abstract: Identifying chemical compounds that inhibit bacterial infection has recently gained a considerable amount of attention given the increased number of highly resistant bacteria and the serious health threat it poses around the world. With the development of automated microscopy and image analysis systems, the process of identifying novel therapeutic drugs can generate an immense amount of data - easily reaching terabytes worth of information. Despite increasing the vast amount of data that is currently generated, traditional analytical methods have not increased the overall success rate of identifying active chemical compounds that eventually become novel therapeutic drugs. Moreover, multispectral imaging has become ubiquitous in drug discovery due to its ability to provide valuable information on cellular and sub-cellular processes using florescent reagents. These reagents are often costly and toxic to cells over an extended period of time causing limitations in experimental design. Thus, there is a significant need to develop a more efficient process of identifying active chemical compounds. This dissertation introduces novel machine learning methods based on parallelized cellomics to analyze interactions between cells, bacteria, and chemical compounds while reducing the use of fluorescent reagents. Machine learning analysis using image-based high-content screening (HCS) data is compartmentalized into three primary components: (1) \textit{Image Analytics}, (2) \textit{Phenotypic Analytics}, and (3) \textit{Compound Analytics}. A novel software analytics tool called the Insights project is also introduced. The Insights project fully incorporates distributed processing, high performance computing, and database management that can rapidly and effectively utilize and store massive amounts of data generated using HCS biological assessments (bioassays). It is ideally suited for parallelized cellomics in high dimensional space. Results demonstrate that a parallelized cellomics approach increases the quality of a bioassay while vastly decreasing the need for control data. The reduction in control data leads to less fluorescent reagent consumption. Furthermore, a novel proposed method that uses single-cell data points is proven to identify known active chemical compounds with a high degree of accuracy, despite traditional quality control measurements indicating the bioassay to be of poor quality. This, ultimately, decreases the time and resources needed in optimizing bioassays while still accurately identifying active compounds. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016

The natural resources offer a unique opportunity for the discovery of active compounds, due to the complexity and biodiversity of their chemical structures. Natural resources have been used as medicines throughout human history. Saudi Arabia’s natural resources, for instance its terrestrial medicinal plants and the Red Sea sponges, have not been extensively investigated with regard to their biological activities. To better identify the diversity of compounds with bioactive potential, new techniques are also necessary in order to improve the drug discovery path. This study comprises three sections. The first section examines Juniperus phoenicea (Arar), Anastatica hierochuntica (Kaff Maryam) and Citrullus colocynthis (Hanzal); these herbal plants were screened for potential bioactivity using a newly developed pipeline based on a high-content screening technique. We report a new cell-based high-throughput phenotypic screening for the bio-prospecting of unknown natural products from Saudi Arabian plants, in order to reveal their biological activities. The second section investigates Avicennia marina plants, screened for reverse transcriptase anti-HIV bioactivity using biochemical assay. Image-based high-content screening with a set of cellular stains was used to investigate the phenotypic results of toxicity and cell cycle arrest. The third section considers the isolation of Actinomycetes from Red Sea Sponges. Actinomycetes bacterial isolates were tested for bioactivity against West Nile Virus NS3 Protease. Analytical chemical techniques such as liquid chromatography–mass spectrometry (LC-MS), gas chromatography–mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR) were used to gain more understanding of the possible chemical compounds responsible for this bioactivity. Overall, the aim of this work is to investigate the potential bioactive effect of several Saudi Arabian plants and Red Sea sponges against cancer cells and viral infections. Our study demonstrates the efficiency of the newly developed pipeline using cell-based phenotypic screening. Anti-cancer potential activity was detected in Juniperus phoenicea. Bioactive potential against the reverse transcriptase enzyme of HIV virus was confirmed in Avicennia marina leaves. The organic extracts of Actinomycetes bacterial isolates were found active against West Nile Virus NS3 Protease. Here, promising starting point for the potential of drug discovery of plants and marine organism of Saudi Arabia.

京都大学 / 新制・論文博士 / 博士(医学) / 乙第13481号 / 論医博第2256号 / 新制||医||1059(附属図書館) / (主査)教授 平井 豊博, 教授 岩田 想, 教授 秋山 芳展 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

Aberrant DNA synthesis and maintenance have been implicated in numerous human diseases. I describe here a novel strategy for systematically identifying budding yeast mutants with elevated levels of DNA damage foci, which represent hubs of DNA damage and repair. A previous study manually scored foci in single mutants but was limited in its ability to survey many conditions in large populations. I developed an automated and statistically robust method for identifying aberrant foci phenotypes by combining synthetic genetic array (SGA) and high-content screening (HCS) methodology. Using this approach, I scored thousands of essential and non-essential gene mutants subjected to environmental and genetic perturbations, including the DNA damaging agent, phleomycin, and deletions of DNA repair genes, SGS1 and YKU80. Collectively, I identified a functionally enriched set of 367 mutants that had increased frequencies of DNA damage foci and established SGA-HCS as a powerful tool for investigating the yeast DNA damage response.

Aberrant DNA synthesis and maintenance have been implicated in numerous human diseases. I describe here a novel strategy for systematically identifying budding yeast mutants with elevated levels of DNA damage foci, which represent hubs of DNA damage and repair. A previous study manually scored foci in single mutants but was limited in its ability to survey many conditions in large populations. I developed an automated and statistically robust method for identifying aberrant foci phenotypes by combining synthetic genetic array (SGA) and high-content screening (HCS) methodology. Using this approach, I scored thousands of essential and non-essential gene mutants subjected to environmental and genetic perturbations, including the DNA damaging agent, phleomycin, and deletions of DNA repair genes, SGS1 and YKU80. Collectively, I identified a functionally enriched set of 367 mutants that had increased frequencies of DNA damage foci and established SGA-HCS as a powerful tool for investigating the yeast DNA damage response.

Fluorescence lifetime imaging microscopy (FLIM) is an imaging modality that is able to provide key insights into subcellular processes. When used to measure Förster resonance energy transfer (FRET), for instance, it can discern protein-protein interactions and conformational changes. This kind of information is highly useful in the drug screening process in order to determine the effectiveness of drug leads and their mechanisms of action. FLIM has yet to be successfully translated to high-content screening (HCS) platforms due to the high throughput and fine temporal and spatial resolution requirements of HCS. Our prototype HCS FLIM system uses a time-resolving instrument called a streak camera to multiplex the FLIM scanning process, allowing for 100 confocal spots to be simultaneously scanned across a sample. There have been a few major advancements to the prototype. First the fiber array used to connect the fluorescence channels to the streak camera was characterized. Its alternating fiber delay scheme was successful in greatly reducing optical crosstalk between adjacent channels. Next, an optical beam scanner for parallel excitation beams was designed and implemented, greatly improving the possible scan speeds of the system. The streak camera was upgraded to a higher repetition rate sweep, and modifications to system components and reconstruction procedures were made to accommodate the new sweep unit. A single-photon avalanche diode array was also tested as a possible replacement for the streak camera, and was found to offer photon detection efficiency advantages. Finally, improvements were made to the excitation power and optical throughput of the system in order to reduce the required exposure time. These advances to the prototype system bring it closer to realizing the requirements of HCS FLIM, and provide a clear picture for future improvements and research directions. / Thesis / Doctor of Philosophy (PhD) / Fluorescent proteins are commonly used to tag subcellular targets so that they can easily be distinguished with a fluorescence microscope. While this can help visualize where different organelles and proteins are located in the cell, a great deal more information can be gained by measuring the fluorescence lifetime at each point in the sample, which is highly sensitive to the microenvironment. Fluorescence lifetime imaging microscopy (FLIM) has the potential to be a powerful technique for testing drug leads in the drug discovery process, although current FLIM systems are not able to provide the high throughput speeds and high temporal resolution required for drug screening. This thesis project has succeeded in improving a highly parallel FLIM microscope by reducing inter-channel crosstalk, implementing an optical scanner, improving power and optical throughput, and investigating future time-resolving instruments. This progress has brought the prototype setup closer to being used in a drug screening environment.

