Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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2.2 Neural Stem Cells Introduction signals from a niche are provided by soluble factors and the ECM, and neurospheres are spheroid structures that consist of cells producing their own ECM molecules, such as laminins, fibronectin, chondroitin sulphate proteoglycans, as well as growth factors and beta integrins, epidermal growth factor recep- tors, and cadherins. Therefore, the cell-cell and cell-matrix interactions in the three-dimensional structure of neurospheres can create an environment that is more physiologically relevant than the two-dimensional one in adherent culture systems. Even so, the robustness of cell proliferation in adherent cultures is probably due to the fibronectin and laminin substrates, in that fibronectin is a ubiquitous component of various types of ECMs, and laminin was reported as one of the five different substrates regulating neural differentiation of hu- man ES cells, and precisely stimulating their expansion in a dose dependent manner [303]. In the light of the increasing evidence that astrocytes [210] and endothelial cells [354,452] might also be important components of a niche for NS cells on top of the ECM, the adherent culture system needs to be optimised in this regard. 56
Chapter 3 Brain Cancer Stem Cells Contents 3.1 The Cancer Stem Cell Hypothesis . . . . . . . . . . . . . . 57 3.2 Brain Cancer Stem Cells . . . . . . . . . . . . . . . . . . . 63 3.3 Glioma Culture Systems . . . . . . . . . . . . . . . . . . . . 72 3.1 The Cancer Stem Cell Hypothesis The cancer stem cell hypothesis proposes that organisation of cell lineage in tumours is hierarchical and only a subpopulation of cells termed "cancer stem cells" is responsible for tumour expansion. According to this hypothesis, stem cells or cells that acquired the ability to self-renew, accumulate genetic changes over long periods of time, escape from the control of their environment, and give rise to cancerous growth. One of the postulations of the cancer stem cell hypothesis is that a population of cells with stem cell-like features exists in tumours and this population gives rise to the bulk of the tumour cells with more differentiated phenotypes [123,457]. Two types of cells could fulfill the role of tumour-initiating cell: adult stem cells or their progenitors, which normally undergo limited numbers of cell divisions and aberrantly acquire the capacity to self-renew by accumulating genetic lesions and subsequently becoming the long-lived target (Fig 3.1). Regardless of the cell of origin, the cancer stem cell is defined by its stem cell-like properties [486]. Normal adult stem cells are attractive candidates because they are tissue-specific, they can self-renew and, finally, they can differentiate into all cell types of the tissue of origin. Each division produces at least one daughter cell that maintains the indefinite capacity for cell division and a progenitor cell that has finite division capacity, ultimately differentiating into the mature cell types that constitute specific 57
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Transcriptional Characterization of
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This dissertation is my own work an
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Acknowledgements I would like to th
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5.9 External Dataset Expression Cor
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Introduction 1
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1.1 Glial Cells in the Central Nerv
Page 13 and 14: 1.2 Glioblastoma Multiforme Introdu
Page 19 and 20: 1.3 Primary and Secondary Glioblast
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Page 39 and 40: 1.5 Pathway Crosstalk Introduction
Page 41 and 42: Chapter 2 Neurogenesis Contents 2.1
Page 43 and 44: 2.1 Radial Glia Introduction VZ adj
Page 45 and 46: 2.2 Neural Stem Cells Introduction
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Page 67 and 68: 3.1 The Cancer Stem Cell Hypothesis
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5.5 Literature Mining Methods Figur
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5.5 Literature Mining Methods How m
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5.6 Differential Isoform Expression
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5.9 External Dataset Expression Cor
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5.10 Glioblastoma Pathway Construct
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5.11 MicroRNA Target Prediction Ana
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Results 119
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6.2 Tag mapping Results Table 6.1:
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6.2 Tag mapping Results 123 Figure
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6.3 Copy Number Aberrations Results
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6.3 Copy Number Aberrations Results
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6.4 Core Differentially Expressed G
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6.5 Large-scale qRT-PCR Validation
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6.6 Literature Mining for Different
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6.7 Isoform Differential Expression
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6.8 Long ncRNA Differential Express
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6.8 Long ncRNA Differential Express
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Chapter 7 Dataset Correlation Analy
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7.1 Enrichment Analysis Results Tab
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7.1 Enrichment Analysis Results Fig
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7.1 Enrichment Analysis Results Tab
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7.2 Glioblastoma Expression Signatu
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7.3 Tumour Expression Correlation R
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7.4 Survival Analysis Results Table
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7.4 Survival Analysis Results 867 g
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7.5 Glioblastoma Pathway Analysis R
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8.1 Principles Results say, this is
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8.3 Databases Results Figure 8.1: O
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8.3 Databases Results Figure 8.3: W
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8.4 Filters Results Bayesian method
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8.5 Target Prediction Ensemble Anal
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9.1. Digital Profiling of GNS Cell
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9.2. MicroRNA Target Prediction Ana
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9.3. Concluding Remarks Appendix pr
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A.1 Differential Expression Appendi
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A.2 Classified Differential Express
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A.3 Quantitative RT-PCR Appendix Ta
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A.3 Quantitative RT-PCR Appendix We
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A.3 Quantitative RT-PCR Appendix Ta
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A.3 Quantitative RT-PCR Appendix We
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A.4 Tag-seq vs qRT-PCR Correlation
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Appendix "BEX5", "DIAPH2", "GBP3",
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End Select End If End Sub Appendix
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If NameStr.ToLower().Equals("query"
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Appendix C Long ncRNAs We detected
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C. Long ncRNAs Appendix Tag Accessi
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D.1 Pathway Interactions Appendix F
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D.2 Pathway Images Appendix Figure
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D.2 Pathway Images Appendix Figure
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E. Exon Array Data Appendix Average
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F. MicroRNA Array Data Appendix Tab
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F. MicroRNA Array Data Appendix GNS
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F. MicroRNA Array Data Appendix GNS
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Ln Natural logarithm LOH Loss of He
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3.3 Schematisation of cell cycle ph
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8.3 Workflows at the core of the pr
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7.1 Selected Gene Ontology terms an
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Bibliography [1] Cell signaling tec
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