Viral tag and grow: a scalable approach to capture and characterize infectious virus–host pairs

Improving our understanding of “viral tagging” flow cytometric signals

VT is a deceptively simple idea whereby a mixture of natural viruses are labeled with a DNA-binding fluorescent dye and ‘bait’ hosts infected by these stained viruses can be detected with flow cytometry via the fluorescent shift of “viral-tagged cells” [38, 39] (Fig. 1A, B). These viral-tagged cells can then be sorted, and the viral DNA separated using isotopic fractionation (the DNA of the cultured host is pre-labeled with “heavy” DNA) to access the metagenomes of the viruses that were experimentally determined to have infected these cell types. However, in practice, VT has been only minimally adopted by the community [43], presumably because it requires costly equipment (a high-performance flow sorter) and diverse technical expertise (flow cytometry, phage biology, and bioinformatics), while lacking sufficient benchmarking. To the latter, we sought to use a cultured phage-host model system (Pseudoalteromonas strain H71, hereafter H71, and its specific myophage PSA-HM1, hereafter HM1) to systematically assess the impact of various multiplicities of infection (MOIs; the ratio of the number of virus particles to the number of target cells, [48]) on the resultant VT signals. Further, we sought to augment VT to add an “and grow” capability whereby scalable single-virus cultivation, characterization, and sequencing could be enabled (Fig. 1C).

To gain a better understanding of the biology behind VT signatures, we examined how H71 interacts with HM1, a phage specific for this host, and HS8, a phage that does not adsorb to this host – both assayed via flow cytometry and microscopy (for details, see Methods and online protocol, https://www.protocols.io/view/viral-tagging-and-grow-a-scalable-approach-to-captbwutpewn?form=MY01SV&OCID=MY01SV). Briefly, phages were stained with SybrGold (fluoresces green upon blue-light excitation) and for microscopy, H71 cells were stained with DAPI (fluoresces blue upon blue-light excitation, 4′,6-diamidino-2-phenylindole), as previously described [39, 49]. Replicate cultures of stained cells were then mixed with fluorescently-labeled phages (either HM1 or HS8 in each treatment) at infective MOIs = 1, 2, and 4, then these infections were incubated for 10 min, and processed (centrifuged and resuspended; see Methods for details) three times to remove free phages (see Methods for details). For microscopy, the relative fraction of virus-tagged (VTs) and nontagged cells (NTs) was measured from the available cells up to ~500 cells for each sample. For flow cytometry, cell detection was optimized to minimize background noise [50], and negative controls consisted of stained and washed sheath buffer and filtered Q water samples, as previously described [39].

Overall, the resulting VT experiments were robust and informative. First, our cell-only optimizations resulted in controls that were impeccably clean (see representative cytograms and gating counts in Fig. 2A–C and  Supplementary Fig. S1). Second, in “virus addition” treatments, the resultant VT signal was distinct for specific (HM1) versus nonspecific (HS8) phages. Specifically, adding HM1 at MOIs = 1, 2, and 4 corresponded to VT population shifts of an average of 25%, 50%, and 80%, respectively, while NT populations proportionally decreased (Fig. 2D, E, linear regression r² = 0.98). In contrast, for all tested MOIs of the nonspecific HS8 phage, the shifted populations were negligible (range: ~1.0–1.9%) and uncorrelated (Supplementary Fig. S2A, B; r² = 0.14).

Despite observing a strong linear correlation between MOI and %VT for HM1, it was surprising that even at high MOIs = 1, 2, and 4, the resultant population shifts were 1.2- to 2.5-fold less than expected from theory alone based on Poisson distribution (see Supplementary Fig. S3). To investigate this, we used microscopy to inspect for virus clumping, positioning relative to cell surfaces, and background noise. These results revealed spot-like green signals of various sizes outside of host cells, which we interpreted as free viruses, and this was true even (a) at these higher MOIs, and (b) despite centrifugation to remove free viruses following incubation (see Methods; Fig. 2F and  Supplementary Figs. S4–7). We suspect these unincorporated SYBR-stained particles are viral aggregates, possibly due to host cell parts and/or debris in the lysate [51,52,53] or tangling of phage tails [54]. Prior work has shown that these and other mechanisms that decrease the accessibility of viral particles to host receptors could reduce observed infectious particles [48].

