime trajectory of single cell transcriptomes from HCMV- infected CD34+ HSPCs (n = 7,634, Shnayder et al., 2018) colored by pseudotime (A), viral transcript levels (B), expression of monocyte priming gene signature (Velten et al., 2017) (C) and CD14 expression (D). Zoom-in representation of the pseudotime trajectory in A-D, on the region exhibiting higher viral transcript level (top right group of cells), colored by expression of CD74 (E), viral transcript levels (F), expression level of the cellular response to interferon gamma pathways (G) and of the adaptive immune response pathway (H).

Discussion

HCMV establishes latency in its host in progenitor cells of the myeloid system (Mendelson et al., 1996; Taylor-Wiedeman et al., 1991; von Laer et al., 1995). Nevertheless, in cell culture experimental systems it is apparent that not all cells have the ability to reactivate, indicating that there are variations in the levels or dynamics of latent infection. This heterogeneity means that bulk assays, comparing infected and uninfected cell populations can capture host responses to HCMV infection but likely miss specific responses in the group of cells in which latency is established, and will enable reactivation down the line.

Recent single cell RNA-seq data portray low-level expression of a broad spectrum of canonical viral lytic genes during HCMV latent infection of various cells of the hematopoietic system (Galinato et al., 2018; Shnayder et al., 2018). Our analysis of latent HCMV- infected CD14+ monocytes revealed a continuous population. Essentially, all of the cells were infected; however, they varied in the levels of viral transcripts. We exploited the host and viral heterogeneity, revealed simultaneously in scRNA-seq data, to look for associations between viral transcript levels and the human transcriptome. We found that among the genes that were highly inversely-correlated with viral transcript levels, is a group of genes encoding MHCII as well as the cell surface marker CD74, which serves as an MHCII chaperone. The finding that there are host genes that show specific correlation with viral transcript levels indicates that there is direct interaction between the host and the virus during latent infection- either there is a preference of the virus to infect specific cell types or that the virus actively affects the host transcriptome. Indeed, these markers could be used to enrich for cells with increased viral genome load and viral transcript levels. Importantly, this was supported by analysis of CD14+ monocytes from viremic patients, which also shows a clear inverse-correlation between CD74 cell-surface levels and viral genome load. These results are in line with previous findings showing that human, murine and rat cytomegalovirus down regulate the surface expression of MHCII molecules in cells of the myeloid lineage (Baca Jones et al., 2009; Elder et al., 2019; Lee et al., 2011; Slobedman et al., 2002; Yunis et al., 2018). In addition, CIITA, a transcription factor that regulates CD74 and MHCII, was shown to be down regulated by HCMV (Lee et al., 2011).

The inverse-association between the expression of CD74 and MHCII and viral transcript levels could be related to differences in permissivity for the virus. By cell sorting according to CD74 levels prior to infection, we show that this is likely not the case; instead, these changes seem to be induced by viral infection. Moreover, we see that higher viral transcript levels in CD74^low cells is also accompanied by higher abundance of viral genomes, indicating that the level of viral transcripts is determined, at least initially, by the amount of incoming genomes, and this contributes to the extent of the effect on the host. Importantly, reactivation from HCMV latency happens in a very small population of cells even in experimental systems where the majority of cells are infected. We show here that CD74^low monocytes, which carry higher viral transcript and viral genome levels, reactivate more efficiently. This indicates that the ability of monocytes to reactivate is associated with viral transcript expression and that the cells carrying higher viral loads in these models are functionally the latent cell population, as they are more likely to reactivate. Recently, B7H4 was suggested as a marker for monocytes with higher levels of HCMV genomes from HCMV seropositive individuals (Zhu et al., 2018), however, we could not detect expression of B7H4 mRNA in any of our RNA-seq samples. Moreover, by staining for B7H4, we could not identify a distinct B7H4 positive monocyte population and did not detect higher levels of HCMV genomes in the top 2% B7H4 sorted cells. According to available human datasets, B7H4 is indeed not expressed in healthy monocytes (Blood Atlas, 2019), however, these differences may stem from sampling of different donors or due to other variables related to the isolation of the cells.

Our analysis indicates that the cells that have higher expression of viral genes are less immune-responsive including reduced response to interferons. Since the CD74^high and CD74^low monocytes we analyzed grew in the same culture, they were exposed to the same immune-extrinsic signals. Hence, this difference is intrinsic to specific cells, and may be actively induced by the virus. This is supported by a recent report showing that interferon induced genes are downregulated during latent infection of CD14+ monocytes (Elder et al., 2019). This anergic-like state is functionally related to the ability of the virus to express viral transcripts and to reactivate, as inhibiting the response to interferons resulted in increased expression of viral transcripts as well as increased reactivation efficiency. The importance of the role interferon plays in latent infection is further supported by the fact that CMV replication can be inhibited in otherwise permissive cells by treatment with interferon beta (Dağ et al., 2014). Similarly, extrinsic interferon stimuli inhibited HSV-1 reactivation from latency in neuronal cells (Linderman et al., 2017). Importantly, blocking interferon signaling increases viral gene expression even when done up to 3dpi, suggesting that there is continuous viral gene expression. However, the effect was smaller when the inhibitor was added later along infection, indicating a gradual repression of viral gene expression with time. Overall, these findings indicate that a major aspect of the maintenance of latency and of the ability to reactivate at the cellular level is a balance between opposing forces which also affect each other- the intrinsic immune response, specifically the interferon pathway, and viral transcript levels. Future work will have to delineate the mechanisms by which these immune response pathways control general viral transcription and how viral transcripts and viral proteins modulate the immune response during latent infection.

Previous works have described changes in the differentiation state of monocytes in response to infection or to specific viral genes (Avdic et al., 2013; Chan et al., 2008; Smith et al., 2004). The use of the CD74 marker, allowed us to focus on differentiation processes unique to the CD14+ monocytes with higher viral transcript levels and higher reactivation efficiencies. We show that higher viral transcript levels are associated with less expression of monocyte priming signature and lower expression of a gene signature associated with M2 phenotype. It was previously shown that the HCMV encoded IL-10 homolog polarizes monocytes towards an M2c macrophage phenotype (Avdic et al., 2013). It is possible that the total population is indeed polarized towards this direction; however, this process may be inhibited in cells with higher viral transcript levels. These findings may suggest that the virus promotes attenuation of differentiation processes that monocytes are undergoing in culture and in the blood (Patel et al., 2017).

