Glioma on Chips Analysis of glioma cell guidance and interaction in microfluidic-controlled microenvironment enabled by machine learning

In biosystems, chemical and physical fields established by gradients guide cell migration, which is a fundamental phenomenon underlying physiological and pathophysiological processes such as development, morphogenesis, wound healing, and cancer metastasis. Cells in the supportive tissue of the brain, glia, are electrically stimulated by the local field potentials from neuronal activities. How the electric field may influence glial cells is yet fully understood. Furthermore, the cancer of glia, glioma, is not only the most common type of brain cancer, but the high-grade form of it (glioblastoma) is particularly aggressive with cells migrating into the surrounding tissues (infiltration) and contribute to poor prognosis. In this thesis, I investigate how electric fields in the microenvironment can affect the migration of glioblastoma cells using a versatile microsystem I have developed. I employ a hybrid microfluidic design to combine poly(methylmethacrylate) (PMMA) and poly(dimethylsiloxane) (PDMS), two of the most common materials for microfluidic fabrication. The advantages of the two materials can be complemented while disadvantages can be mitigated. The hybrid microfluidics have advantages such as versatile 3D layouts in PMMA, high dimensional accuracy in PDMS, and rapid prototype turnaround by facile bonding between PMMA and PDMS using a dual-energy double sided tape. To accurately analyze label-free cell migration, a machine learning software, Usiigaci, is developed to automatically segment, track, and analyze single cell movement and morphological changes under phase contrast microscopy. The hybrid microfluidic chip is then used to study the migration of glioblastoma cell models, T98G and U-251MG, in electric field (electrotaxis). The influence of extracellular matrix and chemical ligands on glioblastoma electrotaxis are investigated. I further test if voltage-gated calcium channels are involved in glioblastoma electrotaxis. The electrotaxes of glioblastoma cells are found to require optimal laminin extracellular matrices and depend on different types of voltage-gated calcium channels, voltage-gated potassium channels, and sodium transporters. A reversiblysealed hybrid microfluidic chip is developed to study how electric field and laminar shear can condition confluent endothelial cells and if the biomimetic conditions affect glioma cell adhesion to them. It is found that glioma/endothelial adhesion is mediated by the Ang1/Tie2 signaling axis and adhesion of glioma is slightly increased to endothelial cells conditioned with shear flow and moderate electric field. In conclusion, robust and versatile hybrid microsystems are employed for studying glioma biology with emphasis on cell migration. The hybrid microfluidic tools can enable us to elucidate fundamental mechanisms in the field of the tumor biology and regenerative medicine.Okinawa Institute of Science and Technology Graduate Universit

Analysis of microarray and next generation sequencing data for classification and biomarker discovery in relation to complex diseases

PhDThis thesis presents an investigation into gene expression profiling, using microarray and next generation sequencing (NGS) datasets, in relation to multi-category diseases such as cancer. It has been established that if the sequence of a gene is mutated, it can result in the unscheduled production of protein, leading to cancer. However, identifying the molecular signature of different cancers amongst thousands of genes is complex. This thesis investigates tools that can aid the study of gene expression to infer useful information towards personalised medicine. For microarray data analysis, this study proposes two new techniques to increase the accuracy of cancer classification. In the first method, a novel optimisation algorithm, COA-GA, was developed by synchronising the Cuckoo Optimisation Algorithm and the Genetic Algorithm for data clustering in a shuffle setup, to choose the most informative genes for classification purposes. Support Vector Machine (SVM) and Multilayer Perceptron (MLP) artificial neural networks are utilised for the classification step. Results suggest this method can significantly increase classification accuracy compared to other methods. An additional method involving a two-stage gene selection process was developed. In this method, a subset of the most informative genes are first selected by the Minimum Redundancy Maximum Relevance (MRMR) method. In the second stage, optimisation algorithms are used in a wrapper setup with SVM to minimise the selected genes whilst maximising the accuracy of classification. A comparative performance assessment suggests that the proposed algorithm significantly outperforms other methods at selecting fewer genes that are highly relevant to the cancer type, while maintaining a high classification accuracy. In the case of NGS, a state-of-the-art pipeline for the analysis of RNA-Seq data is investigated to discover differentially expressed genes and differential exon usages between normal and AIP positive Drosophila datasets, which are produced in house at Queen Mary, University of London. Functional genomic of differentially expressed genes were examined and found to be relevant to the case study under investigation. Finally, after normalising the RNA-Seq data, machine learning approaches similar to those in microarray was successfully implemented for these datasets

Urinary exosomes protein cargo as biomarkers of Autosomal Dominant Polycystic Kidney Disease (ADPKD)

ADPKD is the most common genetic renal disease and affects 1:1000 people worldwide with highly variable rates of progression to end-stage renal disease (ESRD). While 50% of patients (PKD1 mutation) will reach ESRD at an average age of 53 years, it is not possible to predict individual rates of progression. There is an unmet clinical need for reliable biomarkers of disease progression. One potential source of biomarkers is exosomes, small vesicles released via the endosomal pathway into the extracellular space and body fluids including urine, in both healthy and diseased states. Exosomes reflect their cell of origin and contain a subset of proteins and RNAs which have been shown to play a role in biological processes and provide the potential to be prognostic markers of disease and severity. Using urine collected from ADPKD patients over a 5-year period stored in 5ml aliquots, a protocol was optimised for the isolation of urinary exosomes (UEX) from small volumes. UEX yield, purity and size were validated using nanoparticle tracking analysis, transmission electron microscopy, immunoblotting and FACS of exosome membrane markers. Liquid-chromatography tandem-mass-spectrometry of UEX proteins revealed differences in expression between: i) normal healthy controls and ADPKD patients; ii) ADPKD patients at different CKD stage; iii) patients with rapid or slow disease progression regardless of renal function at clinical presentation; and, iv) patients with poor, delayed or good responses to Tolvaptan©. In vitro experiments investigated the potential role of exosomes in promoting a disease phenotype by assessing functional changes in exosome-treated normal and ADPKD renal epithelial cells. The intracellular distribution of exosomes was assessed by confocal microscopy providing insight into the uptake of exosomes into normal and ADPKD cells. Taken together the data show an important role for UEX proteins as prognostic markers of ADPKD progression and for monitoring of Tolvaptan efficacy and suggest a role for exosomes in intercellular communication in this disease