Evaluation of the Characteristics and Infectivity of the Secondary Inoculum Produced by Plasmopara viticola on Grapevine Leaves by Means of Flow Cytometry and Fluorescence-Activated Cell Sorting

ABSTRACT

Plasmopara viticola, the oomycete causing grapevine downy mildew, is one of the most important pathogens in viticulture. P. viticola is a polycyclic pathogen, able to carry out numerous secondary cycles of infection during a single vegetative grapevine season, by producing asexual spores (zoospores) within sporangia. The extent of these infections is strongly influenced by both the quantity (density) and quality (infectivity) of the inoculum produced by the pathogen. To date, the protocols for evaluating all these characteristics are quite limited and time-consuming and do not allow all the information to be obtained in a single run. In this study, a protocol combining flow cytometry (FCM) and fluorescence-activated cell sorting (FACS) was developed to investigate the composition, the infection efficiency and the dynamics of the inoculum produced by P. viticola for secondary infection cycles. In our analyses, we identified different structures within the inoculum, including degenerated and intact sporangia. The latter have been sorted, and single sporangia were directly inoculated on grapevine leaf discs, thus allowing a thorough investigation of the infection dynamics and efficiency. In detail, we determined that, in our conditions, 8% of sporangia were able to infect the leaves and that on a susceptible variety, the time required by the pathogen to reach 50% of total infection is about 10 days. The analytical approach developed in this study could open a new perspective to shed light on the biology and epidemiology of this important pathogen.

IMPORTANCE P. viticola secondary infections contribute significantly to the epidemiology of this important plant pathogen. However, the infection dynamics of asexual spores produced by this organism are still poorly investigated. The main challenges in dissecting the grapevine-P. viticola interaction in vitro are attributable to the biotrophic adaptation of the pathogen. This work provides new insights into the infection efficiency and dynamics imputable to P. viticola sporangia, contributing useful information on grapevine downy mildew epidemiology. Moreover, future applications of the sorting protocol developed in this work could yield a significant and positive impact in the study of P. viticola, providing unmatched resolution, precision, and accuracy compared with the traditional techniques.

INTRODUCTION

Grapevine downy mildew, caused by the phytopathogenic oomycete Plasmopara viticola (Berk. et Curtis) Berl. & De Toni (kingdom Chromista, phylum Oomycota, class Oomycetes, order Peronosporales, family Peronosporaceae), is one of the major threats to grapevine production worldwide. Indeed, severe disease epidemics caused by this oomycete are often associated with consistent quantitative and qualitative yield losses (1).

P. viticola is an obligate, biotrophic pathogen, able to undergo numerous infection cycles during a single grapevine-growing season (2). In autumn, the pathogen develops overwintering structures differentiated by sexual reproduction (oospores), through which it survives the winter period embedded in dead leaves on the vineyard floor (2–4). In spring, during favorable climatic conditions (5), oospores germinate producing macrosporangia that, in turn, produce zoospores. Receptive tissues of grapevine leaves are infected by zoospores through splashing by rain, which leads to the primary infections through stomata penetration (6, 7). The pathogen develops an intercellular mycelium with haustoria (feeding structures) and, in high humidity conditions, differentiates into sporangiophores that emerge from stomata and produce sporangia, which will originate secondary infections cycles through the emission of new zoospores (2).

Oospores are considered to play a principal role in triggering the epidemics in the early grapevine season, providing the inoculum for primary infections, while the subsequent stages (secondary infections) are more attributed to the inoculum generated by asexual reproduction through the differentiation of sporangia (1, 3).

Although there are many testing methods to investigate the oospore germination process and oospore infection efficiency (3, 5, 8–12), to the best of our knowledge no studies have been conducted to evaluate the infection potential associated with the sporangia inoculum in vineyards. To date, testing methodologies on sporangia germination are limited because they can provide only a qualitative description of the infection process, and it is not possible to obtain a precise estimation of the percentage of sporangia able to positively infect grapevine plants in the population tested (13, 14). The only exception to this statement is represented by a few methods developed for the quantification of sporangia germination, which are based on visual observation under the microscope or at the spectrophotometer of zoospores’ release (14–19). However, the reliability of this type of data are quite limited, as it only evaluates the release and mobility of the zoospores in aqueous suspension without considering the outcome of the infection process on grapevines’ tissues. For these reasons, the data obtainable by investigating the primary and secondary infection cycles are not homogeneous and create a gap in the available information.

