Haustoria segmentation in microscope colour images of barley cells

Dyed barley cells in microscope colour images of biological experiments are analysed for the occurrence of haustoria of the powdery mildew fungus by a fully automated screening system. The region of interest in the images is found by applying Canny's edge detector to the hue channel of the HSV colour space. For the segmentation of potential haustoria within the dyed cells, two different methods are considered: A clustering in RGB colour space using the Expectation Maximisation (EM) algorithm, and morphological contrast enhancement of the colour image with subsequent hysteresis thresholding in the saturation channel of the enhanced images. The second approach seems to be more viable because of its robustness and more promising results

A high-throughput screening system for barley/powdery mildew interactions based on automated analysis of light micrographs

<p>Abstract</p> <p>Background</p> <p>To find candidate genes that potentially influence the susceptibility or resistance of crop plants to powdery mildew fungi, an assay system based on transient-induced gene silencing (TIGS) as well as transient over-expression in single epidermal cells of barley has been developed. However, this system relies on quantitative microscopic analysis of the barley/powdery mildew interaction and will only become a high-throughput tool of phenomics upon automation of the most time-consuming steps.</p> <p>Results</p> <p>We have developed a high-throughput screening system based on a motorized microscope which evaluates the specimens fully automatically. A large-scale double-blind verification of the system showed an excellent agreement of manual and automated analysis and proved the system to work dependably. Furthermore, in a series of bombardment experiments an RNAi construct targeting the <it>Mlo </it>gene was included, which is expected to phenocopy resistance mediated by recessive loss-of-function alleles such as <it>mlo5</it>. In most cases, the automated analysis system recorded a shift towards resistance upon RNAi of <it>Mlo</it>, thus providing proof of concept for its usefulness in detecting gene-target effects.</p> <p>Conclusion</p> <p>Besides saving labor and enabling a screening of thousands of candidate genes, this system offers continuous operation of expensive laboratory equipment and provides a less subjective analysis as well as a complete and enduring documentation of the experimental raw data in terms of digital images. In general, it proves the concept of enabling available microscope hardware to handle challenging screening tasks fully automatically.</p

Automating microscope colour image analysis using the Expectation Maximisation algorithm

Dyed barley cells in microscope colour images of biological experiments are analysed for the occurrence of haustoria of the powdery mildew fungus by a fully automated screening system. The region of interest in the images is found by applying Canny’s edge detector to the hue channel of the HSV colour space. Potential haustoria regions are extracted in RGB colour space by an adaptive Gaussian mixture classifier based on the Expectation Maximisation (EM) algorithm. Since the classes cell and haustorium are at very close quarters, their correct separation is a crucial part and needs a constraining mechanism which ties the EM algorithm to its initialisation data to prevent a too large deviation from it

A high-throughput screening system for barley/powdery mildew interactions based on automated analysis of light micrographs

Background To find candidate genes that potentially influence the susceptibility or resistance of crop plants to powdery mildew fungi, an assay system based on transient-induced gene silencing (TIGS) as well as transient over-expression in single epidermal cells of barley has been developed. However, this system relies on quantitative microscopic analysis of the barley/powdery mildew interaction and will only become a high-throughput tool of phenomics upon automation of the most time-consuming steps. Results We have developed a high-throughput screening system based on a motorized microscope which evaluates the specimens fully automatically. A large-scale double-blind verification of the system showed an excellent agreement of manual and automated analysis and proved the system to work dependably. Furthermore, in a series of bombardment experiments an RNAi construct targeting the Mlo gene was included, which is expected to phenocopy resistance mediated by recessive loss-of-function alleles such as mlo5. In most cases, the automated analysis system recorded a shift towards resistance upon RNAi of Mlo, thus providing proof of concept for its usefulness in detecting gene-target effects. Conclusion Besides saving labor and enabling a screening of thousands of candidate genes, this system offers continuous operation of expensive laboratory equipment and provides a less subjective analysis as well as a complete and enduring documentation of the experimental raw data in terms of digital images. In general, it proves the concept of enabling available microscope hardware to handle challenging screening tasks fully automatically

Adaptive feature selection for classification of microscope images

For high-throughput screening of genetically modified plant cells, a system for the automatic analysis of huge collections of microscope images is needed to decide whether the cells are infected with fungi or not. To study the potential of feature based classification for this application, we compare different classifiers (kNN, SVM, MLP, LVQ) combined with several feature reduction techniques (PCA, LDA, Mutual Information, Fisher Discriminant Ratio, Recursive Feature Elimination). We achieve a significantly higher classification accuracy using a reduced feature vector instead of the full length feature vector