Our third key observation in these experiments rested with an improved understanding of the ‘signal shift’ between VT and NT populations in the flow cytogram across varied MOIs. Again, comfortably, increasing the MOI pushed VT signals toward higher fluorescence, with NTs decreasing proportionally (Fig. 2F). We posited that such increased “VT” signal could result from multiple phages adsorbing per cell. Indeed, microscopy visualization of ~500 single cells per treatment revealed that the number of detectable phages per infected cell increased proportionally to the MOI (Fig. 2F, G and  Supplementary Figs. S4–6). For example, of the tagged cells, few (~14%) cells exhibited multiple phages adsorbed at an MOI = 1, whereas those numbers increased drastically at MOIs = 2 and 4, where most (~55% and 67%) tagged cells exhibited multiple adsorbed phages per cell. As a negative control, we examined VT signals for a nonspecific phage, and this revealed that virtually all of the 545 single cells that were examined were nontagged (99.3%) even at an MOI = 10 (Supplementary Fig. S7). Presumably, the remaining ~0.7% of cells that appeared to have a phage adsorbed represent promiscuous, reversible binding to nonhost cells as is known to occur in other phage model systems [39]. Mechanistically, multiple phages can bind to a single host cell. For example, under very high-titer infection conditions (e.g., MOI = 100) phages can distribute over an entire cell surface [55], presumably accessing broadly distributed receptors [56]. Prior VT work has demonstrated strong VT signals under very high MOI (e.g., MOI = 1000) conditions [43], though no optimization experiments were presented to understand these patterns and the false positives that would result from free phages coincidently sorted (see further discussion later).

Finally, we re-evaluated the impact of cell physiology (e.g., early, middle, and late log phase host growth) and adsorption time (e.g., 20 min intervals from 0 to 120 min) on Pseudoalteromonas VT signals—and did so at two MOIs = 1 and 4, respectively (Fig. 2H). At both MOIs tested, growth phase was seen to impact the VT signals, with late log phase cells showing the highest fluorescent shift for VT cells in contrast to signals that were reduced in early log phase cells and nearly absent from stationary phase cells (Fig. 2H). This finding is consistent with our prior optimizations with Pseudoalteromonas phage-host model systems [39]. However, we observed that VT signals were optimal at 20 min after adsorption (see Methods) and, rather than stay high as we had previously observed, these experiments revealed that the VT signals were reduced by nearly half at subsequent time points. Though conflicting with our prior work [39], these current experiments employ hierarchical gating (Supplementary Fig. S1; see Methods), which we feel more appropriately quantify these patterns. This is because we interpret the signal reduction to be due to the lysis of first-adsorbed tagged cells and/or the injection of fluorescent DNA of the adsorbed virus(es) into cells as the latent period of phage HM1 for H71 cells under these conditions dictates [24]. Indeed, it has been reported that for phage lambda—E.coli system, the injection of fluorescent phage DNA followed by signal diffusion inside the cells decreased ~40% of the overall signal intensities of individual virus–host pairs [57].

Together, though an extensive set of experiments, these findings are largely confirmatory with our prior work characterizing Pseudoalteromonas phages [39]. However, and critically, our prior work failed to rigorously investigate these phenomena with respect to their (i) flow cytogram population signatures, (ii) single-cell microscopy imaging, and (iii) hierarchically gated tagged-cell timing estimates. We hope that these additional clarifications here provide a better mechanistic understanding of VT signals, and encourage wider adoption of this promising high-throughput method to identify viruses that infect a particular host.