Examining the differentiation state in HSPCs following infection is far more complicated than in monocytes, as CD34+ HSPCs are a mix of pluripotent cells as well as progenitor cells in different stages of lineage commitment (Velten et al., 2017). By applying single cell trajectory analysis, which allows recovery of gene expression kinetics of differentiating cells (Trapnell et al., 2014), and aligning our data with data of lineage commitment during hematopoiesis (Velten et al., 2017), we show that cells containing detectable viral transcripts belong largely to one specific population- cells primed towards the monocyte lineage. From our data, we cannot determine whether this is due to preferential infection by HCMV of monocyte lineage committed cells within the HSPC compartment or whether HCMV infects multipotent cells, which are then either preferentially skewed towards the monocytic lineage or viral gene expression is initiated only when cells start to differentiate in the monocytes lineage. However since it was previously demonstrated that multipotent cells are infected with HCMV (Goodrum et al., 2004), the latter options are more likely. Previous studies demonstrated that HCMV infection leads to an increase in monocyte markers (Zhu et al., 2018) and similar results were shown specifically for the viral gene UL7 (Crawford et al., 2018). Our results are in line with these studies and show directly and in an unbiased manner that several days post infection, viral gene expression can be found only in cells in the monocyte lineage. Moreover, within the monocyte lineage primed cells, the cells exhibiting higher viral transcript levels show the same markers and characteristics as we found when infecting CD14+ monocytes, primarily lower expression of CD74 and weaker immune responsiveness. This is especially interesting as it suggests that regardless of the developmental stage in which HCMV infects, the end point, a few days after infection, are cells with very similar characteristics.

Overall, we use here single cell data to pinpoint the characteristics of the latently infected cells in an unbiased manner. Our analyses indicate that HCMV drives human HSPCs and monocytes into a monocyte state characterized by anergic-like gene signature. These findings shed light on the characteristics of the latent reservoir, which may help in the effort of developing strategies to eradicate the latently infected cells.

Materials and methods

Key resources table

Reagent type (species) or resource	Designation	Source or reference	Identifiers	Additional information
Cell line (Homo-sapiens)	Primary human foreskin fibroblasts (HFF)	ATCC	Cat#: CRL-1634
Strain, strain background (HCMV virus)	TB40E-GFP	O'Connor and Murphy, 2012; Sinzger et al., 2008a
Antibody	anti-human APC-CD74 (Mouse monoclonal)	Miltenyi Biotec	Clone: 5–329 RRID:AB_2659190	1:200
Antibody	anti-human PE-HLA-DR, DP, DQ (recombinant)	Miltenyi Biotec	clone: REA332 RRID:AB_2652177	1:200
Antibody	anti-human FITC-CD14 (Mouse monoclonal)	Miltenyi Biotec	Clone: TÜK4 RRID:AB_244303	1:200
Antibody	anti-human APC-B7H4 (Mouse monoclonal)	Biolegend	clone: MIH43 RRID:AB_2562580	1:200
Antibody (isotype control)	APC-Mouse IgG1 isotype control (Mouse monoclonal)	Biolegend	clone: MOPC-21 RRID:AB_326443	1:200
Commercial assay or kit	Human CMV HHV5 kit for qPCR using a glycoprotein B target	PrimerDesign	Cat#: Path-HHV5
Commercial assay or kit	HEX labeled RPP30 copy number assay for ddPCR	Bio-Rad	Cat#: dHsaCP1000485
Chemical compound, drug	Ruxolitinib	InvivoGen	Cat#: tlrl-rux	4 µM
Peptide, recombinant protein	Human GM-CSF	Peprotech	Cat#: 300-03-20	1,000 U/ml
Peptide, recombinant protein	Human IL-4	Peprotech	Cat#: 200-04-20	1,000 U/ml
Biological compound (E. coli 0111:B4)	LPS	Sigma	Cat#: L4391	500 ng/ml
Software, algorithm	R 3.5.1	https://www.r-project.org/	RRID:SCR_001905
Software, algorithm	Monocle 2.10.1	http://cole-trapnell-lab.github.io/monocle-release/	2.10.1
Software, algorithm	DESeq2 1.22.2	https://bioconductor.org/packages/release/bioc/html/DESeq2.html	RRID:SCR_015687
Software, algorithm	enrichplot	https://github.com/GuangchuangYu/enrichplot
Software, algorithm	GSEA 3.0	http://software.broadinstitute.org/gsea/index.jsp	RRID:SCR_003199
Software, algorithm	MSigDB 6.2	http://software.broadinstitute.org/gsea/index.jsp	RRID:SCR_016863	Database used for GSEA
Software, algorithm	CellRanger 2.0.0	https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger	RRID:SCR_017344
Software, algorithm	lme4 1.1–21	https://cran.r-project.org/web/packages/lme4/index.html	RRID:SCR_015654	R package
Software, algorithm	Bowtie2 2.2.9	http://bowtie-bio.sourceforge.net/bowtie2/index.shtml
Software, algorithm	Rtsne 0.15	https://cran.r-project.org/web/packages/Rtsne/index.html	RRID:SCR_016342	R package

Cells and virus stocks

Request a detailed protocol

Primary CD14+ monocytes were isolated from fresh venous blood, obtained from healthy donors, using Lymphoprep (Stemcell Technologies) density gradient followed by magnetic cell sorting with CD14+‏ magnetic beads (Miltenyi Biotec). The cells were cultured in X-Vivo15 media (Lonza) supplemented with 2.25 mM L-glutamine at 37⁰C in 5% CO2 (Fortunato, 2014). Primary human foreskin fibroblasts (HFF) (ATCC CRL-1634) were maintained in DMEM with 10% fetal bovine serum (FBS), 2 mM L-glutamine, and 100 units/ml penicillin and streptomycin (Beit-Haemek, Israel).

