New analytical methods for studying low abundance metabolome

代谢组学新兴领域的主要目标是分析特定生物系统中存在的所有小分子代谢物，以便更好地了解生命系统中发生的动态过程。然而，在一次分析中对数百或数千种代谢物进行综合研究的分析方法目前还不发达，更适合于中到高丰度的稳定代谢物，并且缺乏适当的验证程序。拟议的研究计划的目标是改进液-质联用(LC-MS)全球代谢组学中的样品制备方法，主要重点是在不牺牲高样品吞吐量和分析速度的情况下，提高低丰度代谢物的检测和鉴定。特别是，我们计划研究新的萃取材料、纳米颗粒和离子液体的使用，以确定最有用的新材料，以设计基于磁分离平台的顺序萃取/分馏方案。我们还将设计新的系统方案来评估给定样本中代谢物的生物利用度，以确定游离代谢物和总代谢物浓度，并评估这种新方法是否可以改进生物标志物的搜索。该研究计划将回答当前代谢组学文献中未涉及的重要问题，包括：(I)如何在保持分析的全球性的同时提高数据质量；(Ii)可定制纳米颗粒和离子液体等新兴材料是否可以改善传统方法的代谢物覆盖面；(Iii)如何确定最具互补性的样品制备正交法，并开发适用于即使在样本有限的情况下也适用于代谢组深度挖掘的标准化顺序提取方案；以及(Iv)代谢物的生物可用性是否可以改善生物标记物的发现和数据解释过程。该项目开发的新的分析方法将对改善靶向和非靶向代谢组学研究发挥关键作用，并促进这一新领域的更快进展和提取方法的合理设计。

The main objective of the emerging field of metabolomics is the analysis of all small molecule metabolites present in a particular biological system in order to provide better understanding of dynamic processes occurring in living systems. However, the analytical approaches for the comprehensive study of hundreds or thousands of metabolites in a single analysis are currently underdeveloped, better-suited for medium to high-abundance stable metabolites and lack adequate validation procedures. The objective of the proposed research program is to improve sample preparation methods in global metabolomics by liquid chromatography - mass spectrometry (LC-MS) with the primary focus of improving the detection and identification of low abundance metabolites without sacrificing high sample throughput and speed of analysis. In particular, we plan to examine the use of new extraction materials, nanoparticles and ionic liquids to identify the most useful new materials for the design of a sequential extraction/fractionation protocol based on magnetic separation platform. We will also devise new systematic protocol to evaluate the biological availability of metabolites in a given sample to determine both free and total metabolite concentrations, and evaluate whether this novel approach can improve the search for biomarkers. The research program will answer important questions not addressed in current metabolomics literature including (i) how to improve data quality while maintaining the global nature of analysis (ii) whether emerging materials such as customizable nanoparticles and ionic liquids can improve metabolite coverage over classical approaches (iii) how to identify the most complementary orthogonal methods for sample preparation and develop standardized sequential extraction protocols suitable for deep mining of the metabolome even in sample-limited situations and (iv) whether biological availability of metabolite can improve the processes of biomarker discovery and data interpretation. The new analytical methods developed in this program will play a critical role to improve both targeted and untargeted metabolomics studies, and facilitate faster progress and rational design of extraction methods in this new field.