Introducing VT and grow: VT coupled to plate-based cultivation assays

Given this improved understanding of the VT signal, we next sought to expand VT to include an “and grow” capability to scalably capture and characterize viruses linked to hosts (conceptually presented in Fig. 1C). Pragmatically, this should also help resolve long-standing questions of (i) what fraction of VT cells lead to productive infections (i.e., does adsorption equal infection?, [45]), and (ii) whether sample processing (e.g., laser detection, sheath fluid growth inhibition [37, 58]) or cell density effects resulting from single-cell sorts [59, 60] would prohibit downstream growth assays.

To this end, we used the Pseudoalteromonas-virus HM1 model system to optimize sorting and growth conditions. Specifically, we wondered how many cells from sorted populations would be required to observe lysis (both dynamically, and terminally) under various MOI conditions. To test this, viral-tagged cells (the “VT” treatment) or nontagged cells (the “NT” treatment) were sorted into individual wells of a 96-well plate containing growth medium; fresh host cells were added, and growth-lysis curves were established by measuring optical density (OD) over time (see Methods). Treatment variables included the number of cells sorted (n = 1, 3, or 9) and infection conditions (MOI = 1 or 4), while controls included (i) NT cells to control for false-positive culture lyses by free viruses coincidently sorted with target cells, and (ii) sorting process controls against host cell lysis and growth in plates consisting of wells containing cultures with and without phage HM1, respectively. For all experiments, cells were infected during late-exponential phase for 10 min, followed by dilution to halt further infection, and centrifugation to remove free viruses (see Methods, [41]).

We first analyzed the reduced-titer MOI = 1 infection. When only single cells were sorted, the growth curves from those wells as compared to those of phage-free controls, showed that more than half (56%; 20/36) of the VT wells with detectably reduced OD, whereas only a single NT well (8%; 1/12) showed such a decrease (Fig. 3A). This low rate of false-positive culture lysis in NT wells suggests that in most of the VT wells, progeny phages produced from an isolated parent VT—not free viruses―infect and lyse the host culture (For more details, see the burst size distribution of sorted single VTs below). Presumably, the 16 VT wells that did not lyse were due to one of the following: (i) reduced viability of isolated VTs through multiple steps of sample preparation or sorting with high sheath pressure [37, 58], (ii) possible reversible virus adsorption from the VT cell prior to well capture, and/or (iii) mis-diagnoses due to the weak fluorescent shift of singly-VT cells as is a known challenge in fluorescence-based cell sorting [58, 61].

To assess the MOI = 1 infections further, we evaluated the data for wells containing more than 1 cell sorted to each well. This revealed that sorting 3 or 9 cells improved the fraction of wells lysed in the VT treatments to 88 and 100%, respectively, but this came at the cost of increased false positives in the NT treatment (pie charts in Fig. 3A). The latter is likely due to the same challenges described above of differentiating the NT from VT populations when signal intensity was relatively low. Given the 96-well plate format, these experiments demonstrate the ability to follow growth kinetics for each well (time course OD figures in Fig. 3A). This revealed that single VT cell sorts had delayed lysis relative to the multiple-cell sorts and hints at the power such kinetics data could provide for scalably characterizing new en masse captured phage isolates from field samples. Stepping back, however, it is promising that the number of sorted cells per well, for both VT and NT wells, was linearly proportional to the percentages of lysed wells (r² = 0.73 and 0.99), respectively (Supplementary Fig. S8). This suggests a robustness and repeatability for these experiments.

Beyond the fraction of the VT and NT wells displaying clear lysis, the kinetics of lysis—particularly for single-cell sorts—can be a valuable first read-out for variability in virus infection dynamics. To assess this in our dataset, we examined the kinetics of OD readings through 20 h (growth-lysis curves in Fig. 3A). Focusing on the 36 wells containing a single VT cell, 20 lysed (reported above), but their lysis kinetics drastically differed—some wells showed stepwise decreases after early increases in OD and the others a very low or no increase followed by the curve recovery. Similar lysis patterns have been observed in other phage-host systems, where host culture growth depended on phage concentration, with suppression of host cells increasing with higher phage titers and vice versa [62, 63]. Our observation of the well-to-well variation in culture lysis is likely due to different progeny production from isolated VT per well, relating to the stochasticity of viral infection [37, 64,65,66,67]. However, the stochastic infection alone cannot explain such diverse lysis patterns, given the random nature of diffusion and contact of progeny particles from infected cells to neighboring susceptible cells in the fluid (i.e., the host culture) [68, 69]. Either biological or physical infection process, or both, could impact varied lysis pattern. Further experiments are required to test this hypothesis (e.g., single-cell burst size assay, [37]; see below).

