abstract: The technology expansion seen in the last decade for genomics research has permitted the generation of large-scale data sources pertaining to molecular biological assays, genomics, proteomics, transcriptomics and other modern omics catalogs. New methods to analyze, integrate and visualize these data types are essential to unveil relevant disease mechanisms. Towards these objectives, this research focuses on data integration within two scenarios: (1) transcriptomic, proteomic and functional information and (2) real-time sensor-based measurements motivated by single-cell technology. To assess relationships between protein abundance, transcriptomic and functional data, a nonlinear model was explored at static and temporal levels. The successful integration of these heterogeneous data sources through the stochastic gradient boosted tree approach and its improved predictability are some highlights of this work. Through the development of an innovative validation subroutine based on a permutation approach and the use of external information (i.e., operons), lack of a priori knowledge for undetected proteins was overcome. The integrative methodologies allowed for the identification of undetected proteins for Desulfovibrio vulgaris and Shewanella oneidensis for further biological exploration in laboratories towards finding functional relationships. In an effort to better understand diseases such as cancer at different developmental stages, the Microscale Life Science Center headquartered at the Arizona State University is pursuing single-cell studies by developing novel technologies. This research arranged and applied a statistical framework that tackled the following challenges: random noise, heterogeneous dynamic systems with multiple states, and understanding cell behavior within and across different Barrett's esophageal epithelial cell lines using oxygen consumption curves. These curves were characterized with good empirical fit using nonlinear models with simple structures which allowed extraction of a large number of features. Application of a supervised classification model to these features and the integration of experimental factors allowed for identification of subtle patterns among different cell types visualized through multidimensional scaling. Motivated by the challenges of analyzing real-time measurements, we further explored a unique two-dimensional representation of multiple time series using a wavelet approach which showcased promising results towards less complex approximations. Also, the benefits of external information were explored to improve the image representation. / Dissertation/Thesis / Ph.D. Industrial Engineering 2011

Over recent years microbial oil has attracted much attention due to its potential to replace traditional oil sources in the production of biofuels and nutraceuticals. Its advantages arise from its independence of the food supply chain and its ease of production compared to conventional plant oils. Also, as concerns for the environment grow, microbially-synthesized oil emerges as potential competitor for the sustainable production of biodiesel. However, the high cost of its production currently hinders its large scale application. The bottlenecks to industrial microbial oil production are the cost of substrate and cultivation. Current research is focusing on process improvements to make microbial oil more competitive and worthwhile to produce. Several types of microorganisms have been explored so far and waste substrates have been utilised as cheap feedstocks. The overall cost is affected by the fermentation stage, therefore it is imperative to design cultivations with little operating requirements and high yields. Consequently, the present thesis aims to contribute to the field by developing and investigating a simple process for oleaginous yeast cultivation, focusing mainly on enhancing the yields during the bioreactor stage. Oleaginous yeasts were screened for their ability to grow on glycerol and the most promising strain was selected for further research. Then, the necessary conditions for its growth and oil accumulation were defined. Shake-flask cultivations showed that the specific growth rate and glycerol consumption of Rh. glutinis were higher at lower glycerol concentrations (smaller or equal to40 g/L), while higher C/N elemental ratios enhanced oil content. Experimental data were used to construct an unstructured kinetic model to describe and predict the system's behaviour. The Monod-based model took into account double substrate growth dependence and substrate inhibition. Following that, bioreactor cultivations extended the range of parameters studied, to include the influence of aeration rate and oxygen supply on cellular growth and microbial oil production. Cultivations at different air flow rates were performed in a 2 L bioreactor and showed that a low aeration rate of 0.5 L/min gave the best glycerol and nitrogen uptake rates, resulting in a concentration of biomass of 5.3 g/L with oil content of 33% under simple batch operation. This was improved by 68% to 16.8 g/L (cellular biomass) with similar oil content (34%) by applying a fed-batch strategy. Finally, different glycerol feeding schemes were evaluated in terms of their effect on oil accumulation. The concept of targeting first a cell proliferation stage, limited by the availability of nitrogen, followed by a lipid accumulation stage, fuelled by glycerol was tested. Continual feeding and pulsed feedings, delivering the same total amount of nitrogen (and glycerol), resulted in similar elevated values of both cellular biomass (~25 g/L) and oil content (~40%). Addition of glycerol at higher rates but giving the same total amount of nitrogen led to a further increase in oil content to 53%, resulting in an overall oil yield of more than 16 g/L (the highest achieved throughout the project). With comparable yields to those reported in the literature but achieved with a much poorer medium, there is every reason to be optimistic that microbial oil production from glycerol could be commercially viable in the future.

No description available.

Single-cell RNA sequencing and in situ sequencing were combined in a recent study of the developing human heart to explore the transcriptional landscape at three developmental stages. However, the method used in the study to create the spatial cellular maps has some limitations. It relies on image segmentation of the nuclei and cell types defined in advance by single-cell sequencing. In this study, we applied a new unsupervised approach based on graph neural networks on the in situ sequencing data of the human heart to find spatial gene expression patterns and detect novel cell and sub-cell types. In this thesis, we first introduce some relevant background knowledge about the sequencing techniques that generate our data, machine learning in single-cell analysis, and deep learning on graphs. We have explored several graph neural network models and algorithms to learn embeddings for spatial gene expression. Dimensionality reduction and cluster analysis were performed on the embeddings for visualization and identification of biologically functional domains. Based on the cluster gene expression profiles, locations of the clusters in the heart sections, and comparison with cell types defined in the previous study, the results of our experiments demonstrate that graph neural networks can learn meaningful representations of spatial gene expression in the human heart. We hope further validations of our clustering results could give new insights into cell development and differentiation processes of the human heart.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23082号 / 医博第4709号 / 新制||医||1049(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 長船 健二, 教授 妹尾 浩, 教授 村川 泰裕 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23382号 / 医博第4751号 / 新制||医||1052(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 伊藤 貴浩, 教授 滝田 順子, 教授 江藤 浩之 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

