AgroLux: bioluminescent <i>Agrobacterium</i> to improve molecular pharming and study plant immunity

Jutras, Philippe V.; Soldan, Riccardo; Dodds, Isobel; Schuster, Mariana; Preston, Gail M.; Hoorn, Renier A. L. van der

doi:10.1111/tpj.15454

Cited by 10 publications

(8 citation statements)

References 80 publications

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“…Once the Agrobacteria are inside the leaf, DNA encoding the cargo is transferred into leaf cells leading to transient expression of the cargo. Historically, agroinfiltration has been valuable in phenotyping the plant immune system (Ma et al ., 2012; Bücherl et al ., 2017; Jutras et al ., 2021).…”

Section: Overcoming Transgenesis Bottlenecks Via Somatic Gene Deliverymentioning

confidence: 99%

Exploring the frontier of rapid prototyping technologies for plant synthetic biology and what could lie beyond

Morey,

Khakhar

2024

New Phytologist

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SummaryRealizing the full potential of plant synthetic biology both to elucidate the relationship between genotype and phenotype and to apply these insights to engineer traits requires rapidly iterating through design‐build‐test cycles. However, the months‐long process of transgenesis, the long generation times, and the size‐based limitations on experimentation have stymied progress by limiting the speed and scale of these cycles. Herein, we review a representative sample of recent studies that demonstrate a variety of rapid prototyping technologies that overcome some of these bottlenecks and accelerate progress. However, each of them has caveats that limit their broad utility. Their complementary strengths and weaknesses point to the intriguing possibility that these strategies could be combined in the future to enable rapid and scalable deployment of synthetic biology in plants.

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Section: Overcoming Transgenesis Bottlenecks Via Somatic Gene Deliverymentioning

confidence: 99%

Exploring the frontier of rapid prototyping technologies for plant synthetic biology and what could lie beyond

Morey,

Khakhar

2024

New Phytologist

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“…In some cases, the image processing software ImageJ is used to obtain numerical data from images obtained with ultra-sensitive CCD cameras (21,28). The throughput of the former approach is low and the ImageJ macros used are often not well documented (21,24). Hence, reproducibility is poor and/or still requires manual preparation of the individual sample images (8).…”

Section: Introductionmentioning

confidence: 99%

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

Paauw,

Hardeman,

Pfeilmeier

et al. 2024

Preprint

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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.

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“…gardneri and tomato [22], Clavibacter michiganensis and tomato [23], Agrobacterium tumefaciens (a.k.a. Rhizobium radiobacter) and Nicotiana benthamiana [24], Pseudomonas syringae pv. phaseolicola and bean [25] and Pst and Arabidopsis [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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

Paauw,

Hardeman,

Taks

et al. 2024

Plant Methods

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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 a 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, computational image processing 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. Thereto, 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 chlorotic (yellow) leaf areas. The spread of luminescence-tagged bacteria is monitored via light-sensitive films, which are processed in a similar manner 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.

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AgroLux: bioluminescent Agrobacterium to improve molecular pharming and study plant immunity

Cited by 10 publications

References 80 publications

Exploring the frontier of rapid prototyping technologies for plant synthetic biology and what could lie beyond

Exploring the frontier of rapid prototyping technologies for plant synthetic biology and what could lie beyond

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

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

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