The TB40E virus containing an SV40-GFP tag (TB40E-GFP) was described previously (O'Connor and Murphy, 2012; Sinzger et al., 2008b). Virus was propagated by electroporation of infectious bacterial artificial chromosome (BAC) DNA into fibroblasts using the Amaxa P2 4D-Nucleofector kit (Lonza) according to the manufacturer’s instructions. Viral stocks were concentrated by centrifugation at 26000xg, 4⁰C for 120 min. Infectious virus yields were assayed on THP-1 cells (ATCC TIB-202).

Infection and reactivation procedures

View detailed protocol

For experimental latent infection, CD14+ monocytes were incubated with the virus for 3 hr, washed twice and supplemented with fresh media. To assess infection efficiency, a sample of the infected cell population was FACS analyzed for GFP expression at 3dpi. All experiments were conducted when there was a shift in GFP intensity of the entire population following infection, indicating all cells were infected. HCMV latency was validated by absence of GFP positive plaques on fibroblasts incubated with infected monocytes cell lysate.

For reactivation assays, infected monocytes were counted, plated and co-cultured with primary fibroblasts immediately or following differentiation into dendritic cells (DCs). DC differentiation was done by incubation of cells with granulocyte-macrophage CSF and interleukin-4 (Peprotech) at 1,000 U/ml for 5 days, followed by stimulation with 500 ng/ml of LPS (Sigma) for 48 hr (as previously described in Cobbs et al., 2014). Release of infectious virions was assayed by quantification of GFP positive plaques on the fibroblasts monolayer.

Ruxolitinib treatment

Request a detailed protocol

Ruxolitinib (Ruxo) was added at a concentration of 4 uM, either immediately after infection (3hpi) or at later time points (1, 2, or 3dpi). Monocytes were washed to remove residual Ruxo before co-culturing with fibroblasts.

Quantitative real-time PCR analysis

Request a detailed protocol

For analysis of RNA expression, total RNA was extracted using Tri-Reagent (Sigma) according to manufacturer’s protocol. cDNA was prepared using qScript cDNA Synthesis Kit (Quanta Biosciences) according to manufacturer’s protocol. For analysis of DNA levels, cells were lysed in a 1:1 mixture of PCR solutions A (100 mM KCl, 10 mM Tris–HCl pH 8.3, and 2.5 mM MgCl2) and B (10 mM Tris–HCl pH 8.3, 2.5 mM MgCl2, 1% Tween 20, 1% Non-idet P-40, and 0.4 mg/ml Proteinase K), for 60 min at 60°C followed by a 10 min 95°C incubation, as described in Roback et al. (2001). Real time PCR was performed using the SYBR Green PCR master-mix (ABI) on the QuantStudio 12K Flex (ABI) with the following primers (forward, reverse):

UL 138 (GTGTCTTCCCAGTGCAGCTA, GCACGCTGTTTCTCTGGTTA)
UL22 (TTACTAGCCGTGACCTTGACG, CAGAAATCGAAGCGCAGCG)
RNA 2.7 (TCCTACCTACCACGAATCGC, GTTGGGAATCGTCGACTTTG)
CD74 (TGGAAGGTCTTTGAGAGCTGGATG, TTCCTGGCACTTGGTCAGTA)
MHCII-HLA-DQA1 (CTTCATCATCCAAGGCCTGC, CGGGCCAGAGAATAGTGCTA)
ANXA5 (AGTCTGGTCCTGCTTCACCT, CAAGCCTTTCATAGCCTTCC)

Viral DNA was quantified with RNA2.7 primers, host DNA was measured with the following primers (forward, reverse):

B2M (TGCTGTCTCCATGTTTGATGTATCT, TCTCTGCTCCCCACCTCTAAGT)

Cell staining for flow cytometry and sorting

Request a detailed protocol

Cells were counted, and stained in cold MACS buffer (PBS, 5% BSA, 2 mM EDTA). Cell staining was done using the following antibodies: anti-human APC-CD74 (Clone: 5–329, Miltenyi Biotec), anti-human PE-HLA-DR, DP, DQ (clone: REA332, Miltenyi Biotec), anti-human FITC-CD14 (Clone: TÜK4, Miltenyi Biotec), anti-human APC-B7H4 (clone: MIH43, Biolegend), APC-Mouse IgG1 isotype control (clone: MOPC-21, Biolegend) according to manufacturer’s instructions. Cells were analyzed and sorted on a BD FACSAriaIII.

Detection of viral genomes by digital PCR

Request a detailed protocol

Detection of viral DNA in monocytes from natural latent samples was done using the QX200 droplet digital PCR system (Bio-Rad), using FAM labeled HCMV primer and probe: Human CMV HHV5 kit for qPCR using a glycoprotein B target (PrimerDesign); and HEX labeled RPP30 copy number assay for ddPCR (Bio-Rad), as previously described (Jackson et al., 2017). Calibration curve was ran in duplicate, using CMV positive control template (PrimerDesign). The limit of detection was 3 events per sample, with accuracy improved at 10 copies and higher (Figure 5—figure supplement 1). For sample preparation cells were counted, dry pelleted, and stored at −80°C prior to DNA extraction. DNA was extracted from the cell pellet in a 1:1 mixture of PCR solutions A (100 mM KCl, 10 mM Tris–HCl pH 8.3, and 2.5 mM MgCl2) and B (10 mM Tris–HCl pH 8.3, 2.5 mM MgCl2, 0.25% Tween 20, 0.25% Non-idet P-40, and 0.4 mg/ml Proteinase K), for 60 min at 60°C followed by a 10 min 95°C incubation, according to the description in Roback et al. (2001).

RNA library construction

Request a detailed protocol

RNA libraries were generated from samples of ~10,000 cells according to the MARS-seq protocol (Jaitin et al., 2014; Keren-Shaul et al., 2019).