The definitive endoderm gives rise to several specialized organs, including the thymus. Improper development of the definite endoderm or its derivatives can lead to human disease; in the case of the thymus, immunodeficiency or autoimmune disorders. Human pluripotent stem cells (hPSCs) have emerged as a system to model human development, as study of their differentiation allows for elucidation of the molecular basis of cell fate decisions, under both healthy and impaired conditions. Here, we first developed a CRISPR-effector system to control endogenous gene expression in hPSCs, a novel approach to manipulating hPSC state. Next, the human-specific, loss-of-function phenotypes of candidate transcription factors driving hPSC-to-definitive endoderm differentiation were analyzed through combined CRISPR-perturbation and single-cell RNA-sequencing. This analysis revealed the importance of TGFβ mediators in human definitive endoderm differentiation as well as identified an unappreciated role for FOXA2 in human foregut development. Finally, as the differentiation of definitive endoderm to thymic epithelial progenitors (TEPs) is of particular interest, a single-cell transcriptomic atlas of murine thymus development was generated in anticipation of identifying factors driving later stages of TEP differentiation. Taken together, this dissertation establishes a CRISPR-effector system to interrogate gene and regulatory element function in hPSC differentiation strategies, details the role of specific transcription factors in human endoderm differentiation, and sets the groundwork for future investigations to characterize hPSC-derived TEPs and the factors driving their differentiation.

No description available.

Die Alzheimer-Krankheit ist eine gewaltige Bedrohung für eine alternde Gesellschaft. Millionen von Menschen leben weltweit mit der Alzheimer-Krankheit, für die es keine aktuelle Behandlung gibt. Die Amyloidkaskaden-Hypothese (AKH) ist die aktuell am meisten akzeptierte Hypothese zur Ursache der Alzheimer-Krankheit. Die AKH bietet eine mechanistische Sicht auf die pathologische Kaskade, ausgehend von der Amyloid-Aggregation über die chronische Entzündung bis hin zur TAU-Pathologie. Die Medikamente, die auf der Grundlage der AKH entwickelt wurden, konnten Amyloid-Plaques bei Alzheimer-Patienten entfernen, brachten aber keine Verbesserung der kognitiven Fähigkeiten. Diese Misserfolge legen nahe, dass die Alzheimer-Krankheit nicht nur theoretisch im Rahmen der AKH betrachtet werden kann. Neuere Hypothesen kulminieren die Auswirkungen verschiedener Zelltypen (z.B. neurale Stammzellen, Astrozyten, Oligodendrozyten) auf den Ausbruch der Alzheimer-Erkrankung. Komplexe Rückkopplungs- und Feed-Forward-Mechanismen sind in der Pathophysiologie der Alzheimer-Demenz wahrscheinlich. Das Zusammenspiel zwischen der Pathologie und der Beteiligung anderer Zelltypen macht diese Krankheit multifaktoriell und komplex. Kürzlich zeigten zwei Studien (Moreno-Jimenez et al., 2019; Tobin et al., 2019), dass die Produktion neuer Neuronen im menschlichen Gehirn bei der Alzheimer-Erkrankung dramatisch abnimmt. Eine interessante Hypothese wurde durch diese Studien gestützt: Die pathologisch induzierte Erzeugung neuer Neuronen (regenerative Neurogenese) bei Alzheimer-Patienten könnte helfen, die Pathologie der Alzheimer-Erkrankung rückgängig zu machen. Da die Regenerationsfähigkeit bei Säugetieren entwicklungsmäßig wenig ausgeprägt ist (Tanaka und Ferretti, 2009), kann uns die Untersuchung der Neurodegeneration in einem Modellorganismus mit Regenerationsfähigkeit daher lehren, wie man die Proliferation und Neurogenese neuraler Stammzellen unter pathologischen Bedingungen induzieren kann. Für diese spezielle Frage können uns Modellorganismen mit natürlicher Regenerationsfähigkeit zeigen, wie man Proliferation und Neurogenese unter den pathologischen Bedingungen der Alzheimer-Erkrankung induzieren kann. Der Zebrafisch bietet eine beispiellose Möglichkeit, die Neurodegeneration und Regeneration zu modellieren, um die molekularen Mechanismen zu untersuchen, wie anhand der Neurogenese in Wirbeltiergehirnen die Alzheimer-Krankheit verbessert werden kann. Dies wurde in unserem Labor bereits in mehreren Publikationen gezeigt. Aus diesem Grund habe ich in meiner Doktorarbeit Zebrafische verwendet, um die Plastizität neuraler Stammzellen (NSZ) zu untersuchen. Besonders interessierte mich die Heterogenität von NSZ-Populationen in Bezug auf ihre molekularen Programme und die molekulare Grundlage der regenerativen Neurogenese von NSZ auf das Amyloid-β-42 (Aβ42) und TAU-Pathologien.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19551号 / 医博第4058号 / 新制||医||1012(附属図書館) / 32587 / 京都大学大学院医学研究科医学専攻 / (主査)教授 川口 義弥, 教授 坂井 義治, 教授 羽賀 博典 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM