Image analysis methods for assessment of H2O2 production andPlasmopara viticola development in grapevine leaves: Application to theevaluation of resistance to downy mildew☆

Ian Li Kim Khiook a, Charles Schneider a, Marie-Claire Heloir a, Benjamin Bois b, Xavier Daire a,Marielle Adrian a, Sophie Trouvelot a,⁎a Université de Bourgogne, UMR AgroSup/INRA/uB 1347 Agroécologie, ERL CNRS 6300, BP 86510, F-21000 Dijon, Franceb Institut Universitaire de la Vigne et du Vin — CRC, UMR 6282 Biogéosciences CNRS/Université de Bourgogne-6, Boulevard Gabriel, 21000 Dijon, France

a b s t r a c ta r t i c l e i n f o

Article history:Received 23 May 2013Received in revised form 1 August 2013Accepted 11 August 2013Available online 28 August 2013

Keywords:Image analysisPlasmopara viticolaDowny mildewGrapevineH2O2

Resistance tests

The grapevine downy mildew (Plasmopara viticola) provokes severe damages and destroys the harvest in theabsence of an effective protection. Numerous fungicide treatments are thus generally necessary. To promote asustainable production, alternative strategies of protection including new antifungal molecules, resistant geno-types or elicitor-induced resistance are under trial. To evaluate the relevance of these strategies, resistancetests are required. In this context, three image analysis methods were developed to read the results of testsperformed to assess P. viticola sporulation and mycelial development, and H2O2 production in leaves. Theyhave been validated using elicitors of plant defenses. These methods are reliable, innovative, rapid, and theirmodular concept allows their further adaptation to other host–pathogen systems.

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

Plasmopara viticola [(Berk. & Curt.) Berl. & de Toni] is the oomyceteresponsible for grapevine downy mildew, a disease that affects herba-ceous organs. Leaves are infected by asexual zoospores that swim inwater droplets on the lower surface to reach stomatawhere they encystand form a germ-tube that penetrates into the stomatal cavity. Anintercellular mycelial network then develops into the leaf mesophylland forms haustoria for nutrient uptake. The oomycete next usesother stomata to emit sporangiophores for sporulation. The symptomsare expressed as the typical “oil spots” on the leaf upper face and thecorresponding downy white sporulation on the lower one.

Most of the grown grapevines (Vitis vinifera cultivars) are suscepti-ble to downy mildew and numerous treatments are generally requiredto guarantee the quantity and quality of the harvest. Nowadays, with

the aim of developing a sustainable viticulture, there is an increasing so-cietal demand, political incitation and winegrower awareness for newstrategies of protection allowing a lower use of chemical fungicides.Among them is the development of a strategy based on the activationof defense reactions by compounds named elicitors. Their perceptionby the plant triggers a cascade of signaling events (including H2O2

production) that induces defense gene expression, leading to defensereactions that prevent or limit the pathogen development (Adrianet al., 2012). Induced resistance (IR) strategy is effective in greenhouseconditions but remains inconsistent in the vineyard conditions. Numer-ous bioassays are still required to identify the most effective molecules,understand their mode of action or optimize their formulation. Rapid,quantitative, and accurate methods adapted to read the results ofthese bioassays are therefore essential.

Evaluation of downymildew infection is performed on leaves or leafdisks. A traditional method consists in a visual scoring of sporulationbased on a categorization into resistance classes such as those of theOIV452 descriptor (Anonymous, 2009). It is fast, easy, and allows kineticstudies of disease development. However, it is semi-quantitative anduser-dependent. A second method is based on the determination ofthe number of sporangia present on leaf disks. Sporangia are collectedand counted using a cell counter (Bellin et al., 2009). This methodis quantitative, but sample destructive, time consuming, and is notsuitable for infection kinetic studies. Recently, image analysis has beendeveloped in plant pathology to measure and observe the pathogen

Journal of Microbiological Methods 95 (2013) 235–244

☆ This is an open-access article distributed under the terms of the Creative CommonsAttribution-NonCommercial-No Derivative Works License, which permits non-commercialuse, distribution, and reproduction in any medium, provided the original author and sourceare credited.⁎ Corresponding author at: Université de Bourgogne, UMR1347 Agroécologie, 17 rue