Sequencing and data analysis

Request a detailed protocol

RNA-Seq libraries (pooled at equimolar concentration) were performed in duplicates and sequenced using NextSeq 500 (Illumina), with read parameters: Read1: 72 cycles and Read2: 15 cycles.

Analysis of bulk MARS-seq of CD14+ monocytes, sorted according to the CD74 cell-surface levels, was done as described previously (Shnayder et al., 2018). The number of Unique Molecular Identifiers (UMIs) were: 976,294, and 947,474 for the CD74^high samples, 902,150, and 844,872 for the CD74^low samples (Figure 6A–E); 2,422,356 and 2,395,329 for the –Ruxo 3dpi samples, 2,187,290 and 925,362 for the +Ruxo 3dpi samples, 4,228,168 and 3,854,339 for the –Ruxo 6dpi samples and 3,449,188 and 1,900,301 for the +Ruxo 6dpi samples (Figure 7A and B); 2,076,741 and 1,858,871 for the +Ruxo at 1dpi samples, 1,914,289 and 1,163,469 for the +Ruxo at 2dpi samples, 1,554,298 and 1,685,834 for the +Ruxo at 3dpi samples, 2,004,859 and 2,450,039 for the –Ruxo control (Figure 7C).

Reads for gene expression and correlation analyses were normalized using DEseq2.

Based on the t-SNE plot of the CD14+ monocyte cells in Shnayder et al. (2018), the latent cells were defined as all cells besides the small distinct group of cells that show high levels of viral reads (mean 9.5%). All the analyses in this paper include only the remaining 3,416 cells.

Correlation and t-SNE coloring

Request a detailed protocol

For calculating the Spearman correlation between host genes and either viral expression levels or dpi, in each cell, the sum of viral reads was normalized to the total number of reads in the cell, and the number of reads of each host gene was normalized to the total number of host reads in the cell. The correlation was calculated across 1,448 cells for 6997 genes. Cells with no viral reads, or with less than 700 different host genes expressed, were omitted. Genes with total number of reads less than 20 were ignored. The Z-score was calculated based on the mean and the standard deviation calculated over all 6997 genes.

Color coding of t-SNE (Figure 1C, Figure 6D and Figure 7B) or Monocle (e.g. Figure 7C, G, H) plots for expression levels of groups of genes was calculated according to the average relative expression as follows: First, genes with low level of expression, that is expressed in less than 50 cells, were omitted. Next, the number of host reads per cell was normalized so that all cells will include the same number of reads. Next, for each cell and each gene, the relative expression is calculated as ${l o g}_{2} (\frac{r_{i j} + 1}{\bar{r_{i}} + 1})$ where $r_{i j}$ is the number of reads of gene $i$ in cell $j$ and $\bar{r_{i}}$ is the average number of reads of gene $i$ over all cells. Finally, for each cell the average ${l o g}_{2}$ relative expression over all genes in the group was calculated.

Differential expression and enrichment analysis

Request a detailed protocol

The differential expression analysis was done with DESeq2 (version 1.22.2) (Love et al., 2014) using default parameters, with the number of reads in each of the samples as an input. The normalized number of reads according to DESeq2 were used for enrichment analysis using GSEA (version 3.0) (Subramanian et al., 2005). The genes sets that were used were GO biological process (c5.bp) and REACTOME (c2.cp.reactome) from MSigDB (version 6.2) (Liberzon et al., 2011) and the monocyte progenitor gene list from Velten et al. (2017). The GSEA plots were created based on the GSEA output with the R package enrichplot. To calculate differential expression and pathway enrichment within the distinct group of cells that expressed monocyte priming markers, we used the graph-based clustering of the Cell Ranger software, stratifying this group of cells into two distinct clusters and GSEA (version 3.0) (Subramanian et al., 2005) for pathway enrichment. Differential expression and enrichment analysis were done on these two clusters. Enrichment of M1 and M2 associated genes was done by hypergeometric test taking all genes with mean expression ≥5 as background.

Monocle analysis

Request a detailed protocol

CD34+ cells were ordered according to the predicted pseudo-time using Monocle (version 2.10.1) (Trapnell et al., 2014). The cells used in this analysis were filtered to have at least 1000 expressed genes and not more than 10,000 UMIs, while the genes were filtered to have mean expression greater than 0.1, and empirical dispersion greater than the global dispersion fit.

Ethics statement

Request a detailed protocol

All fresh peripheral blood samples were obtained after approval of protocols by the Weizmann Institutional Review Board (IRB application 92–1). The study using HSCT recipient samples was approved by the Human Research Ethics Committee of the University of Sydney and the Western Sydney Local Health District. Informed consent was obtained from all study participants prior to enrolment in accordance with the Declaration of Helsinki.

Data availability

Sequencing data have been deposited in GEO under accession code GSE138838.

The following data sets were generated

1. Stern-Ginossar N
2. Shnayder M
3. Schwartz M
4. Nachshon A
5. Rozman B
6. Bernshtein B
7. Lavi M
8. Fein N
(2019) NCBI Gene Expression Omnibus
ID GSE138838. Single cell analysis reveals human cytomegalovirus drives latently infected cells towards an anergic-like monocyte state.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138838

The following previously published data sets were used

(2018) NCBI Gene Expression Omnibus
ID GSE101341. Defining the Transcriptional Landscape during Cytomegalovirus Latency with Single-Cell RNA Sequencing.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE101341

References

1. Avdic S
2. Cao JZ
3. McSharry BP
4. Clancy LE
5. Brown R
6. Steain M
7. Gottlieb DJ
8. Abendroth A
9. Slobedman B
(2013) Human Cytomegalovirus interleukin-10 polarizes monocytes toward a deactivated M2c phenotype to repress host immune responses
Journal of Virology 87:10273–10282.

https://doi.org/10.1128/JVI.00912-13
- PubMed
- Google Scholar
(2009) Rat Cytomegalovirus infection depletes MHC II in bone marrow derived dendritic cells
Virology 388:78–90.

