Interpreting the human transcriptome

Sammanfattning: The human body is made of billions of cells and nearly all have the same genome. However, there is a high diversity of cells, resulted from what part of the genome the cells use, i.e. which RNA molecules are expressed. Rapid advances within the field of sequencing allow us to determine the RNA molecules expressed in a specific cell at a certain time. The use of the new technologies has expanded our view of the human transcriptome and increased our understanding of when, where, and how each RNA molecule is expressed.The work presented in this thesis focuses on analysis of the human transcriptome. In Paper I, we describe an automated approach for sample preparation. This protocol was compared with the standard manual protocol, and we demonstrated that the automated version outperformed the manual process in terms of sample throughput while maintaining high reproducibility. Paper II addresses the impact of nuclear transcripts on gene expression. We compared total RNA from whole cells and from cytoplasm, showing that transcripts with long, structured 3’- and 5’-untranslated regions and transcripts with long protein coding sequences tended to be retained in the nucleus. This resulted in increased complexity of the total RNA fraction and fewer reads per unique transcript. Papers III and IV describe dynamics of the human muscle transcriptome. For Paper III, we systematically investigated the transcriptome and found remarkably high tissue homogeneity, however a large number of genes and isoforms were differentially expressed between genders. Paper IV describes transcriptome differences in response to repeated training. No transcriptome-based memory was observed, however a large number of isoforms and genes were affected by training. Paper V describes a transcript profiling protocol based on the method Reverse Transcriptase Multiplex Ligation-dependent Probe Amplification. We designed the method for a few selected transcripts whose expression patterns are important for detecting breast cancer cells, and optimized the method for single cell analysis. We successfully detected cells in human blood samples and applied the method to single cells, confirming the heterogeneity of a cell population.