Measuring Quantitative Virulence in the Wheat Pathogen Zymoseptoria tritici Using High-Throughput Automated Image Analysis

Zymoseptoria tritici, causal agent of Septoria tritici blotch on wheat, produces pycnidia in chlorotic and necrotic lesions on infected leaves. A high-throughput phenotyping method was developed based on automated digital image analysis that accurately measures the percentage of leaf area covered by lesions (PLACL) as well as pycnidia size and number. A seedling inoculation assay was conducted using 361 Z. tritici isolates originating from a controlled cross and two different winter wheat cultivars. Pycnidia size and density were found to be quantitative traits that showed a continuous distribution in the progeny. There was a weak correlation between pycnidia density and size (r = −0.27) and between pycnidia density and PLACL (r = 0.37). There were significant differences in PLACL and pycnidia density on resistant and susceptible cultivars. In all, >20% of the offspring exhibited significantly different pycnidia density on the two cultivars, consistent with host specialization. Automated image analysis provided greater accuracy and precision compared with traditional visual estimates of virulence. These results show that digital image analysis provides a powerful tool for measuring differences in quantitative virulence among strains of Z. tritici.

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An Improved Method for Measuring Quantitative Resistance to the Wheat Pathogen Zymoseptoria tritici Using High-Throughput Automated Image Analysis

Phytopathology ◽

10.1094/phyto-01-16-0018-r ◽

2016 ◽

Vol 106 (7) ◽

pp. 782-788 ◽

Cited By ~ 38

Author(s):

Ethan L. Stewart ◽

Christina H. Hagerty ◽

Alexey Mikaberidze ◽

Christopher C. Mundt ◽

Ziming Zhong ◽

...

Keyword(s):

Image Analysis ◽

Quantitative Resistance ◽

Recombinant Inbred Lines ◽

Continuous Distribution ◽

Field Resistance ◽

Automated Image Analysis ◽

Septoria Tritici Blotch ◽

Septoria Tritici ◽

Improved Method ◽

Zymoseptoria Tritici

Zymoseptoria tritici causes Septoria tritici blotch (STB) on wheat. An improved method of quantifying STB symptoms was developed based on automated analysis of diseased leaf images made using a flatbed scanner. Naturally infected leaves (n = 949) sampled from fungicide-treated field plots comprising 39 wheat cultivars grown in Switzerland and 9 recombinant inbred lines (RIL) grown in Oregon were included in these analyses. Measures of quantitative resistance were percent leaf area covered by lesions, pycnidia size and gray value, and pycnidia density per leaf and lesion. These measures were obtained automatically with a batch-processing macro utilizing the image-processing software ImageJ. All phenotypes in both locations showed a continuous distribution, as expected for a quantitative trait. The trait distributions at both sites were largely overlapping even though the field and host environments were quite different. Cultivars and RILs could be assigned to two or more statistically different groups for each measured phenotype. Traditional visual assessments of field resistance were highly correlated with quantitative resistance measures based on image analysis for the Oregon RILs. These results show that automated image analysis provides a promising tool for assessing quantitative resistance to Z. tritici under field conditions.

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Ranking Quantitative Resistance to Septoria tritici Blotch in Elite Wheat Cultivars Using Automated Image Analysis

Phytopathology ◽

10.1094/phyto-04-17-0163-r ◽

2018 ◽

Vol 108 (5) ◽

pp. 568-581 ◽

Cited By ~ 32

Author(s):

Petteri Karisto ◽

Andreas Hund ◽

Kang Yu ◽

Jonas Anderegg ◽

Achim Walter ◽

...

Keyword(s):