Fluorescence-activated cell sorting (FACS) is a technique to purify specific cell populations based on phenotypes or fluorescence detected by flow cytometry (FCM), which can represent an interesting opportunity to improve information on the sporangia infection process (20–22). Briefly, FCM is a technology able to provide rapid multiparametric analysis of single cells in suspension using lasers as light sources to produce both scattered and fluorescent light signals that are read by detectors such as photodiodes or photomultiplier tubes (23). These signals are converted into electronic signals which are analyzed by a computer. FACS implementation provides a method for separating a heterogeneous mixture of cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell (24). Considering the continuous advancement of these technologies, the use of FCM rapid protocols to quantify single cells, such as fungal spores, can be set up. Indeed, FCM allows the researcher to monitor different parameters to distinguish between living and dead cells, such as membrane permeability, efflux pumps, or enzymatic activity and loss of membrane potential (25).

Implementation of FCM technologies in fungal plant pathogens investigations could be a useful and innovative approach, which in the case of P. viticola, could permit us to bridge the gap of information obtainable by studying the infection process in the different life stages of pathogens as mentioned above. This could represent a big step forward in the study of grapevine downy mildew, allowing the study of the secondary infection cycles of the pathogen with greater completeness and providing quantitative data. To date, no reports of applications of FMC on infection efficiency screening in plant pathogens are available. To the best of our knowledge, the only implementations of FCM have been carried out on the related oomycete species Phytophthora infestans (Mont.) de Bary (kingdom Chromista, phylum Oomycota, class Oomycetes, order Peronosporales, family Peronosporaceae) (26, 27). In particular, Day and collaborators used FCM to discriminate P. infestans sporangia from other airborne biological particles by using light scatter parameters and fluorescent staining. A single study was performed on P. viticola, to determine the sporangia viability exposed to chlorine dioxide using fluorescent dyes (28).

The aim of the present work was to characterize the sporangia suspension composition of P. viticola and to evaluate its infection efficiency and dynamics. This was accomplished by developing a FACS-mediated single-sporangia infection assay.

RESULTS

The molecular characterization confirmed that the ITS sequence of the isolate used in this study (OP326699) belongs to P. viticola species (98.1% identity with P. viticola internal transcribed spacer 1, 5.8S rRNA gene, and internal transcribed spacer 2, GenBank id DQ665668.1).

FCM and light microscopy discriminate the different components of P. viticola inoculum.

The sporangia suspension of P. viticola obtained from infected grapevine leaves was analyzed by FCM. First, the analysis was carried out considering FSC-H and SSC-H parameters related to the ability of a particle to scatter the light. In detail, FSC-H and SSC-H are proportional to particle size and granularity, respectively (29). Results were represented in a two-dimensional plot (Fig. 1A). Both parameters have been shown to correlate with cell size and particle composition/complexity (30) and were represented with logarithmic scale. Based on FSC-H, we identified three populations (gates R3, R4, and R5) with different dimensions (R5 < R3 < R4). Indeed, the higher the FSC-H, the larger the particle. Interestingly, population R3 was composed by two subpopulations (R3a and R3b), differing in the SSC-H parameter. Particularly, R3a was characterized by a lower SSC-H value compared with R3b. Based on these data, SSC-H signal resulted the most discriminating parameter (rather than FSC-H) for highlighting the morphological heterogeneity of a complex suspension.

FIG 1.

(A) FSC-H (particle size) versus SSC-H (granularity) density plot of a P. viticola sporangia suspension. (B) SSC-H (granularity) versus red fluorescence (670LP-H) density plot: four distinct subpopulations are visible (R3a, R3b, R4, and R5).

