Protein phosphorylation plays critical roles in diverse cellular functions, including cell cycle, growth, differentiation, and apoptosis. Deregulated phospho-signaling is often associated with many human diseases and cancers. Despite tremendous efforts to investigate the molecular mechanisms that control the functionality of phospho-signaling pathways, only limited progress has been made on describing the temporal and spatial profiles of cellular protein phosphorylation. The main challenges associated with the study of phospho-signaling processes in cells are related to the short time-scale of certain phosphorylation and dephosphorylation events, the low abundance of the phosphorylated protein forms as compared to their non-phosphorylated counterparts, the complicated and time-consuming sample preparation methods that are accompanying such type of work, and, last, the performance of the detection methods that are suitable for assessing protein phosphorylation. To tackle the challenges associated with the investigation of protein phosphorylation in cells, our objective was to develop a combined mass spectrometry (MS) and microfluidics strategy that enables fast sampling and sensitive detection of key signaling phosphoproteins in complex biological samples. MS is the most widely used analytical tool in the field of proteomics due to its high sensitivity, specificity, and throughput. Microfluidics has been proven as a suitable platform for handling small volumes of scarce samples, being also amenable to automation, integration, and multiplexing. To achieve our objective, this study was conducted in multiple steps: (1) We performed a comprehensive analysis of the factors that affect the performance of mass spectrometry detection (i.e., sensitivity, reproducibility, ability to accurately identify a large number of proteins from complex samples), when used in conjunction with technologies that are conducted in a non-standard fashion, on short time-scales; (2) We developed and evaluated a miniaturized strategy for rapid proteolytic digestion and phosphopeptide enrichment; (3) We demonstrated sensitive detection and quantification of phosphopeptides from complex biological samples using multiple reaction monitoring mass spectrometry (MRM-MS) and microfluidic sample processing; and (4) We developed a microfluidic platform for handling and processing cells that enables the investigation of biological processes that occur on short time-scales, and that can be integrated with the devices developed for the analysis of phospho-proteins. SKBR3 cells were used as a model system for developing and demonstrating the microfluidic chips. The detection and quantification of phospho-proteins involved in MAPK (mitogen activated protein kinase) signaling pathways was achieved at the low nM level. Overall, this study demonstrates proof-of-concept applicability of a microfluidics-MS strategy for monitoring phosphorylation processes in signaling networks. / PHD

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第22643号 / 医博第4626号 / 新制||医||1044(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 髙折 晃史, 教授 斎藤 通紀, 教授 生田 宏一 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

A measure of the quality of Voronoi tessellations resulting from various mesh generators founded on feature-driven models is introduced in this work. A planar tessellation covers an image with polygons of various shapes and sizes. Tessellations have potential utility due to their geometry and the opportunity to derive useful information from them for object recognition, image processing and classification. Problem domains including images are generally feature-endowed, non-random domains. Generators modeled otherwise may easily guarantee quality of meshes but certainly bear no reference to features of the meshed problem domain. They are therefore unsuitable in point pattern identification, characterization and subsequently the study of meshed regions. We therefore found generators on features of the problem domain. This provides a basis for element quality studies and improvement based on quality criteria. The resulting polygonal meshes tessellating an n-dimensional digital image into convex regions are of varying element qualities. Given several types of mesh generating sets, a measure of overall solution quality is introduced to determine their effectiveness. Given a tessellation of general and mixed shapes, this presents a challenge in quality improvement. The Centroidal Voronoi Tessellation (CVT) technique is developed for quality improvement and guarantees of mixed, general-shaped elements and to preserve the validity of the tessellations. Mesh quality indicators and entropies introduced are useful for pattern studies, analysis, recognition and assessing information. Computed features of tessellated spaces are explored for image information content assessment and cell processing to expose detail using information theoretic methods. Tessellated spaces also furnish information on pattern structure and organization through their quality distributions. Mathematical and theoretical results obtained from these spaces help in understanding Voronoi diagrams as well as for their successful applications. Voronoi diagrams expose neighbourhood relations between pattern units. Given this realization, the foundation of near sets is developed for further applications. / February 2017