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

代谢组学是研究生物流体、细胞和组织中的整套小分子的学科。它是鉴定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。代谢组学已经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，以进行系统水平的生物学研究。在用于执行代谢组学的各种分析平台中，质谱法（MS）由于其高通量、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，这些工具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。 我的实验室在未来5年的研究目标是在代谢组学领域开发新的分析和生物信息学工具。具体而言，我想提出以下四个研究目标。首先，我们将开发分析工作流程，使用少量生物样品（例如，小鼠脑区、血滴、免疫细胞亚型）。其次，认识到传统代谢特征提取程序的局限性，我们计划开发下一代特征提取程序，从原始LC-MS数据中提供更有效的特征提取。第三，我们将开发新的光谱匹配算法和代谢物识别平台，以识别未在代谢物光谱库中存档的未知代谢物。最后，我们将通过开发基于相关性的多组学集成平台来解决代谢组学数据的系统级解释的日益增长的需求。 我的长期研究目标是推动代谢组学向前发展，并将其纳入生物科学家的工具箱，以实现对生物现象的更全面的理解。拟议的研究活动将导致开发几种新的分析技术和生物信息学工具，以促进代谢组学在更广泛的生物研究领域的应用。这些提出的目标将在代谢组学和系统生物学领域产生开创性的研究，从而扩大我们对生命科学的理解。

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.