Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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List of Figures 1.1 Estimates of survival amongst GBM patients treated with radiotherapy alone or radiotherapy with the alkylating agent temo- zolomide. Taken from Stupp et al 2005 [472]. . . . . . . . . . . . 7 1.2 KEGG Glioma Pathway. . . . . . . . . . . . . . . . . . . . . . . 23 1.3 Visualisation generated from list of 345 interactors (orange) of TP53 (yellow) from the BioGRID 3.1 [62] repository for inter- action datasets. . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 1.4 The Biocarta pathway for Rb signaling. . . . . . . . . . . . . . . 30 2.1 Cross-section through the neural tube. . . . . . . . . . . . . . . 35 2.2 Surface markers of radial glia are expressed by NS cell lines, indicating that these cells may provide the biological context to work with progenitors of the CNS. . . . . . . . . . . . . . . . . . 37 2.3 Sources of NS cells: (a) ICM; (b) SVZ. . . . . . . . . . . . . . . 38 2.4 (a,b) Contrast microscopy images of early phase neurosphere formation. (c,d) Immunofluorescence microscopy images of EGFR and Nestin detected on an intact neurosphere. . . . . . . . . . . 39 2.5 Schematisation of the neurosphere assay used to study neural precursor cells in culture. . . . . . . . . . . . . . . . . . . . . . . 41 2.6 Diagram of the progressive lineage restriction of ES cells differ- entiating toward the neural phenotype. . . . . . . . . . . . . . . 44 2.7 Protocol describing conversion of ES cells into immortalised NS cell lines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.8 Representation of the Sox1-GFP reporter construct used in the niche-independent NS cell protocol. . . . . . . . . . . . . . . . . 46 2.9 Roles of EGF and FGF2 in the derivation and maintenance of NS cells. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.1 Stem cell differentiation hierarchy. . . . . . . . . . . . . . . . . . 58 3.2 Diagram of asymmetric and symmetric cell division. . . . . . . . 59 308
3.3 Schematisation of cell cycle phases. . . . . . . . . . . . . . . . . 67 3.4 Glioblastoma treatment with ionizing radiation. . . . . . . . . . 68 3.5 Glioblastoma treatment with BMPs. . . . . . . . . . . . . . . . 70 3.6 PCA diagram of global mRNA expression in GNS cell lines. . . 82 4.1 Classes of non-coding RNAs discovered to date. . . . . . . . . . 86 5.1 Schematisation of the longSAGE protocol. . . . . . . . . . . . . 95 5.2 Boxplot of normalised Ct values. . . . . . . . . . . . . . . . . . . 102 5.3 Correlation scatter plot of the raw Ct values vs endogenous controls. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.4 Scatter plots of our A and B biological replicates. . . . . . . . . 106 5.5 Dot plot of the standard deviations of the differences between expression levels in two replicates. . . . . . . . . . . . . . . . . . 107 5.6 Literature mining diagram of code functions. . . . . . . . . . . . 109 5.7 Schematisation of a typical Cytoscape network. . . . . . . . . . 115 6.1 Sequencing construct schematisation. . . . . . . . . . . . . . . . 121 6.2 Diagram of the extraction, filtering and mapping phases for reads and tags. . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 6.3 Pie charts for the proportion of filtered tags. . . . . . . . . . . . 123 6.4 Diagram of the tag mapping strategy. . . . . . . . . . . . . . . . 124 6.5 Pie charts for the assignment of tags to genes. . . . . . . . . . . 126 6.6 Correlations for all combinations of cell lines. . . . . . . . . . . . 127 6.7 CGH array analysis. . . . . . . . . . . . . . . . . . . . . . . . . 128 6.8 Curves show distributions of expression level differences between GNS and NS lines. . . . . . . . . . . . . . . . . . . . . . . . . . 129 6.9 Plot of the estimates of the variance against the base levels for each gene. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 6.10 Plot of the fold change versus the mean for normal vs tumour samples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 6.11 Tag mapping of NTRK2 on UCSC genome browser. . . . . . . . 135 6.12 Expression estimates correlate well between Tag-seq and qRT- PCR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 6.13 Heatmap of 29 genes differentially expressed between 16 GNS and 6 NS cell lines . . . . . . . . . . . . . . . . . . . . . . . . . 138 6.14 Expression levels of the 29 genes distinguishing GNS from NS lines as percent of NS geometric mean. . . . . . . . . . . . . . . 142 6.15 Tag mapping of BMP7 on UCSC genome browser. . . . . . . . . 148
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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
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1.2 Glioblastoma Multiforme Introdu
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1.3 Primary and Secondary Glioblast
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1.4 Pathways Involved in Glioblasto
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1.5 Pathway Crosstalk Introduction
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Chapter 2 Neurogenesis Contents 2.1
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2.1 Radial Glia Introduction VZ adj
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2.2 Neural Stem Cells Introduction
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Chapter 3 Brain Cancer Stem Cells C
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3.1 The Cancer Stem Cell Hypothesis
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3.1 The Cancer Stem Cell Hypothesis
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3.2 Brain Cancer Stem Cells Introdu
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3.3 Glioma Culture Systems Introduc
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Chapter 4 The Non-Coding RNA World
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4.1 MicroRNA regulation Introductio
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4.1 MicroRNA regulation Introductio
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4.2 Target Prediction and Validatio
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Methods 93
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5.1 Tag-sequencing Data Processing
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5.1 Tag-sequencing Data Processing
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5.2 Array Comparative Genomic Hybri
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5.4 Quantitative Real Time-PCR Vali
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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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Page 277 and 278: End Select End If End Sub Appendix
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Page 323 and 324: Bibliography [1] Cell signaling tec
Page 325 and 326: [38] G. Bain, D. Kitchens, M. Yao,
Page 327 and 328: [78] S. Bustin, V. Benes, J. Garson
Page 329 and 330: [112] M. Czystowska, J. Han, M. Szc
Page 331 and 332: [153] T. Fujiwara, M. Bandi, M. Nit
Page 333 and 334: [192] M. Hernandez, M. Nieto, and M
Page 335 and 336: [231] T.-M. Kim, W. Huang, R. Park,
Page 337 and 338: [268] H. Lemjabbar-Alaoui, A. van Z
Page 339 and 340: [305] K. MAEDA, S. MATSUHASHI, K. T
Page 341 and 342: [342] H. Moon, M. Ahn, J. Park, K.
Page 343 and 344: [379] D. Park and J. Rich. Biology
Page 345 and 346: [417] P. Rakic. Guidance of neurons
Page 347 and 348: [456] T. Shima, N. Okumura, T. Taka
Page 349 and 350: [490] P. A. C. t’Hoen, Y. Ariyure
Page 351 and 352: [523] T. Watanabe, A. Takeda, T. Ts
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