Sully, 21000 Dijon, France. Tel.: +33 3 80 69 31 04.E-mail addresses: Ian.Li-Kim-Khiook@u-bourgogne.fr (I.L. Kim Khiook),

charles.schneider@dijon.inra.fr (C. Schneider), marie-claire.heloir@u-bourgogne.fr(M.-C. Heloir), xavier.daire@dijon.inra.fr (X. Daire), marielle.adrian@u-bourgogne.fr(M. Adrian), sophie.trouvelot@u-bourgogne.fr (S. Trouvelot).

0167-7012/$ – see front matter © 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.mimet.2013.08.012

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Fig. 1. Flow chart of the processing system developed for the assessment of P. viticola sporulation, starting from image acquisition (image data) and showing the image processing chain.The different steps of the process are described in the Results section.

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development (Bock et al., 2010). It was adapted to assess P. viticolasporulation for the screening of new resistant genotypes (Peressottiet al., 2011). In this paper, an improved method suitable for a widerspectrum of experiments is presented.

In some conditions, no sporulation occurs whereas the internalmycelium has developed into the leaf mesophyll. In that case,sporulation-based methods underestimate the infection level and arenot reliable. An image analysis method of assessment of the mycelialdevelopment was therefore developed. As P. viticolamycelium is inter-nal, it was necessary to stain it with aniline blue to allow visualization, atraditional method used to observe the oomycete structures (Diez-Navajas et al., 2007; Kortekamp, 2005).

Once a molecule has been observed as effective in reducing infec-tion, it is necessary to determine its mode of action: antimicrobial activ-ity or activation of plant defense. Among plant defense events, theaccumulation of H2O2 in P. viticola inoculated leaves (revealed bydiaminobenzidine (DAB) staining) is associated with resistance bylimiting the mycelial development (Trouvelot et al., 2008). So it madesense to develop an image analysis method to assess H2O2 productionin response to P. viticola infection.

This study therefore describes reproducible and accurate imaginganalysismethods suitable for 1— quantification of P. viticola sporulation,2— quantification of P. viticola mycelium development in leaves, and3 — measurement of H2O2 accumulation in leaves. Experimentsusing elicitors of plant defenses and a tolerant genotype wereperformed to validate these methods.

2. Material and methods

2.1. Plant material

The grapevine cultivar V. vinifera L. cv. Marselan (Cabernetsauvignon x Grenache) and hybrid Solaris [FR 240-75: Merzling ! Gm6493 (V. vinifera cv. Saperavi Severneyi ! V. vinifera cv. MuscatOttonel)], respectively susceptible and tolerant to P. viticola, were usedfor this study. Plants were obtained from herbaceous cuttings placedin individual pots (10 ! 10 ! 7 cm) containing a mixture of blondpeat and perlite (3:2, v/v). They were grown in a glasshouse at atemperature of 24 and 18 °C (day and night, respectively) with a photo-period of 16 h of light and at a relative humidity (RH) of 70 ± 10% untilthey developed six leaves. Plants were sub-irrigated with a fertilizersolution (NPK 10-10-10, Plantin, France).

2.2. Elicitor treatment

Sulfated laminarin (PS3, 5 g l!1; Goëmar) and ß-aminobutyric acid(BABA, 2 g l!1; Sigma-Aldrich Chemicals) were used to induce resis-tance against P. viticola. PS3 was dissolved in an adjuvant (final concen-tration of 0.05%; v/v) in order to improve its penetration through thefoliar cuticle. Both the upper and lower sides of the plant leaves weresprayed by elicitor solutions until the point of run-off using a manualsprayer. As controls, the plants were sprayed with the adjuvant aloneor water (reverse osmosis water).

Depending on the experiments, either the second and third adultleaves under the apex [i.e. P2 and P3 as described by Steimetz et al.(2012)] were detached, or disks (1 cm in diameter) were punched outfrom these leaves at 48 h post elicitor/control treatment (hpt), andtreated as described below.