Image Analysis ◽

Quantitative Resistance ◽

Major Gene ◽

Growing Season ◽

Automated Image Analysis ◽

Septoria Tritici Blotch ◽

Septoria Tritici ◽

Wheat Cultivars ◽

Zymoseptoria Tritici ◽

Breeding Programs

Quantitative resistance is likely to be more durable than major gene resistance for controlling Septoria tritici blotch (STB) on wheat. Earlier studies hypothesized that resistance affecting the degree of host damage, as measured by the percentage of leaf area covered by STB lesions, is distinct from resistance that affects pathogen reproduction, as measured by the density of pycnidia produced within lesions. We tested this hypothesis using a collection of 335 elite European winter wheat cultivars that was naturally infected by a diverse population of Zymoseptoria tritici in a replicated field experiment. We used automated image analysis of 21,420 scanned wheat leaves to obtain quantitative measures of conditional STB intensity that were precise, objective, and reproducible. These measures allowed us to explicitly separate resistance affecting host damage from resistance affecting pathogen reproduction, enabling us to confirm that these resistance traits are largely independent. The cultivar rankings based on host damage were different from the rankings based on pathogen reproduction, indicating that the two forms of resistance should be considered separately in breeding programs aiming to increase STB resistance. We hypothesize that these different forms of resistance are under separate genetic control, enabling them to be recombined to form new cultivars that are highly resistant to STB. We found a significant correlation between rankings based on automated image analysis and rankings based on traditional visual scoring, suggesting that image analysis can complement conventional measurements of STB resistance, based largely on host damage, while enabling a much more precise measure of pathogen reproduction. We showed that measures of pathogen reproduction early in the growing season were the best predictors of host damage late in the growing season, illustrating the importance of breeding for resistance that reduces pathogen reproduction in order to minimize yield losses caused by STB. These data can already be used by breeding programs to choose wheat cultivars that are broadly resistant to naturally diverse Z. tritici populations according to the different classes of resistance.

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Ranking quantitative resistance to Septoria tritici blotch in elite wheat cultivars using automated image analysis

10.1101/129353 ◽

2017 ◽

Cited By ~ 2

Author(s):

Petteri Karisto ◽

Andreas Hund ◽

Kang Yu ◽

Jonas Anderegg ◽

Achim Walter ◽

...

Keyword(s):

Image Analysis ◽

Quantitative Resistance ◽

Major Gene ◽

Growing Season ◽

Automated Image Analysis ◽

Septoria Tritici Blotch ◽

Septoria Tritici ◽

Wheat Cultivars ◽

Zymoseptoria Tritici ◽

Breeding Programs

AbstractQuantitative resistance is likely to be more durable than major gene resistance for controlling Septoria tritici blotch (STB) on wheat. Earlier studies hypothesized that resistance affecting the degree of host damage, as measured by the percentage of leaf area covered by STB lesions, is distinct from resistance that affects pathogen reproduction, as measured by the density of pycnidia produced within lesions. We tested this hypothesis using a collection of 335 elite European winter wheat cultivars that was naturally infected by a diverse population of Zymoseptoria tritici in a replicated field experiment. We used automated image analysis (AIA) of 21420 scanned wheat leaves to obtain quantitative measures of conditional STB intensity that were precise, objective, and reproducible. These measures allowed us to explicitly separate resistance affecting host damage from resistance affecting pathogen reproduction, enabling us to confirm that these resistance traits are largely independent. The cultivar rankings based on host damage were different from the rankings based on pathogen reproduction, indicating that the two forms of resistance should be considered separately in breeding programs aiming to increase STB resistance. We hypothesize that these different forms of resistance are under separate genetic control, enabling them to be recombined to form new cultivars that are highly resistant to STB. We found a significant correlation between rankings based on automated image analysis and rankings based on traditional visual scoring, suggesting that image analysis can complement conventional measurements of STB resistance, based largely on host damage, while enabling a much more precise measure of pathogen reproduction. We showed that measures of pathogen reproduction early in the growing season were the best predictors of host damage late in the growing season, illustrating the importance of breeding for resistance that reduces pathogen reproduction in order to minimize yield losses caused by STB. These data can already be used by breeding programs to choose wheat cultivars that are broadly resistant to naturally diverse Z. tritici populations according to the different classes of resistance.

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Precision phenotyping reveals novel loci for quantitative resistance to septoria tritici blotch in European winter wheat

10.1101/502260 ◽

2018 ◽

Cited By ~ 1

Author(s):

Steven Yates ◽

Alexey Mikaberidze ◽

Simon Krattinger ◽

Michael Abrouk ◽

Andreas Hund ◽

...

Keyword(s):

Disease Resistance ◽

Winter Wheat ◽

High Throughput ◽

Quantitative Resistance ◽

Small Chromosome ◽

Snp Markers ◽

Automated Image Analysis ◽

Septoria Tritici Blotch ◽

Septoria Tritici ◽

Limiting Factor

Accurate, high-throughput phenotyping for quantitative traits is the limiting factor for progress in plant breeding. We developed automated image analysis to measure quantitative resistance to septoria tritici blotch (STB), a globally important wheat disease, enabling identification of small chromosome intervals containing plausible candidate genes for STB resistance. 335 winter wheat cultivars were included in a replicated field experiment that experienced natural epidemic development by a highly diverse but fungicide-resistant pathogen population. More than 5.4 million automatically generated phenotypes were associated with 13,648 SNP markers to perform a GWAS. We identified 26 chromosome intervals explaining 1.9-10.6% of the variance associated with four resistance traits. Seventeen of the intervals were less than 5 Mbp in size and encoded only 173 genes, including many genes associated with disease resistance. Five intervals contained four or fewer genes, providing high priority targets for functional validation. Ten chromosome intervals were not previously associated with STB resistance. Our experiment illustrates how high-throughput automated phenotyping can accelerate breeding for quantitative disease resistance. The SNP markers associated with these chromosome intervals can be used to recombine different forms of quantitative STB resistance that are likely to be more durable than pyramids of major resistance genes.

