Infection Assay for Xanthomonas campestris pv. campestris in Arabidopsis thaliana Mimicking Natural Entry via Hydathodes

Hulten, Marieke van; Chatterjee, Sayantani; Burg, H.A. van den

doi:10.1007/978-1-4939-9458-8_16

Cited by 7 publications

(26 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, ScAnalyzer can quantify both disease symptoms and bacterial colonization in Arabidopsis leaves. Compared to our previous method to score disease severity in Arabidopsis leaves infected with Xcc (19), ScAnalyzer is automated, unbiased, and allows for a more detailed assessment of bacterial colonization.…”

Section: Discussionmentioning

confidence: 99%

“…Plant colonization by Xcc was thus far quanti ed using a method based on de ned ordinal luminescence index categories, for which samples were manually evaluated by the researcher (19,20). To develop an unbiased and a more automated manner of scoring bacterial spread in Arabidopsis leaves, we developed a streamlined method for leaf sampling and subsequent image analysis (Fig.…”

Section: Development Of An Automated Image Analysis Pipeline To Quant...mentioning

confidence: 99%

“…To promote the formation of guttation droplets at hydathodes. and thereby natural entry of Xcc, a speci c temperature/humidity cycle was used (19).…”

Section: Xcc Disease Assaysmentioning

confidence: 99%

“…oryzae and rice (16). At present, bioluminescent reporters are popular for studies on Xcc, either in combination with B. oleracea or with Arabidopsis (17)(18)(19)(20) and have proven of value in studies on other bacterial pathosystems such as X. axonopodis pv. manihotis and cassava (21), X. euvesicitoria and pepper (21), X. hortorum pv.…”

Section: Introductionmentioning

confidence: 99%

“…We successfully applied this system to detect differences in disease susceptibility between Arabidopsis accessions and mutants (20). While this method is more suitable to quantify leaf colonization by the vasculature-infecting Xcc than methods that use counting of colony forming units per leaf area, this method still uses an ordinal qualitive scale based on a luminescence index score that typi es the bacterial spread of the luminescent Xcc in the Arabidopsis leaves in time (19). The luminescence scores are similar to a disease index scores with their limitations as described.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

ScAnalyzer: an image processing tool to monitor plant disease symptoms and pathogen spread in Arabidopsis thaliana leaves

Paauw,

Hardeman,

Pfeilmeier

et al. 2024

Preprint

View full text Add to dashboard Cite

Background Plants are known to be infected by a wide range of pathogenic microbes. To study plant diseases caused by microbes, it is imperative to be able to monitor disease symptoms and microbial colonization in an quantitative and objective manner. In contrast to more traditional measures that use manual assignments of disease categories, image processing provides a more accurate and objective quantification of plant disease symptoms. Besides monitoring disease symptoms, it provides additional information on the spatial localization of pathogenic microbes in different plant tissues. Results Here we report on an image analysis tool called ScAnalyzer to monitor disease symptoms and bacterial spread in Arabidopsis thaliana leaves. Detached leaves are assembled in a grid and scanned, which enables automated separation of individual samples. A pixel color threshold is used to segment healthy (green) from diseased (yellow) leaf area. The spread of luminescence-tagged bacteria is monitored via light-sensitive films, which are processed in a similar way as the leaf scans. We show that this tool is able to capture previously identified differences in susceptibility of the model plant A. thaliana to the bacterial pathogen Xanthomonas campestris pv. campestris. Moreover, we show that the ScAnalyzer pipeline provides a more detailed assessment of bacterial spread within plant leaves than previously used methods. Finally, by combining the disease symptom values with bacterial spread values from the same leaves, we show that bacterial spread precedes visual disease symptoms. Conclusion Taken together, we present an automated script to monitor plant disease symptoms and microbial spread in A. thaliana leaves. The freely available software (https://github.com/MolPlantPathology/ScAnalyzer) has the potential to standardize the analysis of disease assays between different groups.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Development Of An Automated Image Analysis Pipeline To Quant...mentioning

confidence: 99%

“…To promote the formation of guttation droplets at hydathodes. and thereby natural entry of Xcc, a speci c temperature/humidity cycle was used (19).…”

Section: Xcc Disease Assaysmentioning

confidence: 99%