It is known that certain biological particles exhibit “autofluorescence.” Indeed, in Blumeria graminis DC Speer (kingdom Fungi, phylum Ascomycota, class Leotiomycetes, order Erysiphales, family Erysiphaceae) and P. infestans nonviable conidia and killed sporangia, respectively, exhibited red autofluorescence (27). Based on these findings, we analyzed P. viticola sporangia suspension by correlating light scattering (SSC-H) and intrinsic fluorescence parameters collected in the FL3 channel (Fig. 1B). It clearly appeared that some of the particles exhibited various levels of red fluorescence. Red fluorescence values were comparable between R4 and R5 populations (1,071 ± 53 RFU and 1,247 ± 62 RFU, respectively). Conversely, R3a and R3b populations (those derived from R3 based on FSC-H and SSC-H) exhibited higher levels of fluorescence (382,138 ± 19,106 RFU and 27500 ± 1375 RFU, respectively).

Our results showed that SSC-H and red autofluorescence were the most useful parameters to differentiate particles of the sporangia suspension.

After FCM detection of four different subpopulations based on SSC-H and red autofluorescence (Fig. 1B), P. viticola sporangia suspension was used in a FACS analysis to characterize the particles present in each subpopulation. Populations R3a and R3b, R4, and R5 were sorted based on their red autofluorescence and separately collected in sterile tubes. A postsorting analysis was carried out by FCM to assess the purity of each subpopulation (Fig. 2).

FIG 2.

SSC-H (granularity) versus Red-fluorescence (670LP-H) density plots of the P. viticola four subpopulations before (A) and after FACS sorting (B to E). Subpopulation sorting is visible in B (R4), C (R3b), D (R3a), and E (R5) plots.

Subsequently, each subpopulation was analyzed under the microscope to associate a specific morphology (Fig. 3). Specifically, the population R5 (low SSC-H and low red autofluorescence) encompassed debris (small particles) (Fig. 3D), encysted zoospores (Fig. 3E), and biflagellate swimming zoospores (Fig. 3F). Conversely, subpopulations R3a (low SSC-H, high red autofluorescence) and R3b (high SSC-H, middle red autofluorescence) corresponded to damaged sporangia. In particular, we observed that emptying sporangia with broken cell wall and intracellular content loss were present in the subpopulation R3a (Fig. 3C). Sporangia with open operculum (Fig. 3B) and vacuolated cytoplasm characterized the subpopulation R3b. Intact P. viticola sporangia were encompassed in subpopulation R4 (high SSC-H, low red autofluorescence) (Fig. 3A).

FIG 3.

Microscopy pictures taken after FACS sorting of the different subpopulations. Intact P. viticola sporangia isolated from subpopulation R4 (A). Sporangia with open operculum and granular cytoplasm from subpopulation R3b (B). Emptying sporangia from subpopulation R3a, with broken cell wall and cytoplasm loss (C). Undefined debris isolated from population R5 (D). P. viticola encysted zoospore (E) and biflagellate swimming zoospores (F) isolated in population R5. Op, operculum; Sp, sporangia; Zp, zoospore; fl, flagella; cyt, cytoplasm loss.

P. viticola infection efficiency is determined by FACS-sorting individual sporangia.

The isolate proved to be highly virulent, as demonstrated by the average 75.1 ± 6.2% (standard deviation) disease severity assessed by inoculating the overall sporangia suspension on grapevine leaves. Subpopulation R4 (containing intact P. viticola sporangia) was selected to be sorted in the 24-well microtiter plates containing grapevine leaf discs. Indeed, as the aim of this experiment was the evaluation of sporangia infection efficiency, it would not have been productive to inoculate emptying sporangia with broken cell wall, debris, or biflagellate zoospores. Although the latter are theoretically capable of generating infections on their own and can be sporadically found inside the emptying sporangia or debris, the reproductive structure within which they form (the sporangium) produces numerous zoospores with the same genetic heritage, considerably increasing the chances of positive infection if inoculated. Moreover, the zoospores are cell wall-lacking structures with a short life (hours), whereas sporangia can survive for at least 1 day (2). Therefore, it was reasonably chosen to investigate the subpopulation R4 for the infection assay.

Results obtained in the four biological replicates are reported in Table 1. The cumulative number of single sporangia able to infect the leaf discs within 14 days after inoculation (DAI) into the 24-well microtiter plates, ranged from a minimum of zero in plate number 4.2 to a maximum of four, obtained in plates numbered 3.1, 3.2, and 4.4 (Table 1). The average value of infection efficiency (IE) (%) for the four biological replicates was 8.3%.