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Pixelwise H-score: A novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images

PLoS ONE ◽

10.1371/journal.pone.0245638 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0245638

Author(s):

Sripad Ram ◽

Pamela Vizcarra ◽

Pamela Whalen ◽

Shibing Deng ◽

C. L. Painter ◽

...

Keyword(s):

Image Analysis ◽

Digital Image ◽

Large Scale ◽

Digital Image Analysis ◽

Tumor Resection ◽

Automated Image Analysis ◽

Current Standard ◽

Tissue Sections ◽

Slide Image ◽

Standard Of Practice

Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

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High-Throughput Method for Automated Colony and Cell Counting by Digital Image Analysis Based on Edge Detection

PLoS ONE ◽

10.1371/journal.pone.0148469 ◽

2016 ◽

Vol 11 (2) ◽

pp. e0148469 ◽

Cited By ~ 45

Author(s):

Priya Choudhry

Keyword(s):

Image Analysis ◽

Edge Detection ◽

High Throughput ◽

Digital Image ◽

Digital Image Analysis ◽

Cell Counting ◽

High Throughput Method

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High-Throughput Phenotyping of Seed/Seedling Evaluation Using Digital Image Analysis

Agronomy ◽

10.3390/agronomy8050063 ◽

2018 ◽

Vol 8 (5) ◽

pp. 63 ◽

Cited By ~ 9

Author(s):

Chongyuan Zhang ◽

Yongsheng Si ◽

Jacob Lamkey ◽

Rick Boydston ◽

Kimberly Garland-Campbell ◽

...

Keyword(s):

Image Analysis ◽

High Throughput ◽

Digital Image ◽

Digital Image Analysis ◽

High Throughput Phenotyping

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RADIX: rhizoslide platform allowing high throughput digital image analysis of root system expansion

Plant Methods ◽

10.1186/s13007-016-0140-8 ◽

2016 ◽

Vol 12 (1) ◽

Cited By ~ 9

Author(s):

Chantal Le Marié ◽

Norbert Kirchgessner ◽

Patrick Flütsch ◽

Johannes Pfeifer ◽

Achim Walter ◽

...

Keyword(s):

Image Analysis ◽

Root System ◽

High Throughput ◽

Digital Image ◽

Digital Image Analysis ◽

System Expansion

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Precision Phenotyping Reveals Novel Loci for Quantitative Resistance to Septoria Tritici Blotch

Plant Phenomics ◽

10.34133/2019/3285904 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Steven Yates ◽

Alexey Mikaberidze ◽

Simon G. Krattinger ◽

Michael Abrouk ◽

Andreas Hund ◽

...

Keyword(s):

Disease Resistance ◽

High Throughput ◽

Quantitative Resistance ◽

Small Chromosome ◽

Snp Markers ◽

Automated Image Analysis ◽

Septoria Tritici Blotch ◽

Septoria Tritici ◽

Limiting Factor ◽

Single Experiment

Accurate, high-throughput phenotyping for quantitative traits is a limiting factor for progress in plant breeding. We developed an automated image analysis to measure quantitative resistance to septoria tritici blotch (STB), a globally important wheat disease, enabling identification of small chromosome intervals containing plausible candidate genes for STB resistance. 335 winter wheat cultivars were included in a replicated field experiment that experienced natural epidemic development by a highly diverse but fungicide-resistant pathogen population. More than 5.4 million automatically generated phenotypes were associated with 13,648 SNP markers to perform the GWAS. We identified 26 chromosome intervals explaining 1.9-10.6% of the variance associated with four independent resistance traits. Sixteen of the intervals overlapped with known STB resistance intervals, suggesting that our phenotyping approach can identify simultaneously (i.e., in a single experiment) many previously defined STB resistance intervals. Seventeen of the intervals were less than 5 Mbp in size and encoded only 173 genes, including many genes associated with disease resistance. Five intervals contained four or fewer genes, providing high priority targets for functional validation. Ten chromosome intervals were not previously associated with STB resistance, perhaps representing resistance to pathogen strains that had not been tested in earlier experiments. The SNP markers associated with these chromosome intervals can be used to recombine different forms of quantitative STB resistance that are likely to be more durable than pyramids of major resistance genes. Our experiment illustrates how high-throughput automated phenotyping can accelerate breeding for quantitative disease resistance.

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