大规模非靶向LC-MS代谢组学分析新策略研究

大规模代谢组学作为精准医疗研究的重要组成部分，在疾病预警、诊断及治疗等方面发挥重要作用。但实现代谢组学进一步临床应用亟待解决代谢组快速、高效、稳定检测，并给出标准化数据的问题。本项目基于高分辨质谱技术发展非靶向方法“拟”靶向化的代谢组学新策略，建立一套适用于大规模临床样本分析的LC-HRMS代谢组学分析检测、代谢物信息提取及数据整合校正的全新分析策略。包括：针对分析通量不足问题，发展基于96孔微板SPE和微柱的高通量分析方法；针对大规模样本分析中冗余数据干扰、低丰度有用数据丢失问题，发展基于峰匹配结果和数据库信息结合的非靶向LC-MS代谢组学数据拟靶向提取新方法；针对多批次、不同来源组学数据难以整合问题，基于信息提取新方法，发展可靠高效的信号漂移校正方法。并将所发展的新策略应用于2型糖尿病研究，为糖尿病分型预警临床应用提供基础。本项目的实施将为精准医学提供更可靠的代谢组学技术平台。

Precision medicine is an emerging approach for disease prevention and treatment that takes into account individual variation in genes and lifestyle for each person. This approach aims to classify and diagnose diseases accurately via large population cohort studies. Large-scale metabolomics and metabolic big data are important components of precision medicine, both playing crucial roles in early warning, diagnosis and treatment of diseases. The puzzles that limit the further application of metabolomics in clinic lie in how to realize the rapid, high-efficient and stable detection of metabolome and the standardization of the data..To find a solution to the puzzle facing for metabolomics, the present project intends to develop a new strategy of metabolomics based on high-resolution mass spectrometry (HRMS) for pseudo-targeting of non-targeted methods, that is, to establish a set of new analytical strategies for LC-HRMS metabolomics analysis, high-efficient metabolic information extraction and data integration and correction, making it suitable for large-scale clinical sample analysis. Our goal will be achieved from three aspects, including: i) a high throughput analytical method based on 96-hole micro-plate SPE and micro-column will be developed to solve the problem of insufficient analytical flux; ii) a new pseudo-targeted extraction method for metabolic information extraction of non-targeted LC-MS data based on the combination of peak matching results and online database will be developed to solve the problems of redundant data interference and low abundance useful data loss in large-scale sample analysis; and iii) a reliable and efficient signal drift correction method will be developed based on the established new metabolic extraction strategy in order to solve the difficulty in integrating different sets of data in multiple batches. In the end, the set of the new strategies will be applied to large-scale cohort study of type 2 diabetes mellitus to evaluate the applicability..The implementation of this project will provide a more reliable metabolomics platform for precision medicine.