TABLE 1.

Cumulative daily number of infected leaf discs and infection efficiency (IE %) defined as the percentage of infected leaf discs over the total, for each of the 24-microtiter plates of the four experimental replicates

Exptl repetition and plate	Days after inoculation											IE %
	4	5	6	7	8	9	10	11	12	13	14
Repetition 1
Plate 1.1	0	0	0	1	1	1	1	1	1	1	1	4.2
Plate 1.2	0	0	1	1	1	1	1	1	1	1	1	4.2
Plate 1.3	0	0	1	2	2	2	2	2	2	2	2	8.3
Plate 1.4	0	0	2	2	2	2	2	2	2	2	2	8.3
Repetition 2
Plate 2.1	0	0	0	0	1	1	1	1	1	1	1	4.2
Plate 2.2	0	0	0	1	1	1	1	1	1	1	1	4.2
Plate 2.3	0	0	2	2	2	2	2	2	2	2	2	8.3
Plate 2.4	0	0	1	2	2	2	2	2	2	2	2	8.3
Repetition 3
Plate 3.1	0	1	1	4	4	4	4	4	4	4	4	16.7
Plate 3.2	0	1	1	3	4	4	4	4	4	4	4	16.7
Plate 3.3	0	0	0	1	1	1	1	1	1	1	1	4.2
Plate 3.4	0	0	1	1	1	1	1	1	1	1	1	4.2
Repetition 4
Plate 4.1	0	1	1	1	3	3	3	3	3	3	3	12.5
Plate 4.2	0	0	0	0	0	0	0	0	0	0	0	0.0
Plate 4.3	0	0	2	3	3	3	3	3	3	3	3	12.5
Plate 4.4	0	0	1	4	4	4	4	4	4	4	4	16.7

In general, no sporulated leaf discs were identified before 5 DAI, and the peak of infection was reached between 7 (biological replicate no. 1) and 8 (biological replicates no. 2, 3, and 4) DAI (Table 1). No new sporulated leaf discs were detected afterwards. The proportions of infected grapevine leaf discs calculated on the total for each population were close together and ranged from 6.3% (replicates 1 and 2) to 10.4% (remaining replicates).

The study of infection dynamics reveals the median sporangia infection timing.

The generalized linear mixed model (GLMM) fitted well, describing the infection dynamics (ID) reproduced by the single-sporangia assay (pseudo-R² = 0.9911) (Fig. 4A). Indeed, all the simulated IE (%) values obtained at any DAI were included within the 95% confidence limits computed for the overall observed percentage of positively infecting sporangia, regardless the experiment. Overall, the type II solution for the GLMM parameters (Table 2) showed a highly significant effect of time (DAI) on the response variable (p(>χ²) < 0.001), whereas the experiment’s effect did not appear to significantly affect the ID (p(>χ²) < 0.598). On the other hand, glancing at Fig. 4B, one can observe that t₅₀ bootstrap empirical distribution approaches the normal density distribution. The bootstrap t₅₀ computed value was 9.795 DAI, its 95% lower confidence limit (LCL) was 9.632 DAI, and its upper confidence limit (UCL) was 9.977 DAI. The GLMM t₅₀ value was 9.797; thus, it was included within the bootstrap t₅₀ 95% confidence limit. Noteworthy, the difference between GLMM t₅₀ and bootstrap t₅₀ was 0.002 DAI, i.e. 2 min and 53 s.

FIG 4.

Sporangia infection trend over time (DAI) for each experiment (A) and comparison between bootstrap t₅₀ and GLMM t₅₀ (y axis reports density of probability associated with Bootstrap and normal distributions (B).

TABLE 2.

Wald’s χ² test (type II solution) for GLMM fixed parameters

Parameter	χ2	DF	p(>χ2)
${\beta }_{\text{ti}}$ (time effect in days after inoculation)	2778.6480	1	<0.001
${\beta }_{\text{j}}$ (expt, with j = {Experiment1, Experiment2, Experiment3, Experiment4}	1.8803	3	0.598

DISCUSSION

The combination of FCM, FACS, and microscopy analyses allowed us for the first time to thoroughly characterize the complexity of a P. viticola sporangia suspension, highlighting the presence of different particles with specific morphology and sporangia with different levels of integrity.