https://doi.org/10.1016/j.virol.2009.02.050
- PubMed
- Google Scholar
1. Bergmann H
(2012) Business as usual: the p35 isoform of human CD74 retains function in antigen presentation
Immunology and Cell Biology 90:839–840.

https://doi.org/10.1038/icb.2012.51
- PubMed
- Google Scholar
Data
1. Blood Atlas
(authors) (2019) VTCN1 the human protein atlas
The Human Protein Atlas.

https://www.proteinatlas.org/ENSG00000134258-VTCN1/blood
(2008) Transcriptome analysis reveals human Cytomegalovirus reprograms monocyte differentiation toward an M1 macrophage
The Journal of Immunology 181:698–711.

https://doi.org/10.4049/jimmunol.181.1.698
- PubMed
- Google Scholar
1. Chan G
2. Nogalski MT
3. Bentz GL
4. Smith MS
5. Parmater A
6. Yurochko AD
(2010) PI3K-dependent upregulation of Mcl-1 by human Cytomegalovirus is mediated by epidermal growth factor receptor and inhibits apoptosis in short-lived monocytes
The Journal of Immunology 184:3213–3222.

https://doi.org/10.4049/jimmunol.0903025
- PubMed
- Google Scholar
(2017) Transcriptome-wide characterization of human Cytomegalovirus in natural infection and experimental latency
PNAS 114:E10586–E10595.

https://doi.org/10.1073/pnas.1710522114
- PubMed
- Google Scholar
Book
(2014) Methods for the Detection of Cytomegalovirus in Glioblastoma Cells and Tissues
In: Yurochko A. D, Miller W. E, editors. Human Cytomegaloviruses, 1119. Humana Press. pp. 165–196.

https://doi.org/10.1007/978-1-62703-788-4_11
- Google Scholar
1. Crawford LB
2. Kim JH
3. Collins-McMillen D
4. Lee B-J
5. Landais I
6. Held C
7. Nelson JA
8. Yurochko AD
9. Caposio P
(2018) Human Cytomegalovirus encodes a novel FLT3 receptor ligand necessary for hematopoietic cell differentiation and viral reactivation
mBio 9:00682-18.

https://doi.org/10.1128/mBio.00682-18
- Google Scholar
1. Crough T
2. Khanna R
(2009) Immunobiology of human Cytomegalovirus: from bench to bedside
Clinical Microbiology Reviews 22:76–98.

https://doi.org/10.1128/CMR.00034-08
- PubMed
- Google Scholar
1. Dağ F
2. Dölken L
3. Holzki J
4. Drabig A
5. Weingärtner A
6. Schwerk J
7. Lienenklaus S
8. Conte I
9. Geffers R
10. Davenport C
11. Rand U
12. Köster M
13. Weiß S
14. Adler B
15. Wirth D
16. Messerle M
17. Hauser H
18. Cičin-Šain L
(2014) Reversible silencing of Cytomegalovirus genomes by type I interferon governs virus latency
PLOS Pathogens 10:e1003962.

https://doi.org/10.1371/journal.ppat.1003962
- PubMed
- Google Scholar
1. Douam F
2. Hrebikova G
3. Albrecht YES
4. Sellau J
5. Sharon Y
6. Ding Q
7. Ploss A
(2017) Single-cell tracking of Flavivirus RNA uncovers species-specific interactions with the immune system dictating disease outcome
Nature Communications 8:14781.

https://doi.org/10.1038/ncomms14781
- Google Scholar
1. Drayman N
2. Patel P
3. Vistain L
4. Tay S
(2019) HSV-1 single-cell analysis reveals the activation of anti-viral and developmental programs in distinct sub-populations
eLife 8:e566489.

https://doi.org/10.7554/eLife.46339
- Google Scholar
1. Elder EG
2. Krishna BA
3. Williamson J
4. Lim EY
5. Poole E
6. Sedikides GX
7. Wills M
8. O'Connor CM
9. Lehner PJ
10. Sinclair J
(2019) Interferon-Responsive genes are targeted during the establishment of human Cytomegalovirus latency
mBio 10:e02574-19.

https://doi.org/10.1128/mBio.02574-19
- PubMed
- Google Scholar
Book
1. Fortunato E
(2014) Use of Diploid Human Fibroblasts as a Model System to Culture, Grow, and Study Human Cytomegalovirus Infection
In: Yurochko AD, Miller WE, editors. Human Cytomegaloviruses, 1119. Springer. pp. 47–58.

https://doi.org/10.1007/978-1-62703-788-4_4
- Google Scholar
Preprint
1. Galinato M
2. Shimoda K
3. Aguiar A
4. Hennig F
5. Boffelli D
6. McVoy MA
7. Hertel L
(2018) Single-cell transcriptome analysis of CD34+ stem cell-derived myeloid cells identifies a CFU-GEMM-like population permissive to human Cytomegalovirus infection
bioRxiv.

https://doi.org/10.1101/438457
- Google Scholar
(2018) Transcriptional profiling identifies novel regulators of macrophage polarization
PLOS ONE 13:e0208602.

https://doi.org/10.1371/journal.pone.0208602
- PubMed
- Google Scholar
1. Goodrum F
2. Jordan CT
3. Terhune SS
4. High K
5. Shenk T
(2004) Differential outcomes of human Cytomegalovirus infection in primitive hematopoietic cell subpopulations
Blood 104:687–695.

https://doi.org/10.1182/blood-2003-12-4344
- PubMed
- Google Scholar
(2017) Latent Cytomegalovirus (CMV) Infection does not detrimentally alter T cell responses in the healthy old, but increased latent CMV carriage is related to expanded CMV-Specific T cells
Frontiers in Immunology 8:733.

https://doi.org/10.3389/fimmu.2017.00733
- Google Scholar
1. Jaitin DA
2. Kenigsberg E
3. Keren-Shaul H
4. Elefant N
5. Paul F
6. Zaretsky I
7. Mildner A
8. Cohen N
9. Jung S
10. Tanay A
11. Amit I
(2014) Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types
Science 343:776–779.