2.3. P. viticola inoculation

The isolate used for this study was obtained from a Burgundyvineyard. It was maintained on Marselan herbaceous cuttings in theglasshouse under the conditions described above. For sporulation,plants presenting oil spot symptoms were placed in the dark at 100%RH overnight. Sporangia were then collected from the lower leaf side

using a brush and suspended in reverse osmosis water. The concentra-tionwas adjusted to 104 sporangia permilliliter using a hemacytometer.

Forty-eight hours post treatment, leaf disks were deposited on amoist Whatman paper in a plastic box, the lower side uppermost.They were next mock-inoculated with a droplet of water (as control)or inoculated with 20 μl of the sporangia suspension. They were thenincubated at a 16 h–8 h (light–dark) photoperiod at a temperature of19–21 °C, respectively. In these conditions, sporulation occurred at6 days post inoculation (dpi).

2.4. Assessment/quantification of P. viticola sporulation

Three methods were used to assess or quantify sporulation at 6 dpi.For each of them, three independent biological repeats were conducted.

2.4.1. Visual scoringLeaf disks were collected (38 disks/condition) and the sporulation

was estimated by visual scoring using a notation scale previouslyestablished and regularly used in the lab (Trouvelot et al., 2008). The no-tation scale from 0 to 5 is as follows: 0 = no visible sporulation, 1 = 1to 10%, 2 = 10 to 25%, 3 = 25 to 50%, 4 = 50 to 75%, and 5 = 75 to100% of the disk area covered.

2.4.2. Determination of the number of sporangiaTen leaf disks per condition were collected, dipped in 50% ethanol

(1 ml/disk) and slowly agitated to detach sporangia. The concentrationof sporangia was then determined using a hemacytometer. Four techni-cal repeats were performed per condition.

2.4.3. Image analysisLeaf disks were removed (38 disks/condition) and placed on a

rectangle (10 ! 7 cm) of white Whatman paper having a scale (tile0.5 ! 0.5 cm). One rectangle is needed per experimental condition. Apicture (3008 ! 2000 pixels) was then taken under an artificial light(Osram Dulux L 55w/865) using a Nikon D50 (AF-S DX 18–55 mm)mounted on a manual photographic reproduction bench at a height of12 in. To quantify the infected area of the disks, the acquired image(one per condition) was analyzed i — using the macro we have devel-oped (thereafter called “Method 1”) for the image analysis Visilog 6.9software (Noesis, France), and ii — using the previously publishedmethod published by Peressotti et al. (2011) based on the use of ImageJsoftware (thereafter called “Method 2”).

2.5. Aniline blue staining and imaging of P. viticola mycelial development

P. viticola development in leaf tissues was observed by histochemis-try after aniline blue staining of the walls of the oomycete. Six disks percondition were collected at 6 dpi and treated as previously described(Trouvelot et al., 2008). After staining, the disks were mounted onmicroscope slides in aniline blue (abaxial side up) and observed byepifluorescence microscopy (Leica Leitz DMRB, λexc 340 nm, λem

380 nm, stop filter LP 430 nm, magnification !100). The myceliumfluoresces blue under these conditions. For each disk, 4 representa-tive photographs were taken using a camera (Nikon DIGITALSIGHT DS SMc) with the following parameters: initial resolution of2560 ! 1920 pixels and color depth of 24 bits.

Three independent biological repeats were conducted.

2.6. DAB staining and imaging of H2O2 production

Forty-eight hours post-treatment, leaves were detached and eachpetiole was dipped in reverse osmosis water. Inoculationwas performedby application of 20 μl droplets of the sporangia suspension. Three dayspost inoculation, leaveswere transferred inDAB-HCl solution and treatedas previously described by Trouvelot et al. (2008). The samples werethen mounted on microscope slides in 50% glycerol (aqueous solution)

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Fig. 2. Flow chart of the processing system developed for the assessment of P. viticola mycelial colonization in leaf disks, starting from image acquisition (image data) and showing theimage processing chain. The scale bar corresponds to 50 μm. The different steps of the process are described in the Results section.