Indeed, FCM allowed us to detect morphological heterogeneity within a population (not only based on SSC-H or FSC-H, but more interestingly based on the intrinsic fluorescence).

The sporangia suspended in aqueous solutions at room temperature naturally tend to open their operculum and release the zoospores, so that they can move in the fluid by the movement of flagella to generate new infections (2). Considering the information on P. viticola macrosporangia ultrastructure (7) and similar studies performed on related oomycete species belonging to Phytophthora spp., such as P. infestans (31) and Phytophthora parasitica Dastur (kingdom Chromista, phylum Oomycota, class Oomycetes, order Peronosporales, family Peronosporaceae) (32), mature sporangia possess a more complex cytoplasm compared with immature spores. Indeed, the mature sporangia are characterized by the presence of structures related to the zoosporogenesis process like flagella, basal bodies, and cleavage vacuoles. The increase in cytoplasm complexity associated with indirect germination matched with microscope observations performed on the subpopulation R3a (Fig. 3B) and could explain the increase in red autofluorescence observed in subpopulations R3a and R3b, compared to subpopulation R4. Usually, the increase in autofluorescence is reported in eukaryotic cells subjected to an increase in respiratory activity in unfavorable conditions (33).

Furthermore, the presence in the cell suspension of sporangia in different physiological phases can be easily attributed to the asynchronous nature of the sporulation process on the host tissues (34). Finally, despite the preliminary filtration of the suspension, the presence of debris and other foreign particles of no relevant interest composing the subpopulation R5 is a common problem also reported by Day et al. (27) when handling material directly obtained from the host tissues.

In the last years, some attempts to account for P. viticola secondary inoculum viability and infectivity at field scale have been described (35, 36). However, no information on the real number of sporangia able to infect grapevine and on their infection dynamics over time are available. Our study revealed the possibility to induce the infection by sorting intact sporangia belonging to the subpopulation R4 onto grapevine leaf discs. The average yield in infected leaf discs was 8% and statistical analysis showed no differences in the infection dynamics between biological replicates, making the results quite sound. Therefore, to the best of our knowledge, because we cannot make any other comparison, we cannot conclude if the achieved IE value is high, average, or low until a massive investigation on different strains will be performed. To better evaluate the sporangia infection efficiency, further experiments are necessary. Indeed, the use of fresh field suspensions (not propagated) which possess a greater infection capacity (18), extending the analysis to different plant and pathogen genotypes and investigating the pathogen infection by means of aniline blue staining, could contribute to better defining the infection efficiency of P. viticola sporangia. Thanks to the protocol developed within the frame of this study, it would also be possible to assess if sporangia produce different numbers of zoospores and, if so, whether the number of produced zoospores influences the disease intensity. Furthermore, the developed method could be used for isolating, in a fast and precise way, the strains that are present on single leaves or on single oilspots, and characterize them for different features (virulence, genetic profile, fungicide resistance, etc.) that nowadays are poorly investigated at this level.

GLMM models represent a well-established statistical approach in plant pathology to model disease incidence (37, 38). The GLMM employed in this study describes well the percentage of positively infected leaf discs (IE) and its dynamics over time (ID). One of the crucial parameters used in plant disease epidemiology to describe disease progress is t₅₀ (time to reach 50% of disease) (39). Therefore, for the first time and with incomparable precision (single-sporangia scale), the GLMM t₅₀ estimated in this study can be assumed as an appropriate index to summarize the median sporangia infection time (ca. 9.8 DAI). Moreover, considering that the difference between GLMM t₅₀ and bootstrap t₅₀ was very short, we can assume that the single experiment does not affect both the infection efficiency and the ID, providing useful information on P. viticola secondary inoculum epidemiology.

Conclusion.

