Publicly-available microarray data constitutes a huge resource for researchers in biological science. A wealth of microarray data is available for the model organism – the mouse. Pluripotent embryonic stem (ES) cells are able to give rise to all of the adult tissues of the organism and, as such, are much-studied for their myriad applications in regenerative medicine. Fully differentiated, somatic cells can also be reprogrammed to pluripotency to give induced pluripotent stem cells (iPSCs). ES cells progress through a range of cellular states between ground state pluripotent stem cells, through the primed state ready for differentiation, to actual differentiation. Microarray data available in public, online repositories is annotated with several important fields, although this accompanying annotation often contains issues which can impact its usefulness to human and / or programmatic interpretation for downstream analysis. This thesis assembles and makes available to the research community the largest-to-date pluripotent mouse ES cell (mESC) microarray dataset and details the manual annotation of those samples for several key fields to allow further investigation of the pluripotent state in mESCs. Microarray samples from a given laboratory or experiment are known to be similar to each other due to batch effects. The same has been postulated about samples which use the same cell line. This work therefore precedes the investigation of transcriptional events in mESCs with an investigation into whether a sample's cell line or source laboratory is a greater contributor to the similarity between samples in this collected pluripotent mESC dataset using a method employing Random Submatrix Total Variability, and so named RaSToVa. Further, an extension of the same permutation and analysis method is developed to enable Discovery of Annotation-Linked Gene Expression Signatures (DALGES), and this is applied to the gathered data to provide the first large-scale analysis of transcriptional profiles and biological pathway activity of three commonly-used mESC cell lines and a selection of iPSC samples, seeking insight into potential biological differences that may result from these. This work then goes on to re-order the pluripotent mESC data by markers of known pluripotency states, from ground state pluripotency through primed pluripotency to earliest differentiation and analyses changes in gene expression and biological pathway activity across this spectrum, using differential expression and a window-scanning approach, seeking to recapitulate transcriptional patterns known to occur in mESCs, revealing the existence of putative “early” and “late” naïve pluripotent states and thereby identifying several lines of enquiry for in-laboratory investigation.

The detection and analysis of biomolecules, such as proteins, are of great interest since these molecules are fundamental for life and our health. Due to the complexity of biological processes, there is a great advantage of studying proteins in their natural context, for example by using bioimaging. The objective of this doctoral thesis has been to develop, implement and evaluate techniques for the use of proteinspecific affinity reagents in diverse bioimaging platforms for analysis of protein expression in situ in cells and tissues. To be able to visualize a desired protein in situ using affinity reagents, reporter labels are needed. A novel technique for labeling of antibodies on solid phase was developed. This method offers simultaneous purification, concentration and labeling of an antibody sample, giving highly predictable and reproducible results, in a miniaturized format. Another study demonstrates the use of an alternative affinity reagent, the Affibody molecule, in bioimaging as well as other immunoassays. As a relevant proof-of-principle, an Affibody molecule binding the HER2 receptor was site-specificly labeled and employed for analysis of HER2 protein expression in cells and tissue using immunofluorescence (IF), immunohistochemistry (IHC), immunoprecipitation and flow cytometry. Furthermore, it is shown how antibody-based bioimaging approaches can be applied for systematic analysis of protein expression in terms of subcellular localization and expression levels in cell lines. The systematic subcellular localization of nearly 500 proteins was performed using IF and confocal microscopy. Global analysis of expression levels of nearly 2000 proteins in a panel of cell lines using IHC and automated image analysis, revealed that most proteins are expressed in a cell size dependent manner. Two normalization approaches were evaluated and found to allow for protein profiling across the panel of morphologically diverse cells, revealing patterns of protein over- and underexpression, and proteins with stable as well as with lineage specific expression were identified. Finally, the value of antibody-based, bioimaging proteomics as a platform for biomarker discovery is demonstrated. The identification and in depth study of a candidate biomarker for colorectal cancer, SATB2, is described using both IHC and IF bioimaging. Results from extended analyses of tumor biopsies showed that detection of SATB2 protein using IHC provides a clinically relevant diagnostic tool with high specificity and sensitivity to aid in diagnosis of colorectal cancer. Furthermore, the study demonstrated a potential prognostic role of SATB2, as decreased expression was associated with a significantly shorter overall survival in patients with advanced colorectal cancer. / QC 20100824