https://doi.org/10.1126/science.1247651
- PubMed
- Google Scholar
1. Keren-Shaul H
2. Kenigsberg E
3. Jaitin DA
4. David E
5. Paul F
6. Tanay A
7. Amit I
(2019) MARS-seq2.0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing
Nature Protocols 14:1841–1862.

https://doi.org/10.1038/s41596-019-0164-4
- PubMed
- Google Scholar
1. Kew V
2. Wills M
3. Reeves M
(2017)
HCMV activation of ERK-MAPK drives a multi-factorial response promoting the survival of infected myeloid progenitors

Journal of Molecular Biochemistry 6:13–25.
- PubMed
- Google Scholar
1. Lee AW
2. Wang N
3. Hornell TM
4. Harding JJ
5. Deshpande C
6. Hertel L
7. Lacaille V
8. Pashine A
9. Macaubas C
10. Mocarski ES
11. Mellins ED
(2011) Human Cytomegalovirus decreases constitutive transcription of MHC class II genes in mature langerhans cells by reducing CIITA transcript levels
Molecular Immunology 48:1160–1167.

https://doi.org/10.1016/j.molimm.2011.02.010
- PubMed
- Google Scholar
(2011) Molecular signatures database (MSigDB) 3.0
Bioinformatics 27:1739–1740.

https://doi.org/10.1093/bioinformatics/btr260
- PubMed
- Google Scholar
1. Lin Q
2. Meloni D
3. Pan Y
4. Xia M
5. Rodgers J
6. Shepard S
7. Li M
8. Galya L
9. Metcalf B
10. Yue TY
11. Liu P
12. Zhou J
(2009) Enantioselective synthesis of Janus kinase inhibitor INCB018424 via an organocatalytic aza-Michael reaction
Organic Letters 11:1999–2002.

https://doi.org/10.1021/ol900350k
- PubMed
- Google Scholar
1. Linderman JA
2. Kobayashi M
3. Rayannavar V
4. Fak JJ
5. Darnell RB
6. Chao MV
7. Wilson AC
8. Mohr I
(2017) Immune escape via a transient gene expression program enables productive replication of a latent pathogen
Cell Reports 18:1312–1323.

https://doi.org/10.1016/j.celrep.2017.01.017
- PubMed
- Google Scholar
1. Love MI
2. Huber W
3. Anders S
(2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Genome Biology 15:550.

https://doi.org/10.1186/s13059-014-0550-8
- PubMed
- Google Scholar
(1996) Detection of endogenous human Cytomegalovirus in CD34+ bone marrow progenitors
Journal of General Virology 77:3099–3102.

https://doi.org/10.1099/0022-1317-77-12-3099
- PubMed
- Google Scholar
Book
1. Mocarski ES
2. Shenk T
(2013)
CytomegalovirusesFields’ Virology

Lippincott Williams & Wilkins.
- Google Scholar
1. O'Connor CM
2. Murphy EA
(2012) A myeloid progenitor cell line capable of supporting human Cytomegalovirus latency and reactivation, resulting in infectious progeny
Journal of Virology 86:9854–9865.

https://doi.org/10.1128/JVI.01278-12
- PubMed
- Google Scholar
1. Parry HM
2. Zuo J
3. Frumento G
4. Mirajkar N
5. Inman C
6. Edwards E
7. Griffiths M
8. Pratt G
9. Moss P
(2016) Cytomegalovirus viral load within blood increases markedly in healthy people over the age of 70 years
Immunity & Ageing 13:1–13.

https://doi.org/10.1186/s12979-015-0056-6
- PubMed
- Google Scholar
1. Patel AA
2. Zhang Y
3. Fullerton JN
4. Boelen L
5. Rongvaux A
6. Maini AA
7. Bigley V
8. Flavell RA
9. Gilroy DW
10. Asquith B
11. Macallan D
12. Yona S
(2017) The fate and lifespan of human monocyte subsets in steady state and systemic inflammation
The Journal of Experimental Medicine 214:1913–1923.

https://doi.org/10.1084/jem.20170355
- PubMed
- Google Scholar
1. Paul F
2. Arkin Y
3. Giladi A
4. Jaitin DA
5. Kenigsberg E
6. Keren-Shaul H
7. Winter D
8. Lara-Astiaso D
9. Gury M
10. Weiner A
11. David E
12. Cohen N
13. Lauridsen FK
14. Haas S
15. Schlitzer A
16. Mildner A
17. Ginhoux F
18. Jung S
19. Trumpp A
20. Porse BT
21. Tanay A
22. Amit I
(2015) Transcriptional heterogeneity and lineage commitment in myeloid progenitors
Cell 163:1663–1677.

https://doi.org/10.1016/j.cell.2015.11.013
- PubMed
- Google Scholar
1. Rato S
2. Rausell A
3. Muñoz M
4. Telenti A
5. Ciuffi A
(2017) Single-cell analysis identifies cellular markers of the HIV permissive cell
PLOS Pathogens 13:e1006678.

https://doi.org/10.1371/journal.ppat.1006678
- Google Scholar
(2005) An in vitro model for the regulation of human Cytomegalovirus latency and reactivation in dendritic cells by chromatin remodelling
Journal of General Virology 86:2949–2954.

https://doi.org/10.1099/vir.0.81161-0
- PubMed
- Google Scholar
(2012) Human Cytomegalovirus activation of ERK and myeloid cell leukemia-1 protein correlates with survival of latently infected cells
PNAS 109:588–593.

https://doi.org/10.1073/pnas.1114966108
- Google Scholar
1. Roback JD
2. Hillyer CD
3. Drew WL
4. Laycock ME
5. Luka J
6. Mocarski ES
7. Slobedman B
8. Smith JW
9. Soderberg-Naucler C
10. Todd DS
11. Woxenius S
12. Busch MP
(2001) Multicenter evaluation of PCR methods fordetecting CMV DNA in blood donors
Transfusion 41:1249–1257.

https://doi.org/10.1046/j.1537-2995.2001.41101249.x
- Google Scholar
(2018) Extreme heterogeneity of influenza virus infection in single cells
eLife 7:e32303.

