Understanding the complex metabolic networks present in organisms, through the use of high throughput liquid chromatography coupled mass spectrometry, will give insight into the physiological changes responding to stress. However the lack of a proper work flow and robust methodology hinders verifiable biological interpretation of mass profiling data. In this study a novel workflow has been developed. A novel Kernel based feature alignment algorithm, which outperformed Agilent’s Mass profiler and showed roughly a 20% increase in alignment accuracy, is presented for the alignment of mass profiling data. Prior to statistical analysis post processing of data is carried out in two stages, noise filtering is applied to consensus features which were aligned at a 50% or higher rate. Followed by missing value imputation a method was developed that outperforms both at model recovery and false positive detection. The use of parametric methods for statistical analysis is inefficient and produces a large number of false positives. In order to tackle this three non-parametric methods were considered. The histogram method for statistical analysis was found to yield the lowest false positive rate. Data is presented which was analysed using these methods to reveal metabolomic changes during plant pathogenesis. A high resolution time series dataset was produced to explore the infection of Arabidopsis thaliana by the (hemi) biotroph Pseudomonas syringe pv tomato DC3000 and its disarmed mutant DC3000hrpA, which is incapable of causing infection. Approximately 2000 features were found to be significant through the time series. It was also found that by 4h the plants basal defence mechanism caused the significant ‘up-regulation’ of roughly 400 features, of which 240 were found to be at a 4-fold change. The identification of these features role in pathogenesis is supported by the fact that of those features found to discriminate between treatments a number of pathways were identified which have previously been documented to be active due to pathogenesis

Cell mass is an important indicator of cell health and status. A diverse set of techniques have been developed to precisely measure the masses of single cells, with varying degrees of technical complexity and throughput. Here, the development of a non-invasive, label-free optical technique, termed Live-Cell Interferometry (LCI), is described. Several applications are presented, including an evaluation of LCI’s utility for assessing drug response heterogeneity in patient-derived melanoma lines and the measurement of CD3+ T cell kinetics during hematopoietic stem cell transplantation. The characterization of mast cells during degranulation, the measurement of viral reactivation kinetics in Kaposi’s Sarcoma, and drug response studies in patient-derived xenograft models of triple-negative breast cancer are also discussed. Taken together, data from these studies highlight LCI’s versatility as a tool for clinical, translational, and basic research applications.