Edited by: Ioannis Stringlis, Utrecht University, Netherlands
Reviewed by: Silvia Proietti, University of Tuscia, Italy; Brian H. Kvitko, University of Georgia, United States
This article was submitted to Plant Microbe Interactions, a section of the journal Frontiers in Plant Science
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Hydrogen peroxide (H2O2) functions as an important signaling molecule in plants during biotic interactions. However, the extent to which H2O2 accumulates during these interactions and its implications in the development of disease symptoms is unclear. In this work, we provide a step-by-step optimized protocol for
Accumulation of reactive oxygen species (ROS) is a common plant response to pathogens, having many and often contrasting functions depending on the plant-pathogen system under study (
Changes in ROS levels also occur during beneficial interactions. Upon contact with plant growth promoting rhizobacteria (PGPR), plant H2O2 levels often increase, and H2O2 accumulation can be primed for enhanced resistance against pathogens (
Despite oxidative stress and pathogen responses are well-studied processes involving H2O2 in various ways, it is unclear how H2O2 signaling operates in the presence of both pathogenic and beneficial bacteria. This study aims to provide an optimized protocol for
This method was suitable to analyze and compare the differential H2O2 induction effect between the experimental conditions tested. Our results show that H2O2 accumulates at different degrees depending on the leaf region or the different plant-bacteria interactions. Notably,
Wheat (
Four day-old seedlings were divided in four groups (four tip boxes) composed of 7 seedlings each, with three replicates per group: control, non-bacterized (C); RAM10-inoculated (RAM10);
Prepare several H2O2 dilutions ≤ 47 mM from stock solution at 30% (w/w), that is 9.8 M, with ultra-pure water or sterile deionized water (SDW).
Note: H2O2 Molar mass = 34.01468 g mol–1, density = 1.11 g mL–1.
Measure the absorbance of the H2O2 dilutions at 240 nm in a quartz cuvette, after adjusting zero absorbance with the water used for dilutions.
Calculate the exact H2O2 concentration of the different solutions using Lambert-Beer law, considering the molar attenuation coefficient or absorptivity (ε) for H2O2 at 240 nm equal to 42.3 M–1 cm–1 and pathlength (l) = 1 cm.
Note: Lambert-Beer law is valid up to an absorbance ≤2.
Prepare paper filter disks with an area ≤internal area of a 2 mL microtube. Place the disk inside de microtube in horizontal position.
Impregnate all the disk surfaces with adequate volume of H2O2 solution, without overloading, and add the DAB solution (1 mg/mL). Make this in triplicate for each [H2O2]. Additionally, place 3 disks with DAB only for later background subtraction.
Note: It is important to avoid overloading of paper filter disks, since the precipitated formed by H2O2 reaction with DAB may sediment in the bottom of the microtube, underestimating [H2O2] and subsequent analysis. We used filter disks with a diameter of 55 mm, 16.6 μL of H2O2 solution and 150 μL DAB per disk.
Incubate the microtubes at room temperature and in dark conditions, overnight.
Take out the disks with clean tweezers and mount the disks in a transparent plastic slide.
Digitalize the disks with a scanner and open the image with Fiji/ImageJ software.
Apply the color deconvolution plugin in order to unmix the color vectors of the digitalized disks. From the resulting panel containing DAB color only, select each disk (region of interest, ROI) using the “oval” selection tool and measure the initial average DAB pixel intensity (
where
Invert the initial average pixel intensity values by using the formula:
being
Subtract the background DAB intensity to the
where
Construct a calibration curve correlating the
Quantify average pixel intensity also in the complimentary image and represent in a graph the values of average pixel intensity with the corresponding H2O2 concentration (μmol H2O2/cm2) (
Ten seedlings from each of the four treatments (C,
After incubation, leaves were decolorized in boiling (∼78°C) 95% ethanol for 20 min and transferred into a solution containing water and 20% glycerol.