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with the abaxial surface uppermost. H2O2 was visualized as a reddish-brown stain. Fifteen representative photographs were taken per condi-tion using a camera (Nikon DIGITAL SIGHT DS SMc). The program isadapted to initial resolution ranging from 640 ! 480 to 2560 ! 1920pixels, and with a color depth of 24 bits.

Three independent biological repeats were conducted.

2.7. Statistical analysis

For each method, treatments were compared by means of non-parametric approaches, at the 5% significance level. Both Kruskal–Wallisand Friedman's tests were used. Treatments were grouped according tothe least significant difference of the sum of the ranks (Friedman's testpost-hoc multiple comparison). For more details, the reader can referto numerous reviews (Bewick et al., 2004).

3. Results

3.1. Development of an image analysis method suitable for leaf downymildew sporulation assessment

The method was developed for images (JPEG files) acquiredaccording to the specifications described in the Material and Methodssection and treated using Visilog as follows (Fig. 1). From each image(one per experimental condition), a step of recognition and determina-tion of the number of leaf disks is achieved by an analysis of the colorcovariance (Fig. 1, step 1). A color is randomly and automatically attrib-uted to each disk detected and two different colors are systematicallyattributed to two distinct neighboring disks. The software next calcu-lates the number of all the recognized items. Another step of user-independent automatic thresholding (Fig. 1, step 2) is then performed1 — to create three monochrome 8 bit images from the red (R), green(G), and blue (B) components of the image, 2— to analyze the distribu-tion of the intensity of these components, and 3 — to perform a logical“AND” between the areas obtained for R, G and B images (only theareas common to the threemonochrome images R, G and B are selected)to provide a binary image called “Image A” showing sporulation, whichcontains the sporulation structures labeled with ones and the back-ground with zeros. This image allows the identification and the quanti-fication of sporulation (S) (Fig. 1, step 3).

Depending on the leaf shape and inoculation conditions, the patho-gen has not exactly the same leaf area available for infection from onedisk to another one. Firstly, P. viticola cannot develop in veins. Secondly,inoculation by droplets, contrary to inoculation by spray, restricts thepathogen ingress to the droplet area which is greater in the case of apretreatment including an adjuvant (due to an increased wettability),compared towater. All these observations have been taken into accountfor the development of the method. From Image A, the veins of the leafdisks are automatically removed according to predefined size and shapecriteria (Fig. 1, step 4). If some of them persist, a tool allows the user toremove themmanually. An automatic texture thresholding is thereforeperformed to obtain a picture named “Image B” corresponding to thesporulation inclosing area (SiA). This step permits to estimate the drop-let area (i.e. mean SiA of control disks). The sporulation density (SpD) iscalculated by the S/SiA ratio (Fig. 1, step 5). Finally, the disease severity(DS) corresponding to the percentage of the inoculated surface showingsporulation is calculated from the S/mean SiAcontrol (water or adjuvant) ratio(Fig. 1, step 6). At the end of the process, an excel File presents the list ofthe disks identified and the corresponding S, SiA, SpD andDS values. Themean SpD and DS ± SE values obtained for the set of disks present onan image are also calculated. The processing time necessary for trainedusers to analyze an image showing disks on a 10 ! 7 cm rectangle(38 disks in our case) is around 2 min. The detailed code of this macroand those further described (Sections 3.2 and 3.3) are available onrequest.

3.2. Development of an image analysis method for the assessment ofP. viticola mycelium development after aniline blue staining