Overall, the single-sporangia infection assay developed in this study can represent an accurate technique for single sporangia isolation and infection efficiency evaluation, which allows a big step forward to study the secondary infection cycles of the pathogen and to provide quantitative data. In conclusion, the method proposed here opens new perspectives in different fields of study. On one hand, the isolation of single sporangia could allow better investigation of P. viticola biology with unmatched resolution (e.g., enzyme production, gene expression assays, or “omics” approaches). On the other hand, the developed infection assay can help to shed light on the plant-pathogen interactions (resistant and susceptible varieties), to develop predictive infection models, or for use in pathogen population studies, for example, to quantify resistant individuals in fungicide resistance management.

MATERIALS AND METHODS

P. viticola material.

The P. viticola monosporangial isolate n. 10, belonging to cluster 1 of Italian P. viticola population (40) and available at the collection of the Department of Agricultural and Environmental Sciences (DiSAA, University of Milan, Italy), was used in this study. This P. viticola strain, designated CAS, was isolated in 2016 as a part of an Italian downy mildew genetic study (40, 41) from a field population in north-eastern Italy (Casarsa della Delizia, province of Pordenone). The characteristics of the isolate geographic origin, cluster, and microsatellite profile are available on Dryad repository (https://datadryad.org/stash/dataset/doi:10.5061%2Fdryad.kh189328s). From its isolation to the beginning of the experimental activity, the isolate has been maintained through weekly propagation on detached grapevine leaves (cv Pinot noir) (42). Large numbers of sporangia for experiments were obtained by massively propagating the strain on 100 leaves.

The sporangia suspension was obtained by collecting the sporangia present on grapevine leaves 7 DAI in phosphate buffered saline (PBS pH 7.4, Merck, Darmstadt, Germany). Before use, PBS was filtered through a 0.2-μm pore size polyvinylidene fluoride (PVDF) membrane filter and sterilized in an autoclave (121°C, 20 min). Sporulating lesions of each of the leaves were placed one by one in a 50-mL glass beaker and rinsed with 5 mL of PBS by using a glass pipette to detach sporangia from the infected material. Sporangia concentration was determined by using Kova chambers (Kova International, Garden Grove, CA, USA) under a microscope (Zeiss Primo Vert, Carl Zeiss Microscopy, NY, USA) and adjusted to a final concentration of 1 × 10⁷ sporangia/mL. The sporangia suspension was filtered through a 30 μm-pore size strainer (Sysmex, Gorlitz, Germany) immediately before the FCM and FACS analyses. All the material used in these activities was previously sterilized in autoclave (121°C, 20 min).

Species identity.

The species identity of the strain was confirmed by sequencing the ITS region with the ITS6 (GAAGGTGAAGTCGTAACAAGG) and ITS7 (AGCGTTCTTCATCGATGTGC) primers (43). To this purpose, P. viticola sporangia were collected in sterile distilled water (as described in the previous section). Water was removed after centrifuging the suspension (10 min at 13,200 rpm) and the DNA extracted from the sporangia (44) was resuspended in 40 μL of TE (Tris-EDTA) buffer at 65°C for 2 min and checked for its purity and concentration (Nanodrop ND1000; Thermo Fisher Scientific, Rodano, Milan, Italy). PCR was performed in an Eppendorf Mastercycler (Eppendorf, Milano) thermal cycler using Phusion Plus PCR Master Mix (Thermo Fisher Scientific, USA), according manufacturer’s instruction. The PCR contained 1× Phusion plus PCR master mix, 0.5 μM each primer, and 50 ng of DNA. Thermal cycling conditions were: 98°C for 30 s followed by 35 cycles of 98°C for 10 s, 61.5°C for 10 s, and 72°C for 15 s, and a final extension of 72°C for 5 m. The amplicon length (312 bp) was verified by agarose (2%) gel electrophoresis and the PCR product was purified and Sanger-sequenced by Eurofins Genomics Italy using ITS6 and ITS7 primers. The consensus sequence (GenBank accession number OP326699) was compared with those present in NCBI database by using BLASTn software tool.

Flow cytometry analyses, fluorescence-activated cell sorting, and microscopy.

FCM analyses were performed on the Accuri C6 Plus (BD Biosciences, Franklin Lakes, NJ, USA) flow cytometer equipped with a blue (488 nm, 20 mW) laser. Forward scatter (FSC), side scatter (SSC), and red fluorescence (>670 nm in FL3 channel equipped with 670LP filter) signals were collected by acquiring 9,000 events. All parameters were collected as logarithmic signals, and data were analyzed with the BD Accuri C6 Plus software (version 1.0.23.1).