Fundamental to all biological processes is the interplay between biomolecules such as proteins and nucleic acids. Studies of interactions should therefore be more informative than mere detection of expressed proteins. Preferably, such studies should be performed in material that is as biologically and clinically relevant as possible, i.e. in primary cells and tissues. In addition, to be able to take into account the heterogeneity of such samples, the analyses should be performed in situ to retain information on the sub-cellular localization where the interactions occur, enabling determination of the activity status of individual cells and allowing discrimination between e.g. tumor cells and surrounding stroma. This requires assays with an utmost level of sensitivity and selectivity. Taking these issues into consideration, the in situ proximity-ligation assay (in situ PLA) was developed, providing localized detection of proteins, protein-protein interactions and post-translational modifications in fixed cells and tissues. The high sensitivity and selectivity afforded by the assay's requirement for dual target recognition in combination with powerful signal amplification enables visualization of single protein molecules in intact single cells and tissue sections. To further increase the usefulness and application of in situ PLA, the assay was adapted to high content analysis techniques such as flow cytometry and high content screening. The use of in situ PLA in flow cytometry offers the possibility for high-throughput analysis of cells in solution with the unique characteristics offered by the assay. For high content screening, it was demonstrated that in situ PLA can enable cell-based drug screening of compounds affecting post-translational modifications and protein-protein interactions in primary cells, offering superior abilities over current assays. The methods presented in this thesis provide powerful new tools to study proteins in genetically unmodified cells and tissues, and should offer exciting new possibilities for molecular biology, diagnostics and drug discovery. 

Modern techniques like automated microscopy and spectroscopy now make it possible to study quantitatively, across multiple phenotypic and molecular parameters, how cell populations are affected by different treatments and/or environmental disturbances. As the technology development at the instrument level often is ahead of the data analytical tools and the scientific questions, there is a large and growing need for computational algorithms enabling desired data analysis. These algorithms must have capacity to extract and process quantitative dynamic information about how the cell population is affected by different stimuli with the final goal to transform this information into development of new powerful therapeutic strategies. In particular, there is a great need for automated systems that can facilitate the analysis of massive data streams for label-free methods such as phase contrast microscopy (PCM) imaging and spectroscopy (NMR). Therefore, in this thesis, algorithms for quantitative high-throughput phenotypic and systemic profiling of in vitro growing cell populations via label-free microscopy and spectroscopy are developed and evaluated. First a two-dimensional filter approach for high-throughput screening for drugs inducing autophagy and apoptosis from phase contrast time-lapse microscopy images is studied. Then new methods and applications are presented for label-free extraction and comparison of time-evolving morphological features in phase-contrast time-lapse microscopy images recorded from in vitro growing cell populations. Finally, the use of dynamic morphology and NMR/MS spectra for implementation of a reference database of drug induced changes, analogous to the outstanding mRNA gene expression based Connectivity Map database, is explored. In conclusion, relatively simple computational methods are useful for extraction of very valuable biological and pharmacological information from time-lapse microscopy images and NMR spectroscopy data offering great potential for biomedical applications in general and cancer pharmacology in particular.

