Virus infection at the individual cell level

This section provides a background to single-cell analysis of virus infections. First, single-cell virus replication in bacterial and animal cells is introduced. Later, the influence of DIPs on virus infections and the diversity of the innate immune response at the single cell level are presented. Finally, a brief background to the whole-transcriptome analysis of virus-infected single cells is provided.

2.2.4.1. Bacterial cells

Already very early in 1929, a method to study virus release from single infected bacteria (i.e.

the burst size) was developed (Burnet, 1929). Single-cell analysis was accomplished by utilizing the Poisson statistic: solutions of infected bacteria were diluted and the authors then applied, on average, less than one bacterium per reaction tube. Under these conditions, only a few of the tubes contained a single bacterium (while most of them did not), and the number of tubes

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that contain more than one bacterium was minimized this way. The content of every tube was then plated to obtain a plaque count. Using this method, a large variation in bacterial burst size was observed (Ellis and Delbruck, 1939, Delbruck, 1942). Yet, the throughput of this method was very limited. Later, in 1945, the first statistically sound burst size distribution of phage-infected bacteria was published with only minor modifications of this initial methodology (Delbruck, 1945b). The authors observed a very high cell-to-cell heterogeneity in the bacterial burst size distribution, ranging from below 25 to more than 1,000 phages per bacterium.

Moreover, it was noted that differences in the size of bacteria, which cover a range more than two, cannot account for large variability in virus titers. A similarly large cell-to-cell heterogeneity in bacterial burst sizes was also observed later in bacteriophage-infected streptococci (Fischetti et al., 1968) and cyanophage-infected cyanobacteria (Kirzner et al., 2016).

It was speculated that the large between-cell difference in bacterial burst size may originate from stochastic fluctuations in intracellular virus growth processes (Delbruck, 1945b). More specifically, it was shown that autocatalytic reaction cascades are prone to the amplification of noise for chemical reactions (Delbruck, 1940). It was speculated that the same may be true for the autocatalytic growth of phages in bacteria, and that this might result in a large cell-to-cell variability in viral burst size (Delbruck, 1945b). This notion was later also used to explain the high cell-to-cell heterogeneity in viral burst sizes of the non-lytic filamentous virus m13 from E.

coli cells (De Paepe et al., 2010). More specifically, the authors devised a mathematical model, which was able to capture these large differences, assuming initial growth rate differences in the autocatalytic intracellular amplification of virus. However, it appears that the inherent randomness in biochemical reactions may not be sufficient to explain the large single-cell variability in virus replication of animal cells, as outlined below.

2.2.4.2. Animal cells

The first study about single-cell infection in animal cells was published in 1953, using western equine encephalomyelitis (WEE) virus and chicken embryo fibroblast (CEF) cells (Dulbecco and Vogt, 1954). The authors used the methodology of Delbruck et al. (Delbruck, 1945b) and observed a similarly large between-cell heterogeneity in virus titers. Moreover, the authors presumed that the differences in cell size may at least partially explain some of the large variability in the virus titers.

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A large single-cell heterogeneity in virus replication was also observed for vesicular stomatitis virus (VSV)-infected baby hamster kidney (BHK) cells (Zhu et al., 2009), with virus titers that spanned at least two orders of magnitude (50 to 8,000 plaque-forming units (PFU) per cell).

Note that this study was the first in which FACS was utilized to isolate single infected cells into the cavities of individual wells of a multi-well plate. This isolation procedure was later also used in many other studies (Schulte and Andino, 2014, Kirzner et al., 2016, Cohen and Kobiler, 2016, Zanini et al., 2018, Xin et al., 2018, Steuerman et al., 2018). Zhu et al. showed that large cells can produce slightly more viral progeny than smaller cells. This was similarly observed in foot-and-mouth disease (FMDV)-infected cells (Xin et al., 2018), but not in poliovirus-infected single cells (Guo et al., 2017). Nevertheless, Zhu et al. speculated that the differences in cell size may not explain the whole variability in virus titers produced by single infected cells (Zhu et al., 2009). Similarly, the authors showed that the cell cycle phase can have a small effect on single-cell virus titers as well. This was later also confirmed for FMDV-infected cells (Xin et al., 2018). Finally, Zhu et al. studied the effect of the between-virus genetic variability on cell-to-cell heterogeneity in virus replication. The authors observed that the genetic differences in the infecting virus population can, to some extent, contribute to the large fluctuations in virus titers (Zhu et al., 2009). Besides static single-cell analysis, also dynamic aspects of single-cell virus production from VSV-infected cells were studied (Timm and Yin, 2012).