Finally, given that flow cytometric population separation was critical for optimizing lysis success and that simply sorting more cells comes at the cost of increased false-positive lysis, we next explored the impact of increasing the per-cell fluorescent VT signal with MOI = 4 infections. Indeed, sorting from these better-resolved populations improved our per-well lysis results as all of the VT wells lysed, and this was the case whether sorting 1, 3, or 9 cells per well (pie charts in Fig. 3B). For the NT wells, false positives were less problematic, but they did remain a minor problem as some wells (4–8%) lysed, and this increased in the multiple-cell sorted wells. Though VT and NT populations are likely better resolved, thereby reducing false-positive lysis in the NT wells from the MOI = 1 infections, presumably the higher MOI infections lead to free viruses being coincidently co-sorted in the sort droplets. Notably, the kinetic read-outs (growth-lysis curves in Fig. 3B) were relatively invariable, possibly suggesting that the much higher number of viruses-per-cell in these infections obscured virus-to-virus variability in life history traits [66, 67, 70].

Together, these experiments provide strong baseline data for assessing the impact of VT signal quality, MOIs, and growth data and hint that the approach may also open up new windows into variation in trait space across virus isolates.

New biology enabled by viral tag and grow: a window into “viral individuality”?

A major challenge in viral ecology is scaling from the handful of viruses that might be well characterized to the millions of virus types in an average seawater or field sample. While diversity surveys have come a long way (e.g., hundreds of thousands of viruses in a single study [23]), the pragmatic challenges of taking physiological measurements across many viral isolates leaves modeling efforts with very little empirical data on virus life history traits, severely bottlenecking the viruses brought into predictive models [71]. Further, microbiologists have revealed that even among “clonal” isolates, there can be remarkable phenotypic heterogeneity, or “microbial individuality” [72,73,74]; does the same exist for viruses? Hints that there is such “virus individuality” among DNA viruses, including phages, are emerging with data demonstrating variability in single-cell burst size (progeny per infected cell), with up to ~100-fold differences and these differences attributed to stochastic events such as variation in starting points in cell size, growth stage, and resources [37, 64,65,66].

Of particular interest in understanding ‘virus individuality’ are recent single-cell analyses developed for a Synechococcus phage-host model system that revealed a wide range of burst sizes (from 2 to 200 infective viruses/cell) within a laboratory clonal isolate [37]. Methodologically, this approach sorts cells—infected or not—into wells (e.g., of a 96-well plate) and follows their infection dynamics. This has the benefit of assessing a single cell’s growth-lysis curve in each well. However, a drawback is that experiments are more conveniently done at high MOI conditions (e.g., an MOI = 3 was used) to get larger numbers of wells lysing among the randomly sorted cells (see Methods). Increasing MOI will lead to more virus-containing and, therefore, lysing wells, subsequently greatly increasing the number of cells with multiple viruses attached such that it will confound measurements of lysis dynamics since they will be a function of both virus-to-virus ‘individuality’ and an unknown, but variable per-cell MOI [70, 75].