The Regulation of Surface Responsive Genes in Blumeria graminis f. sp. hordei

Powdery mildew of barley is caused by the ascomycete pathogen Blumeria graminis f. sp. hordei (Bgh). Bgh is economically important throughout the world, causing crop losses varying between 5 to 20 % and in extreme cases as much as 60 %. Bgh is an obligate biotroph, relying on its host for growth and reproduction. This characteristic has hindered attempts to carry out biochemical and molecular biological analysis. Previous work had highlighted differential gene expression during Bgh development on surfaces other than the host. Consequently, this thesis had three aims. The first attempted to elucidate the nature of this gene expression. Work listed within includes studies of Bgh morphological development on the host barley, wheat, cellulose membrane, and glass. Additional studies included the assessment of gene expression, via RT-qPCR, on glass surfaces enhanced with 1-hexacosonal (a synthetic C26 aldehyde known to spur Bgh development), 16-hydroxyhexadecanoic acid (a cutin monomer found within the barley leaf), as well as surfaces of differing hydrophobicity. Results collected reenforce the surface-dependent nature of gene regulation, and highlight how gene expression is determined by the integration of multiple signal inputs. The second aim of this thesis was the transformation of Bgh utilising Agrobacterium tumefaciens. Efforts are discussed as are approaches for future work aimed at transforming this fungus. The final aim of the thesis aimed to lay foundations for work involving the assessment of 5‟-regulatory regions of genes showing clustered, and differential, expression on alternate surfaces. Utilising the phytopathogenic model fungus Magnaporthe oryzae (the causal agent of rice blast disease), 22 promoter regions were tested for their ability to drive GFP in this pathogen. 2 regions (for genes encoding a H4 histone and an aconitase) along with promoter regions selected for their conservation, were able to do so

Predicting plant environmental exposure using remote sensing

Wheat is one of the most important crops globally with 776.4 million tonnes produced in 2019 alone. However, 10% of all wheat yield is predicted to be lost to Septoria Tritici Blotch (STB) caused by Zymoseptoria tritici (Z. tritici). Throughout Europe farmers spend £0.9 billion annually on preventative fungicide regimes to protect wheat against Z. tritici. A preventative fungicide regime is used as Z. tritici has a 9-16 day asymptomatic latent phase which makes it difficult to detect before symptoms develop, after which point fungicide intervention is ineffective. In the second chapter of my thesis I use hyperspectral sensing and imaging techniques, analysed with machine learning to detect and predict symptomatic Z. tritici infection in winter wheat, in UK based field trials, with high accuracy. This has the potential to improve detection and monitoring of symptomatic Z. tritici infection and could facilitate precision agriculture methods, to use in the subsequent growing season, that optimise fungicide use and increase yield. In the third chapter of my thesis, I develop a multispectral imaging system which can detect and utilise none visible shifts in plant leaf reflectance to distinguish plants based on the nitrogen source applied. Currently, plants are treated with nitrogen sources to increase growth and yield, the most common being calcium ammonium nitrate. However, some nitrogen sources are used in illicit activities. Ammonium nitrate is used in explosive manufacture and ammonium sulphate in the cultivation and extraction of the narcotic cocaine from Erythroxylum spp. In my third chapter I show that hyperspectral sensing, multispectral imaging, and machine learning image analysis can be used to visualise and differentiate plants exposed to different nefarious nitrogen sources. Metabolomic analysis of leaves from plants exposed to different nitrogen sources reveals shifts in colourful metabolites that may contribute to altered reflectance signatures. This suggests that different nitrogen feeding regimes alter plant secondary metabolism leading to changes in plant leaf reflectance detectable via machine learning of multispectral data but not the naked eye. These results could facilitate the development of technologies to monitor illegal activities involving various nitrogen sources and further inform nitrogen application requirements in agriculture. In my fourth chapter I implement and adapt the hyperspectral sensing, multispectral imaging and machine learning image analysis developed in the third chapter to detect asymptomatic (and symptomatic) Z. tritici infection in winter wheat, in UK based field trials, with high accuracy. This has the potential to improve detection and monitoring of all stages of Z. tritici infection and could facilitate precision agriculture methods to be used during the current growing season that optimise fungicide use and increase yield.Open Acces

Identification, variabilité, et connaissance in planta des effecteurs de pathogénicité de l'oomycète plasmopara halstedii, l'agent du mildiou du tournesol

Plasmopara halstedii, l'oomycète phytopathogène à l'origine du mildiou chez le tournesol, est responsable de pertes agronomiques importantes. Pour lutter contre cet agent pathogène, des moyens de luttes génétiques existent sous forme de gènes de résistance (gènes Pl). Cependant, on observe ces 20 dernières années une recrudescence des isolats de P. halstedii contournant les gènes de résistance du tournesol utilisés en culture. Au sein des pathosystèmes, l'issue d'une interaction plante-agent pathogène dépend en grande partie de la co-évolution entre, (i) les protéines de résistance de la plante, et (ii) des protéines sécrétées de l'agent pathogène appelées effecteurs et dont le rôle est de modifier la physiologie de l'hôte pour favoriser l'infection. Au cours de cette thèse nous avons mis en place différentes approches de microscopie, de génomique et de biologie moléculaire pour étudier l'impact des effecteurs de P. halstedii dans sa virulence.Plasmopara halstedii, the plant pathogen oomycete causing downy mildew of sunflower, is responsible for important agronomic losses. To fight against this pathogen, resistance genes exist (Pl genes). Nevertheless, the last 20 years, an increase of P. halstedii isolates breaking down resistance genes used to protect sunflower wasobserved. Within pathosystems, plant-pathogen interaction issues depend largely on the coevolution between (i) the plant resistance proteins, and (ii) proteins secreted by pathogens called effectors, whose role is to modify the physiology of the host to promote infection. In this thesis we have implemented different approaches (microscopy, genomics, and molecular biology) to study the impact of effectors in the virulence of P. halstedii