Les biotechnologies sont arrivées au point ou la quantité d'information disponible permet de penser les objets biologiques comme des systèmes complexes. Dans ce contexte, les phénomènes qui émergent de ces systèmes sont intimement liés aux spécificités de leur organisation. Cela pose des problèmes computationnels et statistiques qui sont précisément l'objet d'étude de la communauté liée à l'apprentissage statistique. Cette thèse traite d'applications de méthodes d'apprentissage pour l'étude de phénomène biologique dans une perspective de système complexe. Ces méthodes sont appliquées dans le cadre de l'analyse d'interactions protéine-ligand et d'effets secondaires, du phenotypage de populations de cellules et du plan d'expérience pour des systèmes dynamiques non linéaires partiellement observés.D'importantes quantités de données sont désormais disponibles concernant les molécules mises sur le marché, tels que les profils d'interactions protéiques et d'effets secondaires. Cela pose le problème d'intégrer ces données et de trouver une forme de structure sous tendant ces observations à grandes échelles. Nous appliquons des méthodes récentes d'apprentissage non supervisé à l'analyse d'importants jeux de données sur des médicaments. Des exemples illustrent la pertinence de l'information extraite qui est ensuite validée dans un contexte de prédiction.Les variations de réponses à un traitement entre différents individus posent le problème de définir l'effet d'un stimulus à l'échelle d'une population d'individus. Par exemple, dans le contexte de la microscopie à haut débit, une population de cellules est exposée à différents stimuli. Les variations d'une cellule à l'autre rendent la comparaison de différents traitement non triviale. Un modèle génératif est proposé pour attaquer ce problème et ses propriétés sont étudiées sur la base de données expérimentales.A l'échelle moléculaire, des comportements complexes émergent de cascades d'interactions non linéaires entre différentes espèces moléculaires. Ces non linéarités engendrent des problèmes d'identifiabilité du système. Elles peuvent cependant être contournées par des plans expérimentaux spécifiques, un des champs de recherche de la biologie des systèmes. Une stratégie Bayésienne itérative de plan expérimental est proposée est des résultats numériques basés sur des simulations in silico d'un réseau biologique sont présentées. / Biotechnologies came to an era where the amount of information one has access to allows to think about biological objects as complex systems. In this context, the phenomena emerging from those systems are tightly linked to their organizational properties. This raises computational and statistical challenges which are precisely the focus of study of the machine learning community. This thesis is about applications of machine learning methods to study biological phenomena from a complex systems viewpoint. We apply machine learning methods in the context of protein-ligand interaction and side effect analysis, cell population phenotyping and experimental design for partially observed non linear dynamical systems.Large amount of data is available about marketed molecules, such as protein target interaction profiles and side effect profiles. This raises the issue of making sense of this data and finding structure and patterns that underlie these observations at a large scale. We apply recent unsupervised learning methods to the analysis of large datasets of marketed drugs. Examples show the relevance of extracted information which is further validated in a prediction context.The variability of the response to a treatment between different individuals poses the challenge of defining the effect of this stimulus at the level of a population of individuals. For example in the context High Content Screening, a population of cells is exposed to different stimuli. Between cell variability within a population renders the comparison of different treatments difficult. A generative model is proposed to overcome this issue and properties of the model are investigated based on experimental data.At the molecular scale, complex behaviour emerge from cascades of non linear interaction between molecular species. These non linearities leads to system identifiability issues. These can be overcome by specific experimental plan, one of the field of research in systems biology. A Bayesian iterative experimental design strategy is proposed and numerical results based on in silico biological network simulations are presented.

Un crible biologique a pour objectif de tester en parallèle l'impact de nombreuses conditions expérimentales sur un processus biologique d'un organisme modèle. Le progrès technique et computationnel a rendu possible la réalisation de tels cribles à grande échelle - jusqu'à des centaines de milliers de conditions. L'imagerie sur cellules vivantes est un excellent outil pour étudier en détail les conséquences d'une perturbation chimique sur un processus biologique. L'analyse des cribles sur cellules vivantes demande toutefois la combinaison de méthodes robustes d'imagerie par ordinateur et de contrôle qualité, et d'approches statistiques efficaces pour la détection des effets significatifs. La présente thèse répond à ces défis par le développement de méthodes analytiques pour les images de cribles temporels à haut débit. Les cadres qui y sont développés sont appliqués à des données publiées, démontrant par là leur applicabilité ainsi que les bénéfices d'une ré-analyse des données de cribles à haut contenu (HCS). Le premier workflow pour l'étude de la motilité cellulaire à l'échelle d'une cellule dans de telles données constitue le chapitre 2. Le chapitre 3 applique ce workflow à des données publiées et présente une nouvelle distance pour l'inférence de cible thérapeutique à partir d'images de cribles temporels. Enfin, le chapitre 4 présente une pipeline méthodologique complète pour la conduite de cribles temporels à haut débit en toxicologie environnementale. / Biological screens test large sets of experimental conditions with respect to their specific biological effect on living systems. Technical and computational progresses have made it possible to perform such screens at a large scale - up to hundreds of thousands of experiments. Live cell imaging is an excellent tool to study in detail the consequences of chemical perturbation on a given biological process. However, the analysis of live cell screens demands the combination of robust computer vision methods, efficient statistical methods for the detection of significant effects and robust procedures for quality control. This thesis addresses these challenges by developing analytical methods for the analysis of High Throughput time-lapse microscopy screening data. The developed frameworks are applied to publicly available HCS data, demonstrating their applicability and the benefits of HCS data remining. The first multivariate workflow for the study of single cell motility in such large-scale data is detailed in Chapter 2. Chapter 3 presents this workflow application to previously published data, and the development of a new distance for drug target inference by in silico comparisons of parallel siRNA and drug screens. Finally, chapter 4 presents a complete methodological pipeline for performing HT time-lapse screens in Environmental Toxicology.