Inspired by this latter work, we sought to improve such single-cell growth-lysis assays in ways that might leverage the scalability of VT + Grow. For these experiments, we wanted to reduce the MOI (to MOI = 0.5) since theory predicts that most (77%) of the infected cells would be singly infected (Poisson distribution), but keep it high enough to have a reasonably separated VT cell population (see Methods). After cells and viruses were mixed, individual VT cells were sorted into different wells containing growth medium, plates were incubated to allow lysis of the single sorted VT cell, and the number of plaques per well were determined by pour plate plaque assays (Fig. 4A; see Methods for details). This operationally single-cell burst size assay showed a wide range of infective viruses per cell (2 to 397, X-axis) from a total of 72 individual cells assessed (Y-axis) (on average = 100; Fig. 4B), with similar average population burst sizes of 110 ± 15 [24]. Though a clonal virus isolate, these findings suggest, just as seen for cyanophages [37], that stochastic events must dictate the specific burst size for any given interaction. However, unlike the prior work, it is unlikely that cells with multiple viruses adsorbed any of this signal since such events should be much rarer at an MOI = 0.5 instead of MOI = 3. This suggests that these stochastic events are of a biological nature, which we posit might mechanistically result from the timing of initial virus–host interactions and/or cell-to-cell or virus-to-virus variation in nonheritable traits such as per-cell nutrient stores. If we interpret such infected cell variability as ecologically relevant variation in “virocells” (sensu [13, 76, 77]), then these findings open a window into “virus individuality” via a more scalable and controllable characterization approach than previously available.

Limitations and future development opportunities for VT and Grow

Though these efforts provide a more robust foundation for broadening the use of VT related methods, there remain challenges. First, researchers must be aware that VT is not a simple method, and its success depends on instrument calibration and ultraclean sample processing to establish maximally separated VT and NT populations (see the link below for details on flow cytometric setup and optimization). Second, sorting purity, particularly in field applications, will be challenged by suboptimal VT flow cytometric signatures, e.g., mis-identification of NT cells. Though this can be overcome with very high MOI infections (e.g., 1000 viruses per cell, [43]), two issues remain: (i) the effective MOIs cannot be measured in field samples (and thus, unknown), and (ii) at such high MOIs, the experiments will suffer from coincident sorting of free viruses that will increase false positives. Another factor that could affect sorting purity is nonviral DNA in the environmental sample, whether it is associated with bacterial cells or not, which could be coincidently sorted. It is thus necessary to ensure that prior to any VT work, environmental samples are properly processed or treated for the removal of nonviral genes and other materials (e.g., filtration and/or centrifugation). Fortunately, the “and grow” approach added to VT provides an additional screening step whereby false-negatives and false positives can be discerned via growth-lysis monitoring. Further, the “and grow” component, a plate-based assay, enables faster and more scalable lysis screening (e.g., 96-well format) than the time- and labor-intensive traditional plaque assay [62, 63]. Third, viral aggregates that alter the effective MOI infection conditions could lead to confounding results when comparing results across laboratories. Here, we invite efforts to find and optimize approaches to reduce viral aggregates (e.g., detergents, sonication, syringe pumping), and until viral aggregates are eliminated, to microscopically examine the state of free viruses in new sample types, particularly for outlier results. Fourth, the methods remain dependent upon a cultivable host, and though VT has been applied to multiple heterotroph and cyanobacterial phage-host pairs [39], two big unknowns remain: (i) how will the “and grow” processing impact growth of these strains, and (ii) will non-marine model systems be amenable to these approaches. The in-depth optimizations presented here for a Pseudoalteromonas phage-host model system serve a foundation for understanding other target virus–host pairs. To this end, we suggest deep investigation for any new model systems being studied, and as information becomes more broadly available, invite a community-standards and benchmarking approach to determine ideal setups for infectious conditions (e.g., growth curve, MOIs) and instrumental parameters. To facilitate this, we have established a VT forum on the Viral Ecology VERVE Net living protocols at protocols.io (below) as a way to empower and broadly engage researchers interested in these new methods and the many variants that could blossom from this base. Specifically, the details for viral and bacterial sample processing can be found at https://www.protocols.io/view/viral-tagging-and-grow-a-scalable-approach-to-capt-bwutpewn?form=MY01SV&OCID=MY01SV and for flow cytometric optimization at https://www.protocols.io/view/bd-influx-cell-sorter-start-up-and-shut-427down-for-v-bv8cn9sw. Both protocols provide additional notes for critical steps to improve methodological reproducibility and/or sensitivity, and particularly for the latter, it will be updated regularly to better optimize, calibrate, and standardize a flow cytometer.

Source: Ecology - nature.com