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Single cell gene expression profiling and quality control
Švec, David ; Kubista, Mikael (advisor) ; Beneš, Vladimír (referee) ; Vopálenský, Václav (referee)
Single cell gene expression profiling and quality control David Švec Institute of Biotechnology AS CR, Laboratory of Gene Expression, Prague, Czech Republic TATAA Biocenter, Research & Development, Gothenburg, Sweden Abstract: Gene expression profiling has become an exceedingly important tool for describing occurence of mRNA in tissue samples and even single cells. Most often we use it for characterization of cell types, degree of differentiation and pathology on a molecular level. In our newly established laboratory, we developed high resolution qPCR tomography to show distribution of tens of maternal mRNAs within a single oocyte. We demonstrated that distribution of mRNAs has an important role in further development of the organism. For high resolution qPCR tomography, where one oocyte is divided in tens of samples and about fifty genes are studied in each sample, we optimized dye based protocol for microfluidic high-throughput platform BioMark. Next step was complementing the molecular profile of tens most important genes with information about histology of each selected tissue section using laser microdissection. As a model we used embryonic development of mouse molar. Our goal was to describe interaction of up to one hundred genes in different stages of development and on the single cell level. This...
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Single cells gene expression profiling and analysis
Novosadová, Vendula ; Kubista, Mikael (advisor) ; Beneš, Vladimír (referee) ; Vopálenský, Václav (referee)
Cells are the basic units of life. Studying complex tissues and whole organs requires an understanding of cell heterogeneity and responses to stimuli at the single-cell level. Even the cells, which belong to the same cell type, behave differently at a specific moment and contain different amount of mRNA. Quantitative polymerase chain reaction (qPCR) is one the most sensitive methods for the detection of mRNA, however, gene expression profiling in single cells leads to a large amount of missing data due to the fact that the transcript is missing, or is below the level of detection. Therefore, it is necessary to establish a new statistical approach for analysis of single cells. In this thesis the potential of single-cell gene expression profiling using the high throughput instrument Biomark, focusing on data analysis and biological interpretation, is discussed. Data normalization and handling of missing data are two important steps in data analysis that are performed differently at the single-cell level. Single cells are not normalized by reference genes but the number of cells as a normalizer is applied. Missing data are replaced by value, which is equaled one quarter of transcript amount in the cell. Furthermore it is shown how single-cell gene expression data can be viewed and how subpopulations...
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