https://doi.org/10.7554/eLife.32303
- PubMed
- Google Scholar
1. Schwartz M
2. Stern-Ginossar N
(2019) The transcriptome of latent human Cytomegalovirus
Journal of Virology 93:e00047-19.

https://doi.org/10.1128/JVI.00047-19
- PubMed
- Google Scholar
1. Shnayder M
2. Nachshon A
3. Krishna B
4. Poole E
5. Boshkov A
6. Binyamin A
7. Maza I
8. Sinclair J
9. Schwartz M
10. Stern-Ginossar N
(2018) Defining the transcriptional landscape during Cytomegalovirus latency with Single-Cell RNA sequencing
mBio 9:e00013-18.

https://doi.org/10.1128/mBio.00013-18
- PubMed
- Google Scholar
1. Sinzger C
2. Digel M
3. Jahn G
(2008a) Cytomegalovirus cell tropism
Current Topics in Microbiology and Immunology 325:63–83.

https://doi.org/10.1007/978-3-540-77349-8_4
- PubMed
- Google Scholar
1. Sinzger C
2. Hahn G
3. Digel M
4. Katona R
5. Sampaio KL
6. Messerle M
7. Hengel H
8. Koszinowski U
9. Brune W
10. Adler B
(2008b) Cloning and sequencing of a highly productive, endotheliotropic virus strain derived from human Cytomegalovirus TB40/E
Journal of General Virology 89:359–368.

https://doi.org/10.1099/vir.0.83286-0
- PubMed
- Google Scholar
(2002) Latent Cytomegalovirus down-regulates major histocompatibility complex class II expression on myeloid progenitors
Blood 100:2867–2873.

https://doi.org/10.1182/blood.V100.8.2867
- PubMed
- Google Scholar
1. Slobedman B
2. Cao JZ
3. Avdic S
4. Webster B
5. McAllery S
6. Cheung AK
7. Tan JC
8. Abendroth A
(2010) Human Cytomegalovirus latent infection and associated viral gene expression
Future Microbiology 5:883–900.

https://doi.org/10.2217/fmb.10.58
- PubMed
- Google Scholar
1. Slobedman B
2. Mocarski ES
(1999) Quantitative analysis of latent human Cytomegalovirus
Journal of Virology 73:4806–4812.

https://doi.org/10.1128/JVI.73.6.4806-4812.1999
- PubMed
- Google Scholar
(2004) Human Cytomegalovirus induces monocyte differentiation and migration as a strategy for dissemination and persistence
Journal of Virology 78:4444–4453.

https://doi.org/10.1128/JVI.78.9.4444-4453.2004
- Google Scholar
1. Smith MS
2. Bivins-Smith ER
3. Tilley AM
4. Bentz GL
5. Chan G
6. Minard J
7. Yurochko AD
(2007) Roles of phosphatidylinositol 3-kinase and NF-kappaB in human cytomegalovirus-mediated monocyte diapedesis and adhesion: strategy for viral persistence
Journal of Virology 81:7683–7694.

https://doi.org/10.1128/JVI.02839-06
- PubMed
- Google Scholar
1. Staras SA
2. Dollard SC
3. Radford KW
4. Flanders WD
5. Pass RF
6. Cannon MJ
(2006) Seroprevalence of Cytomegalovirus infection in the united states, 1988-1994
Clinical Infectious Diseases 43:1143–1151.

https://doi.org/10.1086/508173
- PubMed
- Google Scholar
1. Steuerman Y
2. Cohen M
3. Peshes-Yaloz N
4. Valadarsky L
5. Cohn O
6. David E
7. Frishberg A
8. Mayo L
9. Bacharach E
10. Amit I
11. Gat-Viks I
(2018) Dissection of influenza infection in Vivo by Single-Cell RNA Sequencing
Cell Systems 6:679–691.

https://doi.org/10.1016/j.cels.2018.05.008
- PubMed
- Google Scholar
(2014) HCMV reprogramming of infected monocyte survival and differentiation: a goldilocks phenomenon
Viruses 6:782–807.

https://doi.org/10.3390/v6020782
- PubMed
- Google Scholar
1. Subramanian A
2. Tamayo P
3. Mootha VK
4. Mukherjee S
5. Ebert BL
6. Gillette MA
7. Paulovich A
8. Pomeroy SL
9. Golub TR
10. Lander ES
11. Mesirov JP
(2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles
PNAS 102:15545–15550.

https://doi.org/10.1073/pnas.0506580102
- PubMed
- Google Scholar
(1991) Monocytes are a major site of persistence of human Cytomegalovirus in peripheral blood mononuclear cells
Journal of General Virology 72:2059–2064.

https://doi.org/10.1099/0022-1317-72-9-2059
- PubMed
- Google Scholar
1. Trapnell C
2. Cacchiarelli D
3. Grimsby J
4. Pokharel P
5. Li S
6. Morse M
7. Lennon NJ
8. Livak KJ
9. Mikkelsen TS
10. Rinn JL
(2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
Nature Biotechnology 32:381–386.

https://doi.org/10.1038/nbt.2859
- PubMed
- Google Scholar
1. Velten L
2. Haas SF
3. Raffel S
4. Blaszkiewicz S
5. Islam S
6. Hennig BP
7. Hirche C
8. Lutz C
9. Buss EC
10. Nowak D
11. Boch T
12. Hofmann W-K
13. Ho AD
14. Huber W
15. Trumpp A
16. Essers MAG
17. Steinmetz LM
(2017) Human haematopoietic stem cell lineage commitment is a continuous process
Nature Cell Biology 19:271–281.

https://doi.org/10.1038/ncb3493
- Google Scholar
1. von Laer D
2. Meyer-Koenig U
3. Serr A
4. Finke J
5. Kanz L
6. Fauser AA
7. Neumann-Haefelin D
8. Brugger W
9. Hufert FT
(1995) Detection of Cytomegalovirus DNA in CD34+ cells from blood and bone marrow
Blood 86:4086–4090.