To characterize the composition of P. viticola sporangia suspension, individual subpopulations detected by FCM analysis (Fig. S1A) were isolated by FACS by means of the FACSJazz (BD Biosciences, Franklin Lakes, NJ, USA) cell sorter equipped with a blue laser (488 nm, 80 mW). In detail, a fresh and unstained sporangia suspension was 2.5-fold diluted with PBS buffer and subjected for sorting. The instrument was triggered on forward scatter (FSC-H), and the sorting gate was set first on forward scatter (FSC-H) versus side scatter (SSC-H) of 0.5- to 7-μm diameter uniform microspheres (Fluoresbrite YG Microspheres 0.50 μm [Polysciences, Hirschberg an der Bergstrasse, Germany, Cat. No. 17152-10], Sphero Rainbow Calibration Particles 3.0 to 3.4 μm [BD Biosciences, Cat. No. 559123], CountBright Absolute Counting Beads 7 μm [Thermo Fisher Scientific Life Technologies; Eugene, OR, USA, Cat. No. C36950]) and further on side scatter (SSC-H) versus red fluorescence (FL3-H). The drop delay was calculated with AccuDrop beads (BD Biosciences) using BD FACS Sortware v1.2. The fixed sheath pressure used was 27 lb/in². The sample differential was set at 0.5 lb/in², and kept constant for the duration of the experiment. The drop drive frequency was 39.20 kHz.

Once sorting parameters were established, the subpopulations (SSC-H versus FL3-H) detected inside the sporangia suspensions were sorted using either the sort mode “1.0 drop Pure” into sterile 15-mL centrifuge tubes, subsequently observing an aliquot in a bright field under a light microscope (AX10 Axio Lab A1, Zeiss, Carl Zeiss Microscopy, NY, USA) at ×100 magnification with immersion oil to assess the identity of the sorted structures or the sort mode “1.0 drop Single” in case of sorting on grapevine leaf discs for infection efficiency experiments, as reported below.

Infection efficiency assay.

To assess the infection efficiency of the P. viticola asexual inoculum, a single-sporangia infection assay, where single sporangia were isolated and sorted through FACS on healthy grapevine leaf discs placed inside a 24-well microtiter plate, was designed. The experimental scheme of the assay is reported in Fig. S1. In detail, four sporangia suspensions (biological replicates) were derived from four different sets of 100 leaves inoculated as previously described. For each biological replicates, 96 grapevine leaf discs (technical replicates) were prepared. To this purpose, four 24-well microtiter plates (Falcon multiwell 24, Biosigma, Cona, Italy) were filled in with 1 mL of water-agar (0.5% wt/vol Agar Bacto BD Difco; Becton Dickinson Italia, Milan, Italy) per well. Grapevine (Vitis vinifera L. cv Pinot noir, kingdom Plantae, phylum Spermatophyta, subphylum Angiospermae, Class Dicotyledonae, order Rhamnales, family Vitaceae) leaf discs (15 mm diameter) were excised with a cork borer and placed, with their abaxial surface upwards, into separate wells of a multiwell plate 24 h before the inoculation. Immediately before the inoculation, the leaf discs were sprayed with sterile distilled water to reach 100% relative humidity inside the plate. Each leaf disc was inoculated by FACS-sorting with an individual droplet (about 6 nL volume) containing a single sporangium. An additional plate (not inoculated) was prepared for each replicate as a negative control. The infectivity (virulence) of the isolate was determined by spray-inoculating 1 mL of the overall the sporangia suspension on three healthy grapevine leaves (cv. Pinot noir) placed with their abaxial surface up, in a Petri dish with moistened filter paper underneath, and calculating disease severity at 9 dpi (45). Inoculated plates were incubated for 14 days in a climate chamber (22°C, 10 μmol/m² s, 12/12 h light/darkness period, 70% relative humidity). Leaf discs were scored daily under a stereomicroscope (Leica Wild M10; Leica Microsystems, Wetzlar, Germany) at ×40 magnification from 4 to 14 DAI to check for the presence of sporulation, as confirmation of successful outcome of the infection process. In this context, infection efficiency (IE) was defined as the percentage of infected leaf discs over the total inoculated leaf discs.

