Development of analytical and bioinformatic tools for mass spectrometry-based untargeted metabolomics

代谢组学是一门研究生物体液、细胞和组织中所有小分子的学科。它是识别与特定表型相关的生物标志物以及阐明异常生物过程的潜在机制的有力工具。代谢组学已成为生物学后基因组时代不可或缺的技术，与其他“组学”数据（例如转录组学和蛋白质组学数据）分析相关联，以实现系统级生物学研究。在各种用于进行代谢组学的分析平台中，质谱（MS）因其高通量、灵敏度、特异性和覆盖范围而成为最突出的技术。在我的职业生涯中，我在开发基于ms的分析和生物信息学工具方面打下了坚实的基础，用于非靶向代谢物检测、数据处理和代谢物鉴定。我还将代谢组学应用于各种生物学问题，以发现生物标志物并破译生物过程中涉及的分子机制。

Metabolomics is the study of the entire set of small molecules in biofluids, cells, and tissues. It is a powerful tool for the identification of biomarkers related to a particular phenotype, as well as for elucidating the underlying mechanisms of abnormal biological processes. Metabolomics has emerged as an indispensable technology in the post-genomic era of biology, correlating with other “omic” data (e.g., transcriptomic and proteomic data) analysis towards systems-level biological studies. Among various analytical platforms utilized to perform metabolomics, mass spectrometry (MS) is the most prominent technology owing to its high throughput, sensitivity, specificity and coverage. Over the course of my career I have built a strong foundation in the development of MS-based analytical and bioinformatic tools for untargeted metabolite detection, data processing, and metabolite identification. I have also applied metabolomics to various biological questions to discover biomarkers and decipher the molecular mechanisms involved in biological processes. The research goal of my lab within the next 5 years is to develop novel analytical and bioinformatic tools in the field of metabolomics. In more detail, I would like to propose the following four research objectives. Firstly, we will develop analytical workflows that enable simultaneous profiling of both the metabolome and lipidome using small amounts of biological sample (e.g., mouse brain regions, blood droplets, immune cell subtypes). Secondly, in recognition of the limitations of conventional metabolic feature extraction programs, we plan to develop next generation feature extraction programs that offer more effective feature extraction from raw LC-MS data. Thirdly, we will develop novel spectral matching algorithms and metabolite identification platforms to enable the identification of unknown metabolites that are not archived in metabolite spectral libraries. Lastly, we will address the growing demand of systems-level interpretation of metabolomics data by developing a correlation-based multi-omic integration platform. My long-term research goal is to drive metabolomics forward and bring it into the toolbox of biological scientists to achieve a more comprehensive understanding of biological phenomena. The proposed research activities will result in the development of several novel analytical techniques and bioinformatic tools to facilitate the application of metabolomics to a broader range of biological research areas. These proposed objectives would yield groundbreaking research in the field of metabolomics and systems biology, thereby expanding our understanding of life science.