After P. viticola staining by aniline blue, assessment of the mycelialcolonization by image analysis was performed from images (24 bits)acquired (Fig. 2A, step 1) according to the parameters described in theMaterial and Methods section. The images are processed according totwo thresholds: blue and light intensities (Fig. 2, step 2). The image cor-responding to blue intensity consists on the 8 bit gray-scale blue part ofthe initial 24 bit image (Fig. 2B)whereas the image corresponding to thelight intensity results of the transformation of the initial image on anHLS (Hue Lightness Saturation) image, in which only the 8 bit gray-scale L part (Fig. 2C) is analyzed. The areas of interest result from a log-ical “AND” from both thresholds (Fig. 2, step 3), and a binary image isgenerated (Fig. 2D), which contains the aniline blue stained structureslabeled with ones and the background with zeros. Aniline blue stainsthe ß-glucans and reveals not only the structures of P. viticola but alsothe plant polymer callose produced by leaves as a defense reaction ininfected area. That is why the method includes a step, after a transfor-mation into 16 bits (unsigned short integer), allowing an automaticremoval of areas corresponding to callose (based on shape and sizecriteria) in order to avoid overestimating the mycelial colonization(Fig. 2E, step 4). An image and the value of the internal colonizationare therefore obtained (in pixels) (Fig. 2F, steps 5 and 6) and dividedby the image surface (microscopic field) to obtain the percentage ofthe leaf surface colonized by the pathogen (Fig. 2, step 7). The process-ing time necessary for trained users to analyze an image is 1 to 2 min.

3.3. Development of an image analysis method for the evaluation of H2O2

production in grapevine leaves

Assessment of H2O2 production (as revealed by brown precipitates)by image analysis was performed from images acquired according tothe parameters described above (see Material and Methods) as follows(Fig. 3). The images are acquired through a transmission light micro-scope (Fig. 3A, step1). As someveins could cast a shadow, a step of light-ing homogenization is required. The program asks the user to select,manually, background points (20 to 30 — represented as red crosses)which are evenly distributed except in characteristic DAB stained area(on veins for example; Fig. 3, step 2). A new image (24 bits) is edited(Fig. 3B) and processed according to two thresholds (Fig. 3, step 3):blue intensity (corresponding to the 8 bit gray-scale blue part of the ini-tial 24 bit image, Fig. 3C) and color saturation (resulting from a conver-sion of the initial 24 bit image into an 8 bit gray-scale HLS image, inwhich only the S part is analyzed, Fig. 3D). The areas of interest resultfrom a logical “AND” from both thresholds (Fig. 3, step 4) and a binaryimage is obtained (Fig. 3E), which contains the DAB reactive areaslabeled with ones and the background with zeros. Such a binaryimage is the basis for further analysis. Since DAB revealed also veinsand some idioblasts, those parts are removed by a manual step (Fig. 3,step 5). For this, the binary image previously obtained (Fig. 3E) istransformed in a 16 bit unsigned short integer image (Fig. 3F). Afterthis removal step, a 16 bit final image (Fig. 3G) corresponding to theidentification of defense-specific DAB reactive areas (Fig. 3, step 6) isgenerated. A value of the DAB stained area is therefore obtained(in pixels) and divided by the image surface (microscopic field) to de-termine the percentage of the leaf surface in which H2O2 is produced(Fig. 3, steps 7 and 8 respectively). The processing time necessary fortrained users to analyze an image is around 1 to 2 min and is a functionto the number of artifacts necessary to be removed manually.

3.4. Assessment of the user-independent robustness of these three imageanalysis methods

A series of disks (n = 14) showing different levels of sporula-tion, mycelial colonization, and H2O2 accumulation, respectively,

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Fig. 3. Flow chart of the processing system developed for the assessment of H2O2 accumulation. The different steps of the process are described in the Results section.

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was selected. Five persons used the three image analysis methods devel-oped and performed the corresponding visual scorings (SupplementaryFigs. 1, 2, 3). In all cases, the results demonstrated the user-independency of the imaging methods, contrary to scoring methods.The slight differences observed were due to the steps of manual thresh-olds (Supplementary Figs. 2, 3).

3.5. Validation of these methods

To validate the efficiency of thesemethods, leaves of the downymil-dew susceptible variety Marselan were treated by PS3 and BABA, two

elicitors known to prime defenses in response to P. viticola inoculation(Hamiduzzaman et al., 2005; Trouvelot et al., 2008). For comparison,the downy mildew tolerant genotype Solaris was also used.

