Image-Based Methods to Score Fungal Pathogen Symptom Progression and Severity in Excised Arabidopsis Leaves

Pavicic, Mirko; Overmyer, Kirk; Rehman, Attiq ur; Jones, Piet; Jacobson, Daniel; Himanen, Kristiina

doi:10.3390/plants10010158

Title: Image-Based Methods to Score Fungal Pathogen Symptom Progression and Severity in Excised Arabidopsis Leaves

Journal Article · Fri Jan 15 00:00:00 EST 2021 · Plants

DOI:https://doi.org/10.3390/plants10010158· OSTI ID:1761615

^[1];

^[2];

^[3];

^[4];

^[5]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Helsinki (Finland). Viikki Plant Science Center; Univ. of Helsinki (Finland). National Plant Phenotyping Infrastructure
Univ. of Helsinki (Finland). Viikki Plant Science Center
Univ. of Helsinki (Finland). Viikki Plant Science Center; Univ. of Helsinki (Finland). National Plant Phenotyping Infrastructure; Natural Resources Inst. (Luke), Piikkio (Finland)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States). Bredesen Center for Interdisciplinary Research
Univ. of Helsinki (Finland). Viikki Plant Science Center; Univ. of Helsinki (Finland). National Plant Phenotyping Infrastructure

Image-based symptom scoring of plant diseases is a powerful tool for associating disease resistance with plant genotypes. Advancements in technology have enabled new imaging and image processing strategies for statistical analysis of time-course experiments. There are several tools available for analyzing symptoms on leaves and fruits of crop plants, but only a few are available for the model plant Arabidopsis thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (Botrytis) comprise a potent model pathosystem for the identification of signaling pathways conferring immunity against this broad host-range necrotrophic fungus. Here, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves. Thus, a pixel classification strategy using color hue values from red-green-blue (RGB) images and a random forest algorithm was used to establish necrotic, chlorotic, and healthy leaf areas. Secondly, using chlorophyll fluorescence (ChlFl) imaging, the maximum quantum yield of photosystem II (Fv/Fm) was determined to define diseased areas and their proportion per total leaf area. Both RGB and ChlFl imaging strategies were employed to track disease progression over time. This has provided a robust and sensitive method for detecting sensitive or resistant genetic backgrounds. A full methodological workflow, from plant culture to data analysis, is described.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); Academy of Finland

Grant/Contract Number:: AC05-00OR22725; 283138; 256094; 25097

OSTI ID:: 1761615

Journal Information:: Plants, Vol. 10, Issue 1; ISSN 2223-7747

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

References (34)