Sporangia infection dynamics.

Because the single-sporangia assay reproduces the ID imputable to P. viticola secondary inoculum along the experimental time frame of investigation of 14 d (i.e., time function) in laboratory conditions, such phenomenon can be investigated by fitting a GLMM defined as follows:

$$\text{g}\left({\text{y}}_{\text{tij}}\right)={\eta }_{\text{tij}}={\beta }_0+{\beta }_{\text{ti}}\text{t\hspace{0.17em}}+{\beta }_{\text{j}}+{\epsilon }_{\text{ij}}$$

where y_tij is the number of infected leaf discs out of the total number n_i of inoculated leaf discs (i.e., the proportion of infecting sporangia) at time t for the i-th plate with i = {plate1, plate2, plate3 … plate16} and at the j-th experiment (biological replicate) with j = {Experiment1, Experiment2, Experiment3, Experiment4}; η_tij is the linear predictor expressed according to the Probit link function g(•) = Φ(•); β₀ = b₀ + u_0i is the intercept, where b₀ is the intercept’s fixed term and u_0i is the intercept’s random component, which represents the plate-dependent effect arising from the random inclusion of any i-th plate in the j-th experiment; β_ti = b_t + τ_i is the slope for the time effect on the i-th plate’s ID, where b_t is the fixed slope’s component representing the sporangia infection general trend in time and τ_i is the random plate-dependent slope’s component accounting for the plate’s effect on ID; t is the time expressed as DAI, which is the same for any i-th plate and j-th experiment, as all of the plates were inoculated and incubated on the same day; β_j is the fixed experiment’s effect and ε_ij is the error term. The variance heterogeneity is managed by setting n_i as weights during the GLMM fitting procedure. This GLMM parameterization allows one to compute both a general t₅₀ = –b₀/b_t (named here as the GLMM t₅₀) and a plate-specific t₅₀ⁱ = –β₀/β_t at one time, according to the equation suggested by Faraway and coworkers (46). Such index is useful to summarize the spore germination dynamics, such as in the case of P. viticola oospores (5). Thus, assuming that all of the sporangia used in this trial were collected from the same population, and the GLMM described above is really representative of the ID, i.e., the GLMM’s fitted data values approach the observed ones according to the pseudo-R² computed via observed versus simulated simple linear regression after (47), the single-sporangia assay proposed here can be considered reliable if: (i) the experiment fixed effect β_j is not significantly different from 0 for α = 0.05, and the simulated data do not fall out of the 95% Probit confidence limits (48) computed for the overall observed infecting sporangia at the time t; and (ii) the t₅₀ⁱ expected value is not significantly different from general t₅₀ for α = 0.05. The former condition is readily assessed by computing the Wald’s χ² test (type II solution) for the GLMM parameters (49) and by comparing simulated data to 95% Probit confidence limits cited above; the latter condition is assessed by comparing the general t₅₀ value (i.e., the GLMM t₅₀) to the bootstrap t₅₀ and its 95% confidence limits. The bootstrap t₅₀ is computed by resampling 10⁴ times by bootstrap method (50) from the set of t₅₀ⁱ and then obtaining its expected value as a mean of the values arising from 10⁴ simulations. The bootstrap t₅₀ 95% is directly computed by extracting the 2.5th and the 97.5th quantile from the bootstrap distribution.

GLMM model, as described above, was fitted by glmer() function implemented in lme4 R 3.4.3. statistical packages, whereas its type II solution was computed by Anova() function implemented in car R 3.4.3. statistical packages. The 95% Probit confidence limits (48) computed for the overall observed ID were computed by binom.probit() function implemented in binom R 3.4.3. statistical packages, while the bootstrap simulations and bootstrap 95% confidence limits were obtained by bootstrap() function implemented in bootstrap R 3.4.3. statistical packages and quantile() function implemented in stats R 3.4.3. statistical packages.

Data availability.

The sequence used for confirming the species identity of our P. viticola isolate is available on GenBank (accession number OP326699). Information on the geographic cluster of origin and microsatellite profile of the strain is available on Dryad (10.5061/dryad.kh189328s).