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Identification of cell ype-specific marker genes and ptahways in the mouse brain
KTH, School of Biotechnology (BIO).
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Gene expression profiling techniques such as RNA sequencing has greatly contributed to our understanding of physiological and disease processes in the brain. Wen applied to celllular complex brain tissue samples, these techniques do not account for cell type specific expression changes and the underlying biological pathways of cell types. Aberrations in cell type gene expression patterns have been documented in brain diseases such as depression, schizophrenia, Alzheimer's among others. Therefore, gene expression at cell type resolution migh be critical to understand disease processes and biological pathways. In the recent years, several cell isolation techniques such as laser capture micro-dissection and fluorescence-activated cell sorting have been couple with microarrays for this purpose. Hoever, these methods are technially highly challengin, tim-and-resource consuming and may be limited because of potential isolation artefacts, mRNA length and abundance biases. For these combined technical issues, gene expression profiling with tissues samples is still the most widely applied approach in brain.

In this study, we aim at establishing a transcriptome database for gene expression profiles and identify marker genes that are devoid of these biases, from mousederived in vitro oliodendrocytes, microaglia, astrocytes and neurons. To this end, a modified deep sequencing method that enriches for 3' mRNA reads was used for expression profiling. This study identified numerous cell type-specific gene markers that can potentially be usedd to characterize cell types and even estimate that proportion of cell types in tissue samples. Additionally, a novel strategy based on RNA abundance to compare the pathway enrichment between the cell types was developed and pathways that are particularly enriched in individual cell types were identified. Thus, this transcriptome database of digital RNA sequencing data generated for the major cell types of the brain can be used as reference information for cell type specific gene expression profiles to overcome some limitations of expression studies from brain tissues.

Place, publisher, year, edition, pages
National Category
Engineering and Technology
URN: urn:nbn:se:kth:diva-173799OAI: diva2:854845
Available from: 2015-09-18 Created: 2015-09-18 Last updated: 2015-09-18Bibliographically approved

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