Necrotroph Attacks on Plants: Wanton Destruction or Covert Extortion? Laluk, Kristin; Mengiste, Tesfaye The Arabidopsis Book, Vol. 8 https://doi.org/10.1199/tab.0136	journal	January 2010
Botrytis cinerea: the cause of grey mould disease Williamson, Brian; Tudzynski, Bettina; Tudzynski, Paul Molecular Plant Pathology, Vol. 8, Issue 5 https://doi.org/10.1111/j.1364-3703.2007.00417.x	journal	September 2007
High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis Rousseau, Céline; Belin, Etienne; Bove, Edouard Plant Methods, Vol. 9, Issue 1 https://doi.org/10.1186/1746-4811-9-17	journal	January 2013
Chlorophyll fluorescence imaging of plant–pathogen interactions Rolfe, Stephen Alexander; Scholes, Julie Diane Protoplasma, Vol. 247, Issue 3-4 https://doi.org/10.1007/s00709-010-0203-z	journal	September 2010
Phenomic Approaches and Tools for Phytopathologists Simko, Ivan; Jimenez-Berni, Jose A.; Sirault, Xavier R. R. Phytopathology®, Vol. 107, Issue 1 https://doi.org/10.1094/PHYTO-02-16-0082-RVW	journal	January 2017
Digital Imaging Combined with Genome-Wide Association Mapping Links Loci to Plant-Pathogen Interaction Traits Fordyce, Rachel F.; Soltis, Nicole E.; Caseys, Celine Plant Physiology, Vol. 178, Issue 3 https://doi.org/10.1104/pp.18.00851	journal	September 2018
Pathogen-Responsive MPK3 and MPK6 Reprogram the Biosynthesis of Indole Glucosinolates and Their Derivatives in Arabidopsis Immunity Xu, Juan; Meng, Jie; Meng, Xiangzong The Plant Cell, Vol. 28, Issue 5 https://doi.org/10.1105/tpc.15.00871	journal	April 2016
The Top 10 fungal pathogens in molecular plant pathology: Top 10 fungal pathogens Dean, Ralph; Van Kan, Jan A. L.; Pretorius, Zacharias A. Molecular Plant Pathology, Vol. 13, Issue 4 https://doi.org/10.1111/j.1364-3703.2011.00783.x	journal	April 2012
ImageJ2: ImageJ for the next generation of scientific image data Rueden, Curtis T.; Schindelin, Johannes; Hiner, Mark C. BMC Bioinformatics, Vol. 18, Issue 1 https://doi.org/10.1186/s12859-017-1934-z	journal	November 2017
Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification Arganda-Carreras, Ignacio; Kaynig, Verena; Rueden, Curtis Bioinformatics, Vol. 33, Issue 15 https://doi.org/10.1093/bioinformatics/btx180	journal	March 2017
Infection with virulent and avirulent P. syringae strains differentially affects photosynthesis and sink metabolism in Arabidopsis leaves Bonfig, Katharina B.; Schreiber, Ulrich; Gabler, Andrea Planta, Vol. 225, Issue 1 https://doi.org/10.1007/s00425-006-0303-3	journal	June 2006
Fiji: an open-source platform for biological-image analysis Schindelin, Johannes; Arganda-Carreras, Ignacio; Frise, Erwin Nature Methods, Vol. 9, Issue 7 https://doi.org/10.1038/nmeth.2019	journal	June 2012
Detection of plant leaf diseases using image segmentation and soft computing techniques Singh, Vijai; Misra, A. K. Information Processing in Agriculture, Vol. 4, Issue 1 https://doi.org/10.1016/j.inpa.2016.10.005	journal	March 2017
Image-based phenotyping of plant disease symptoms Mutka, Andrew M.; Bart, Rebecca S. Frontiers in Plant Science, Vol. 5 https://doi.org/10.3389/fpls.2014.00734	journal	January 2015
Frequently asked questions about chlorophyll fluorescence, the sequel Kalaji, Hazem M.; Schansker, Gert; Brestic, Marian Photosynthesis Research, Vol. 132, Issue 1 https://doi.org/10.1007/s11120-016-0318-y	journal	November 2016
Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging Bock, C. H.; Poole, G. H.; Parker, P. E. Critical Reviews in Plant Sciences, Vol. 29, Issue 2 https://doi.org/10.1080/07352681003617285	journal	March 2010
Secondary metabolites influence Arabidopsis/Botrytis interactions: variation in host production and pathogen sensitivity: Secondary metabolites influence Arabidopsis/Botrytis interactions Kliebenstein, Daniel J.; Rowe, Heather C.; Denby, Katherine J. The Plant Journal, Vol. 44, Issue 1 https://doi.org/10.1111/j.1365-313X.2005.02508.x	journal	September 2005
Early detection of Fusarium infection in wheat using hyper-spectral imaging Bauriegel, E.; Giebel, A.; Geyer, M. Computers and Electronics in Agriculture, Vol. 75, Issue 2 https://doi.org/10.1016/j.compag.2010.12.006	journal	February 2011
The Arabidopsis Botrytis Susceptible1 Interactor Defines a Subclass of RING E3 Ligases That Regulate Pathogen and Stress Responses Luo, Hongli; Laluk, Kristin; Lai, Zhibing Plant Physiology, Vol. 154, Issue 4 https://doi.org/10.1104/pp.110.163915	journal	October 2010
Chlorophyll fluorescence analysis: a guide to good practice and understanding some new applications Murchie, E. H.; Lawson, T. Journal of Experimental Botany, Vol. 64, Issue 13 https://doi.org/10.1093/jxb/ert208	journal	August 2013
Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models: Estimation of Semiparametric Generalized Linear Models Wood, Simon N. Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 73, Issue 1 https://doi.org/10.1111/j.1467-9868.2010.00749.x	journal	September 2010
Rapid, Noninvasive Screening for Perturbations of Metabolism and Plant Growth Using Chlorophyll Fluorescence Imaging Barbagallo, Romina P.; Oxborough, Kevin; Pallett, Kenneth E. Plant Physiology, Vol. 132, Issue 2 https://doi.org/10.1104/pp.102.018093	journal	June 2003
Phenomics – technologies to relieve the phenotyping bottleneck Furbank, Robert T.; Tester, Mark Trends in Plant Science, Vol. 16, Issue 12 https://doi.org/10.1016/j.tplants.2011.09.005	journal	December 2011
Visualization of dynamics of plant–pathogen interaction by novel combination of chlorophyll fluorescence imaging and statistical analysis: differential effects of virulent and avirulent strains of P. syringae and of oxylipins on A. thaliana Berger, Susanne; Benediktyová, Zuzana; Matouš, Karel Journal of Experimental Botany, Vol. 58, Issue 4 https://doi.org/10.1093/jxb/erl208	journal	November 2006
Chlorophyll Fluorescence: A Probe of Photosynthesis In Vivo Baker, Neil R. Annual Review of Plant Biology, Vol. 59, Issue 1 https://doi.org/10.1146/annurev.arplant.59.032607.092759	journal	June 2008
Multimodel Inference: Understanding AIC and BIC in Model Selection Burnham, Kenneth P.; Anderson, David R. Sociological Methods & Research, Vol. 33, Issue 2 https://doi.org/10.1177/0049124104268644	journal	November 2004
Lights, camera, action: high-throughput plant phenotyping is ready for a close-up Fahlgren, Noah; Gehan, Malia A.; Baxter, Ivan Current Opinion in Plant Biology, Vol. 24 https://doi.org/10.1016/j.pbi.2015.02.006	journal	April 2015
A permeable cuticle in Arabidopsis leads to a strong resistance to Botrytis cinerea Bessire, Michael; Chassot, Céline; Jacquat, Anne-Claude The EMBO Journal, Vol. 26, Issue 8 https://doi.org/10.1038/sj.emboj.7601658	journal	March 2007
Chlorophyll fluorescence—a practical guide Maxwell, Kate; Johnson, Giles N. Journal of Experimental Botany, Vol. 51, Issue 345 https://doi.org/10.1093/jexbot/51.345.659	journal	April 2000
Case study of combinatorial imaging: What protocol and what chlorophyll fluorescence image to use when visualizing infection of Arabidopsis thaliana by Pseudomonas syringae? Matouš, Karel; Benediktyová, Zuzana; Berger, Susanne Photosynthesis Research, Vol. 90, Issue 3 https://doi.org/10.1007/s11120-006-9120-6	journal	January 2007
Thermal and Chlorophyll-Fluorescence Imaging Distinguish Plant-Pathogen Interactions at an Early Stage Chaerle, Laury; Hagenbeek, Dik; De Bruyne, Erik Plant and Cell Physiology, Vol. 45, Issue 7 https://doi.org/10.1093/pcp/pch097	journal	July 2004
Focus issue on plant immunity: from model systems to crop species Schwessinger, Benjamin; Bart, Rebecca; Krasileva, Ksenia V. Frontiers in Plant Science, Vol. 6 https://doi.org/10.3389/fpls.2015.00195	journal	March 2015
Frequently asked questions about in vivo chlorophyll fluorescence: practical issues Kalaji, Hazem M.; Schansker, Gert; Ladle, Richard J. Photosynthesis Research, Vol. 122, Issue 2 https://doi.org/10.1007/s11120-014-0024-6	journal	August 2014
Establishment of integrated protocols for automated high throughput kinetic chlorophyll fluorescence analyses Tschiersch, Henning; Junker, Astrid; Meyer, Rhonda C. Plant Methods, Vol. 13, Issue 1 https://doi.org/10.1186/s13007-017-0204-4	journal	July 2017

Similar Records

Rosette core fungal resistance in Arabidopsis thaliana
Journal Article · Mon Sep 16 00:00:00 EDT 2019 · Planta · OSTI ID:1761615

Dai, Yanwan; Ogilvie, Huw A.; Liu, Yuan; +10 more

Ozone injury and infection of potato leaves by Botrytis cinerea
Journal Article · Mon Sep 01 00:00:00 EDT 1969 · Plant Dis.; (United States) · OSTI ID:1761615

Manning, W J; Feder, W A; Perkins, I; +1 more

Air pollution injury of potato in Michigan
Journal Article · Tue May 01 00:00:00 EDT 1973 · Am. Potato J.; (United States) · OSTI ID:1761615

Hooker, W J; Yang, T C; Potter, H S

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
Arabidopsis
high-throughput
plant phenotyping
imaging sensors
Botrytis
disease symptom
chlorophyll fluorescence

Title: Image-Based Methods to Score Fungal Pathogen Symptom Progression and Severity in Excised Arabidopsis Leaves

Citation Formats

References (34)

Similar Records

Related Subjects