The effect of stochastic noise on single poliovirus-infected HeLaS3 cells was investigated by Schulte et al. (Schulte and Andino, 2014). In this study, intracellular viral RNA replication of infected single cells was studied for the first time using real-time reverse transcription quantitative PCR (RT-qPCR). The authors observed a high cell-to-cell variability in viral RNA levels, with differences that spanned one to two orders of magnitude. Interestingly, a significantly higher between-cell variance in viral RNA levels was observed for a low multiplicity of infection (MOI) (i.e. an MOI of 0.1) as compared to infections performed at an MOI of 10. It was speculated that RNA replication might be more susceptible to stochastic effects at such low MOIs, where only one viral RNA genome enters the cell, which may enhance the cell-to-cell variability. In contrast, at high MOIs, the differences on individual RNA replication reactions were suggested to average out, leading to a more stable and robust RNA replication, and thus, a reduced variance in viral RNA levels between single infected cells. However, the between-cell variability in virus titers did not seem to be affected by the MOI. This was also observed for phage-infected bacteria (Delbruck, 1945b) and VSV infections (Zhu et al., 2009, Timm and Yin, 2012). Moreover, Schulte et al. observed that the average single-cell virus yield was not

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affected by the MOI, although the average of viral RNA levels were reduced (Schulte and Andino, 2014). Furthermore, the authors show that the viral RNA content and the virus titer showed a weak correlation at low MOI, but not at high MOI. Thus, it was speculated that stochastic effects may have a stronger effect on virus production at low MOIs compared to high MOIs. Altogether, it was concluded that the virus titer of an individual cell may be rather dictated by an apparent cellular resource limit at high MOIs, and not by stochastic noise in viral RNA synthesis.

2.2.4.3. DIP co-infection

The effect of DIP co-infection on single-cell virus replication was, so far, only investigated using VSV-infected cells. VSV-derived DIPs can be physically separated from infectious virus particles by velocity sedimentation in sucrose gradients (Frensing, 2015), based on the finding that they are significantly smaller in relation their parental STVs (Holland, 1987). Such a purification of DIPs facilitated the co-infection VSV-infected cells with various amounts of DIPs for single-cell studies (Sekellick and Marcus, 1980). In this work, the authors isolated single cells (from an infected cell population) by picking them with thin glass capillaries under the microscope (i.e.

micromanipulation). The individual infected cells were then transferred to reaction tubes to allow for virus growth. Subsequently, the supernatants were subjected to plaque assays to quantify the virus yields. Note that this isolation procedure was also used later in another single-cell virology study (Combe et al., 2015).

First, Sekkelick et al. co-infected VSV-infected cells with a multiplicity of DIP (MODIP) of 1 (Sekellick and Marcus, 1980). Assuming the poisson statistics, approx. ~63% of the cells would be co-infected with one or more DIPs, while the remaining cells would not receive a DIP.

Consistent with this theoretical assumption, the authors observed that roughly ~61% of the infected single cells either became “non-yielders” (i.e. no infectious virus progeny was released) or “low-yielders” (a comparatively low virus yield was observed). However, the co-infection with an MODIP=16 (where all infected cells should be co-infected by at least one DIP) did not result in all cells being either “non-yielders” or “low-yielders”. Instead, the authors observed a new class of infected single cells: “intermediate-yielders”. Based on these results, it was concluded that this may be explained by the self-interference of DIPs (i.e. the interference with their own interference, which leads to a reduction in interference). Such a self-interference was later similarly described in another single-cell virology study with VSV-derived DIPs (Akpinar et al., 2016b).

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In 2016, a microfluidic workflow for single-cell virology was presented that facilitated the analysis of hundreds (and up to one thousand) of infected single cells per experiment (Warrick et al., 2016). This procedure was later used to investigate single-cell VSV infection under the influence of DIPs (Akpinar et al., 2016b), or to study the effect of the innate immune response on cell-to-cell variability in VSV replication (Timm et al., 2017). For this, Warrick et al. combined a microwell-based device, fluorescence-based imaging, and a streamlined bioinformatic data analysis workflow. Moreover, the technology allowed for time-lapse analysis of single-cell virus infections. A very similar experimental approach was later also developed for the study of poliovirus-infected single cells (Guo et al., 2017).