At 6 dpi, sporulation was assessed by visual scoring, determinationof the number of sporangia (counted using a hemacytometer), and theimage analysis method developed here (“Method 1”, Fig. 4). Whateverthe method used, the results confirmed i — that both PS3 and BABA in-duce resistance against P. viticola and ii — the tolerance of Solaris todowny mildew. These results are a good indicator of the reliability of“Method 1”. The correlation coefficients between the results obtainedfor the three methods used above, and also with “Method 2” described

Fig. 4. Assessment of P. viticola sporulation using visual scoring (A), determination of the number of sporangia (B) and our image analysis method (C). Grapevine leaves of V. vinifera cv.Marselan were treated by BABA, PS3 or the corresponding control water (W) and the adjuvant (ADJ). Forty eight hours post treatment, leaf disks were punched out and inoculatedwith asuspension of P. viticola sporangia (104 ml!1). For comparison, leaf disks of the variety Solaris were inoculatedwithout prior treatment. At 6 dpi, the sporulationwas assessed by the threemethods. Means with different letters (a, b, c, d) are significantly different (P b 0.05). Photographs of representative disks of each experimental condition are presented at the top of thefigure.

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byPeressotti et al. (2011)were determined. As “Method 2” does not cal-culate the disease severity, the S valuewas used for comparison. A goodcorrelation was obtained for the assessment of sporulation for bothimage analysis methods (r2 = 0.9963) and slight lower ones were ob-served between thesemethods and i— the quantification of the numberof sporangia (r2 = 0.9181 and 0.9147 for “Method 1” and “Method 2”,respectively) and ii — the visual scoring (r2 = 0.9550 and 0.9550 for

“Method 1” and “Method 2”, respectively). These correlations aresufficient to validate the method.

Regarding mycelial development, both PS3 and BABA reduced thepathogen ingress in a similar manner (86 and 90% respectively, Fig. 5),as observed in Solaris leaves. These results are in accordance with previ-ous published ones (Dubreuil-Maurizi et al., 2010; Hamiduzzamanet al., 2005; Trouvelot et al., 2008).

Fig. 5.Assessment of P. viticolamycelial development using the image analysismethod. Grapevine leaves ofV. vinifera cv.Marselanwere treated by BABA, PS3 or the corresponding controlwater (W) and the adjuvant (ADJ). At 48 hpt, leaf disks were punched out and inoculated by a suspension of P. viticola sporangia (104 ml!1). For comparison, leaf disks of the varietySolaris were inoculated without prior treatment. At 6 dpi, the mycelial development in the mesophyll was visualized by fluorescence microscopy after aniline blue staining. Meanswith different letters (a, b) are significantly different (P b 0.05). Photographs of representative disk area of each experimental condition are presented at the top of the figure.

Fig. 6.Assessment ofH2O2 production using the image analysismethod. Grapevine leaves of V. vinifera cv.Marselanwere treated by BABA, PS3 or the corresponding control water (W) andthe adjuvant (ADJ) and inoculatedby a sporangia suspension (104 ml!1). At 3 dpi, theywere treated byDAB-HCl forH2O2 visualization (reddish-brown stain).Meanswith different letters(a, b, c) are significantly different (P b 0.05). Photographs of representative disk area of each experimental condition are presented at the top of the figure.

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On the basis of previous experiments (Dubreuil-Maurizi et al., 2010;Trouvelot et al., 2008), H2O2 production was assessed at 3 dpi.Compared to controls, both PS3 and BABA primed H2O2 production(Fig. 6). It appeared that BABA had a rating (1.8% of leaf area is DABstained) similar to that of the tolerant hybrid Solaris leaves (1.5%). Weobserved local H2O2 accumulation sites corresponding to the pathogeningress and predictive of future necrosis areas (data not shown). In con-trast, large amounts of H2O2 (5.4%; i.e. 3 fold more than in response toBABA) were detected in response to PS3, similar to what was observedin the resistant species Vitis rupestris (data not shown) and mainly dueto the fact that a stronger defense response is observed at the cellularlevel.