Leaf segments were placed in filter paper to remove the excess of glycerol solution, mounted in transparent plastic slides, scanned (Epson XP-235) and the images opened with Fiji/ImageJ software. Initial settings of the software were applied to measure area (mm2) and mean pixel intensity. Global scale of the image analysis was set as 46.5 pixels = 1 mm. Then, the image was submitted to the plug-in “color deconvolution” using the built-in vector HDAB in order to limit to the DAB dye image. Three different areas (regions of interest, ROIs) were selected for analysis in the DAB only image: from 0 to 4, from 4 to 8 and from 8 to 12 mm from the
The average DAB intensity was calculated according to the formula: iDAB = 255-i, being iDAB = final DAB intensity of the ROI compared to average intensity of total white of the ROI, i = the mean DAB intensity of the ROI. In order to subtract the background of the leaf tissue, the average intensity of 20 leaves pressure-infiltrated with water, incubated in water without DAB, and then destained (blank leaves) was measured and subtracted to the iDAB value calculated for each ROI, according to the formula: fDAB = iDAB- iblank, being fDAB = final DAB intensity and iblank the average intensity of the blank leaves.
Infiltration with
A linear regression model to quantify H2O2 was applied by combining the DAB staining method with image processing using Fiji/ImageJ software. This was done by relating average DAB intensity values to a given amount of H2O2. A DAB color gradient was generated by incubating filter disks in separate microtubes containing DAB + increasing H2O2 concentrations. Disks ranged from light to dark-brown stained disks, relative to low to high H2O2 concentrations (or low to high intensities), respectively (
Two calibration curves were constructed relating the average DAB intensity values obtained in the DAB only stained section with the corresponding H2O2 concentration (μmol H2O2/cm2) applied. The first curve ranged from 0 to 104 DAB intensity values and the second one from 104 to 147 values. Both curves showed a linear relationship between the two variables (
Previous authors have stressed the importance of taking into account the intensity values of the pixels in the complimentary image, since they may contain shades of DAB, leading to false positive stain separation (
In parallel, wheat leaves were incubated in the DAB solution, digitalized and subjected to the color deconvolution plugin (
Digitalized DAB-treated leaves 3 h post-
Infiltration of
Relative H2O2 concentration in the entire selected area (∑ROIs) in C, RAM10,
The analysis of each independent ROI (
In order to assess the RAM10-mediated alleviation effect, the area under curve (AUC) of H2O2 accumulation was calculated at each time post-infection in the different treatments, and the evolution of cumulative H2O2 fold induction triggered by
Evolution of cumulative H2O2 fold induction triggered by
There exist numerous functions accounted to H2O2 in response to pathogens. Despite its crucial role in plant metabolism, there is little consensus regarding the amount of H2O2 dynamics in plants challenged with pathogens and pre-treated with PGPR. This is mainly due to both biological variability and technical inaccuracies during its quantification (
Image analysis for
DAB stained leaves can be digitalized, opened in Fiji/ImageJ and subjected to the color deconvolution plugin, an algorithm developed by
In previous studies using DAB staining, leaf H2O2 content was estimated as the percentage of dark brown DAB pixels relative to the pixels composing the leaf area. In order to express H2O2 content in concentration units, these studies relied on parallel spectrophotometric assays for H2O2 quantification (
Leaves infiltrated with
The method proposed in this study was applicable to analyze and compare the differential H2O2 induction effect of
Interestingly, RAM10-treated plants showed consistently less H2O2 accumulation, where the most remarkable alleviation effect was observed 24 and 48 hpi in the most distal area (ROI3), which maintained a low, initial
In this work, we report for the first time an integrated protocol that simultaneously allows to detect DAB distribution, to quantify amount of DAB signal in different leaf regions and to relate this signal to a given concentration of H2O2. The method is non-expensive and applicable to analyze and compare the differential H2O2 induction effects of wheat plants bacterized with both pathogenic and beneficial bacteria.
This methodology allowed to show that the pathogen
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
PC and AS designed the experiments and analyzed the data in the H2O2 quantification part. PC, RT, and CC designed the experiments and analyzed the data concerning the biotic interaction part. PC performed the experiments. All researchers contributed to the research and approved the final version of the manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We thank FCT/MCTES for the scholarship PD/BD135249/2017 to PC and the financial support to cE3c (Research Unit grant number UIDB/00329/2020) and BioISI (Research Unit grant numbers UIDB/04046/2020 and UIDP/04046/2020).
The Supplementary Material for this article can be found online at:
area under curve
3,3′-diaminobenzidine
region of interest
sterile deionized water.