Akpinar et al. used the workflow developed by Warrick et al. (Warrick et al., 2016) to study the influence of varying amounts of DIPs on VSV-infected single cells (Akpinar et al., 2016b), similar to earlier studies (Sekellick and Marcus, 1980). However, the authors utilized a recombinant VSV that expressed red fluorescent protein (RFP) as a reporter of viral protein production and time-resolved single-cell analysis (Akpinar et al., 2016b). It was observed that DIP co-infections lead to a reduction and delay of intracellular viral gene expression. Moreover, the authors developed a mathematical model that was able to recapitulate the kinetic parameters of virus replication in single cell (under the influence of varying amounts of DIP-coinfections). Later, this model was extended, and was able to describe virus growth and spread, from the single-cell to the multicellular level (Akpinar et al., 2016a). More specifically, new experimental data of the spatio-temporal spread pattern of virus infection in a plaque, under the influence of varying amounts of DIP-coinfections (Akpinar et al., 2016a), could be recapitulated.

2.2.4.4. Innate immune response

The innate immune response is the first line of defense that inhibits virus replication and spread until the adaptive immune response can deliver a more effective suppression (Trinchieri, 2010). In general, it was observed that the IFN response can be only observed in a small subpopulation of single cells upon virus infection (Zawatzky et al., 1985, Hu et al., 2007), including IAV-infected cells (Rand et al., 2012, von Recum-Knepper et al., 2015, Killip et al., 2017). In this context, it was shown that the IFN-β expression is a stochastic process, in which the type-I IFN response (which involves various components of a signaling cascade) must be self-amplified within a single cell, which ultimately leads to an all-or-nothing “fate decision”

whether to secrete IFN or not (Patil et al., 2015, Rand et al., 2012). Such a bimodal behavior

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is believed to exists as only a few IFN-secreting cells (the “first responder” cells) are sufficient to protect the whole cell population, which is accomplished by the paracrine stimulation of surrounding cells by the “first responder” cells (Rand et al., 2012, Talemi and Hofer, 2018, Patil et al., 2015).

Timm et al. studied the influence of the single-cell innate immune response upon infection with VSV (Timm et al., 2017). For this, the high-throughput microfluidic platform from Warrick et al.

(described above) was used (Warrick et al., 2016). Moreover, the authors used a reporter cell line, which was engineered to express a GFP upon activation of the innate immune response, together with a recombinant VSV expressing RFP as a reporter of viral gene expression (Timm et al., 2017). It was observed that the relative timing (rather than the magnitude) of either host immune or viral gene expression determined the outcome of an infection. More specifically, earlier viral or anti-viral gene expression either favored or hindered virus replication, respectively.

2.2.4.5. Whole transcriptome analysis

In 2018, the first studies dealing with the whole transcriptomic analysis of infected single cells using NGS technologies were published (Russell et al., 2018, Zanini et al., 2018, Steuerman et al., 2018). Russel et al. isolated single cells using a droplet-based microfluidic system (Zheng et al., 2017), followed by scRNA-seq. An extreme wide cell-to-cell variation in viral mRNAs was observed in IAV-infected cells (Russell et al., 2018). Moreover, the authors observed that some cells failed to produce at least one viral transcript, in line with previous observations (Brooke et al., 2013, Heldt et al., 2015). In addition, the authors correlated cellular genes with the abundance of viral mRNAs, and observed that the oxidative stress response showed a co-variation with viral gene expression (Russell et al., 2018). Later, Zanini et al. studied the whole transcriptome of dengue and zika virus-infected single cells (Zanini et al., 2018). The single cells were isolated using FACS, followed by NGS analysis. Afterwards, the authors correlated the viral load (i.e. viral mRNA abundancy) with host cell transcripts and identified novel antiviral and proviral factors (including proteins involved in the ER translocation, signal peptide processing, and membrane trafficking), which were validated in subsequent experiments.

Steuermann et al. conducted scRNA-seq of in vivo IAV-infected cells from mouse lungs (Steuerman et al., 2018). More specifically, the lung tissues were dissociated, and the single cells were isolated using FACS, followed by subsequent scRNA-seq analysis. Moreover, a diversity of cells types was investigated (macrophages, endothelial, natural killer, and dendritic

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cells). The authors identified generic infection responses, equal to all cell types (e.g. the interferon response), and those which are cell-type specific. Moreover, the authors observed that the suppression of mitochondrially encoded genes was largely independent from the IFN induction. Therefore, it was suggested that both mechanisms may represent independent lines of defense of the host cells against virus infection.

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