4. Discussion

We report here the development of three fast image analysismethods allowing the assessment of grapevine resistance againstdowny mildew. Ourmethod developed to assess the pathogen sporula-tion has three main advantages. i — It is rapid: at least four times fasterthan the method developed by Peressotti et al. (2011). ii— It is suitablefor inoculation by droplets (as we did) or by spray. Inoculation by drop-lets allows the deposit of a precise number of sporangia but sporulationhas to be estimated in relation to the inoculum droplet surface (as cre-ated in ourmethod). In the case of an inoculation by spray, the determi-nation of the disease severity is easier since the entire leaf disk surface isinoculated (DS = S / mean disk surface for our method) but the num-ber of sporangia deposited at the time of inoculation is approximate.iii — It is adapted to a wide spectrum of experiments, including thosecombining inoculation by droplets and treatments containing adju-vants. Treatments containing adjuvant increase the spread surface ofthe inoculum and by such, the leaf surface is likely to be infected bythe pathogen.

Since P. viticola is an internal pathogen, it is crucial to measure itsmycelial development. Molecular methods, especially quantitativereal-time PCR, were developed to quantify pathogens in their environ-ment (for a review, see Schena et al., 2013). However, for P. viticola,such methods are not available for stages preceding the occurrence ofthe leaf symptoms (Gamm et al., 2011). Image analysis methods havebeen adapted to follow grapevine downy mildew infection (Bellowet al., 2013; Cséfalvay et al., 2009). However, they do not considerP. viticola itself but its effects on plant metabolism. That is why animage analysis method based on mycelial observation and suitable forall colonization stages was developed. To our knowledge this is thefirst time that such a method is reported. It allows having relevant andquantitative information on the pathogen colonization that permits tocompare the mycelial development in different conditions (elicitor-treated or natural resistant grapevine genotypes) and could be ap-plied in the context of phenotyping assays. In addition, combiningthe results obtained in this method with those acquired on the spor-ulation permits to evaluate more precisely the pathogen develop-ment. For example, no sporulation was observed on BABA-treatedMarselan disks, nor Solaris ones though the mycelium had devel-oped. This result confirms that a simple assessment of sporulationis not sufficient in some cases to conclude about the level of resis-tance to downy mildew.

Finally, we have developed and validated an image analysis methodto assess H2O2 production. Somemethods allow theH2O2 quantificationin plant cell suspensions, based on colorimetric (Miyata et al., 2006) orchemiluminescent (Keppler et al., 1989) assays. For in planta assess-ment, fluorescent probes, such as resorufin, are available but require astep of infiltration (Snyrychova et al., 2009) which is clearly stressfulin grapevine leaf disks. The production of reactive oxygen species canbe easily detected by using DAB, a classic photometric H2O2 probeused for leaf studies (Snyrychova et al., 2009). In this method, thebrownish polymerization product resulting from the oxidation of DABin the presence of endogenous peroxidases and H2O2 is visualized by

transmission lightmicroscopy. An image analysismethodwas thereforedeveloped to rapidly measure the DAB reactive areas in grapevineleaves suitable for all the H2O2 production kinetic (i.e. from 12 hpt to3 dpi). To our knowledge, this is the first method for the assessmentof H2O2 production in planta, compatible with the subcellular localiza-tion of this production. By applying this method, it was demonstratedthat two distinct grapevine resistance inducers (BABA and PS3) mayact similarly with respect to enhanced production of H2O2 after patho-gen inoculation but differently in terms of response intensity. Thismethod could also be applied to compare the H2O2 production intensi-ties in resistant genotypes challenged with P. viticola and be effective inphenotyping assays.

5. Conclusions

The image analysis methods reported here are suitable to assess i —P. viticola infection (i.e. sporulation and internal mycelium develop-ment) of grapevine leaves and ii — a plant defense event (in situH2O2 accumulation). These reliable and user-independent methodswill be useful for tests of natural/induced grapevine resistance to P.viticola or new antimicrobial molecules. Their modular concept sug-gests their strong potential to be extended for other host–pathogensystems, either in grapevine (powdery mildew or gray mold) or inother plant species.

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.mimet.2013.08.012.

Acknowledgments

The authors thank the BIVB (Bureau Interprofessionnel des Vinsde Bourgogne) and the Conseil Régional de Bourgogne for financialsupport, Laboratoires Goëmar for providing PS3 and the adjuvant, andalso Emilie Steimetz, Christelle Guillier, and Franck Paris for their partic-ipation to user tests.

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