INTRODUCTION: In the last two decades metabolomics become a versatile tool for categorize cellular metabolite. New perception on cellular pathways can be gained by incorporating metabolic data to altered gene expression profiles. The development of robust validation method for cellular metabolic analysis facilitate various applications of cell culture. One of the most utilized technologies used in metabolomics study is one-dimensional proton NMR spectroscopy (1D-1H-NMR). But due to the spectral overlap of the resonance and measurement error it hampered the specific identification of metabolites. Principal Component Analysis (PCA) and Partial Least Square (PLS) regression are the most commonly used multivariate statistical methods to process the large amount of data generated by metabolomics analysis. New methods has been developed recently to minimize the spectral convolution thus maximize the metabolic specification including 2D-NMR spectroscopy. Besides, 2D-NMR spectroscopy required a long acquisition time for 2D-spectra.
OBJECTIVES: My main research interest is to develop new analytical method for metabolomics using 2D-NMR spectroscopy to overcome the spectral convolution hence getting accurate metabolomics data for cell culture. The main focus of my work will be developing new techniques to reduce the acquisition time period in 2D-NMR spectroscopy. The impact of different pulse sequence of NMR spectroscopy in metabolite identification will be also observed.
METHOD: Preliminary studies will involve literature survey on analytical methods on metabolomics analysis of cell culture based on NMR spectroscopy. Then my research will be focused on studying cell culture techniques and how to optimize it, the basic principle of different types of NMR spectroscopy, and the effect of pulse sequence of NMR spectroscopy in metabolic profiling. The metabolite of different cell concentration will also be studied, for the unbiased sample set to minimize the sample number Plackett-Burman design can be implemented. I will also study different statistical analysis such as PCA, PLS and other techniques to convert the metabolomics data into information. I will observed the influence of measurement and data processing error on the final result of metabolite concentration. For the improvement of analytical method, simplify the spectra and reduce the error I will examined NMR spectroscopy methods such as 2D-NMR or combination of optimized 1D-NMR techniques.
IMPACT: In biological research, metabolomics data offers a wide range of application in biomarker discovery, toxicology, genetic modification, drug discovery, disease diagnosis, development in agriculture. In biopharmaceuticals industry, metabolomics analysis can reveal the biological pathways of a gene that could be useful for therapeutic drug discovery. Agriculture sector also influence by the metabolomics analysis, studying the metabolism of a specific insect, pesticide can be prepared to damage the targeted gene of the insect. Metabolomics analysis has impact on both human health and environment. Thus, accurate metabolite identification with a simple, cost effective and robust analytical method allows for more experiments to be carried out and more specific metabolomics data to be collected.