https://doi.org/10.1182/blood.V86.11.4086.bloodjournal86114086
- PubMed
- Google Scholar
1. Wyler E
2. Franke V
3. Menegatti J
4. Kocks C
5. Boltengagen A
6. Praktiknjo S
7. Walch-Rückheim B
8. Bosse J
9. Rajewsky N
10. Grässer F
11. Akalin A
12. Landthaler M
(2019) Single-cell RNA-sequencing of herpes simplex virus 1-infected cells connects NRF2 activation to an antiviral program
Nature Communications 10:4878.

https://doi.org/10.1038/s41467-019-12894-z
- PubMed
- Google Scholar
1. Yunis J
2. Farrell HE
3. Bruce K
4. Lawler C
5. Sidenius S
6. Wyer O
7. Davis-Poynter N
8. Stevenson PG
(2018) Murine Cytomegalovirus degrades MHC class II to colonize the salivary glands
PLOS Pathogens 14:e1006905.

https://doi.org/10.1371/journal.ppat.1006905
- PubMed
- Google Scholar
1. Zanini F
2. Pu SY
3. Bekerman E
4. Einav S
5. Quake SR
(2018) Single-cell transcriptional dynamics of Flavivirus infection
eLife 7:e32942.

https://doi.org/10.7554/eLife.32942
- PubMed
- Google Scholar
1. Zhu D
2. Pan C
3. Sheng J
4. Liang H
5. Bian Z
6. Liu Y
7. Trang P
8. Wu J
9. Liu F
10. Zhang CY
11. Zen K
(2018) Human Cytomegalovirus reprogrammes haematopoietic progenitor cells into immunosuppressive monocytes to achieve latency
Nature Microbiology 3:503–513.

https://doi.org/10.1038/s41564-018-0131-9
- PubMed
- Google Scholar

Article and author information

Author details

Miri Shnayder

Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel

Contribution
Conceptualization, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

Competing interests
No competing interests declared
Aharon Nachshon

Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel

Contribution
Conceptualization, Software, Formal analysis, Validation, Investigation, Methodology, Writing - original draft, Writing - review and editing

Competing interests
No competing interests declared
Batsheva Rozman

Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel

Contribution
Formal analysis, Investigation, Writing - review and editing

Competing interests
No competing interests declared
Biana Bernshtein

Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel

Contribution
Formal analysis, Investigation, Methodology, Writing - review and editing

Competing interests
No competing interests declared
Michael Lavi

Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel

Contribution
Resources, Investigation, Writing - review and editing

Competing interests
No competing interests declared
Noam Fein

Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel

Contribution
Investigation, Writing - review and editing

Competing interests
No competing interests declared
Emma Poole

Department of Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, United Kingdom

Contribution
Investigation, Writing - review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0003-3904-6121
Selmir Avdic

Sydney Cellular Therapies Laboratory, Westmead, Sydney, Australia

Contribution
Resources, Writing - review and editing

Competing interests
No competing interests declared
Emily Blyth
1. Sydney Cellular Therapies Laboratory, Westmead, Sydney, Australia
2. Blood and Bone Marrow Transplant Unit, Westmead Hospital, Sydney, Australia
Contribution
Resources, Writing - review and editing

Competing interests
No competing interests declared
David Gottlieb
1. Sydney Cellular Therapies Laboratory, Westmead, Sydney, Australia
2. Blood and Bone Marrow Transplant Unit, Westmead Hospital, Sydney, Australia
Contribution
Resources, Writing - review and editing

Competing interests
No competing interests declared
Allison Abendroth

Discipline of Infectious Diseases and Immunology, Faculty of Medicine and Health, Charles Perkins Centre, University of Sydney, Sydney, Australia

Contribution
Resources, Writing - review and editing

Competing interests
No competing interests declared
Barry Slobedman

Discipline of Infectious Diseases and Immunology, Faculty of Medicine and Health, Charles Perkins Centre, University of Sydney, Sydney, Australia

Contribution
Conceptualization, Resources, Writing - review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-9431-6094
John Sinclair

Department of Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, United Kingdom

Contribution
Conceptualization, Investigation, Writing - review and editing

Competing interests
No competing interests declared
Noam Stern-Ginossar

Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel

Contribution
Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Writing - review and editing

For correspondence
noam.stern-ginossar@weizmann.ac.il

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0003-3583-5932
Michal Schwartz

Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel

Contribution
Conceptualization, Formal analysis, Supervision, Investigation, Methodology, Writing - original draft, Writing - review and editing

For correspondence
michalsc@weizmann.ac.il

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0001-5442-0201

Funding

Seventh Framework Programme (Infect-ERA, TANKACY)

Noam Stern-Ginossar

H2020 European Research Council (Starting grant (StG-2014-638142))

Noam Stern-Ginossar

Abisch-Frenkel Foundation (18/WIS7)

Noam Stern-Ginossar

National Institute for Health Research (Cambridge NIHR BRC Cell Phenotyping Hub)

John Sinclair

Medical Research Council (G0701279)

John Sinclair

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank the members of the Stern-Ginossar lab for critical reading of the manuscript. We thank Eain A Murphy for the TB40E-GFP virus strain. This research was supported by Infect-ERA (TANKACY), the European Research Council starting grant (StG-2014–638142) and a grant from the Abisch-Frenkel Foundation (18/WIS7) to NS-G, and by the Cambridge NIHR BRC Cell Phenotyping Hub and a grant from the British Medical Research Council (Grant G0701279) to JS. NS-G is incumbent of the skirball career development chair in new scientist. BS and EB were supported by a Biomed-Connect Grant from the University of Sydney.

Ethics

Human subjects: All fresh peripheral blood samples were obtained after approval of protocols by the Weizmann Institutional Review Board (IRB application 92-1). Informed written consent was obtained from all volunteers, and all experiments were carried out in accordance with the approved guidelines. The study using HSCT recipient samples was approved by the Human Research Ethics Committee of the University of Sydney and the Western Sydney Local Health District. Informed consent was obtained from all study participants prior to enrolment in accordance with the Declaration of Helsinki.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.