Development of Mass Spectrometric Techniques for Comprehensive Metabolome Analysis in Metabolomics

代谢组学中综合代谢组分析的质谱技术的发展

• 批准号: RGPIN-2014-05456
• 负责人: Li, Liang
• 金额: $ 4.95万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=611657
• 关键词: Development; Mass; Spectrometric; Techniques; Comprehensive

My research program focuses on developing new mass spectrometric techniques for proteomics and metabolomics. Compared to proteomics, metabolomics is a relatively new field, but has the potential to produce a wealthy array of information, often complementing proteomics, in studying systems biology and searching for disease biomarkers. Metabolomics involves the study of all the small molecule metabolites (the metabolome) present in a biological sample. Small molecules are important cellular components that can be used as disease biomarkers, as phenotype indicators and as molecular targets in functional genomics research. While the number of publications related to metabolomics has increased in recent years, this field still faces a number of analytical challenges, particularly in the area of generating comprehensive and quantitative metabolome profiles; current analytical tools can detect and quantify only a small fraction of the metabolome. Thus the objective of our proposed research is to develop new mass spectrometric techniques to address some of these analytical challenges. To achieve our goal, we will develop an integrated analytical platform for quantitative metabolome profiling with high metabolome coverage, high detection specificity and high throughput. The workflow of this platform will involve several steps, including preprocessing of metabolomic samples, metabolite extraction, isotope labeling of metabolites, liquid chromatography (LC) separation of the labeled metabolites combined with mass spectrometry (MS) and tandem MS (MS/MS) for metabolite quantification and identification, and data analysis. We will develop new methods to improve the analytical performance of each step in this workflow. Specifically, we will develop (1) improved techniques to process the metabolome samples with efficient fractionation of chemical groups, (2) new and improved isotope labeling chemistries to target different groups of metabolites or sub-metabolomes in order to increase the overall metabolome coverage, (3) multidimensional separation tools to reduce metabolome sample complexity for improved MS analysis, (4) new and improved MS and MS/MS strategies to increase detection sensitivity and specificity, (5) an expanded metabolome library including experimental and predicted MS/MS spectra, and (6) a web-based public resource for compound identification. During the development stage, we will apply each developing technique to real world samples to gauge its analytical performance and illustrate the practical utility of the method. We will also investigate the physical and chemical processes underlying the operation of each method so as to further optimize its analytical performance. In the end, we will integrate these methods to develop an analytical platform that will enable the generation of comprehensive and quantitative metabolome profiles of biological samples with high efficiency and specificity. The novelty and expected significance of this research lies in the development of a new MS-based analytical technology that can be used for large scale studies of biological systems, such as discovery of specific biomarkers for diagnosis or prognosis of diseases, monitoring the health status of individuals leading to better life style and early detection of disorders related to physical health and psychological well-being, and applying systems biology to investigate and understand the functions of a biosystem holistically. As we anticipate that metabolomics will play an increasingly important role in many areas in the future, this research program provides great opportunities for training a number of highly skilled and highly sought-after analytical chemists.

我的研究项目主要集中在蛋白质组学和代谢组学的新质谱技术的开发。与蛋白质组学相比，代谢组学是一个相对较新的领域，但在研究系统生物学和寻找疾病生物标志物方面，代谢组学有可能产生丰富的信息，通常是蛋白质组学的补充。代谢组学涉及研究生物样品中存在的所有小分子代谢物（代谢物组）。小分子是重要的细胞组分，可用作疾病生物标志物、表型指示物和功能基因组学研究中的分子靶点。虽然近年来与代谢组学相关的出版物数量有所增加，但这一领域仍然面临着许多分析挑战，特别是在生成全面和定量代谢组学特征方面;目前的分析工具只能检测和定量代谢组学的一小部分。因此，我们提出的研究目标是开发新的质谱技术，以解决这些分析挑战。 为了实现我们的目标，我们将开发一个集成的分析平台，用于定量代谢组分析，具有高代谢组覆盖率，高检测特异性和高通量。该平台的工作流程将涉及几个步骤，包括代谢组学样品的预处理、代谢物提取、代谢物的同位素标记、标记代谢物的液相色谱（LC）分离结合质谱（MS）和串联MS（MS/MS）进行代谢物定量和鉴定以及数据分析。我们将开发新的方法来提高该工作流程中每个步骤的分析性能。具体而言，我们将开发（1）改进的技术，以处理代谢组样品，有效分离化学基团，（2）新的和改进的同位素标记化学物质，以靶向不同的代谢物组或亚代谢组，以增加总体代谢组覆盖率，（3）多维分离工具，以降低代谢组样品的复杂性，以改进MS分析，（4）新的和改进的MS和MS/MS策略，以提高检测灵敏度和特异性，（5）扩展的代谢组库，包括实验和预测的MS/MS光谱，和（6）基于网络的公共资源，用于化合物鉴定。在开发阶段，我们将应用每一个开发技术的真实的世界的样品，以衡量其分析性能，并说明该方法的实际效用。我们还将研究每种方法操作的物理和化学过程，以进一步优化其分析性能。最后，我们将整合这些方法来开发一个分析平台，该平台将能够以高效率和特异性生成生物样品的全面和定量代谢组谱。 这项研究的新奇和预期意义在于开发了一种新的基于MS的分析技术，该技术可用于生物系统的大规模研究，例如发现用于疾病诊断或预后的特定生物标志物，监测个体的健康状况，从而改善生活方式，并早期检测与身体健康和心理健康相关的疾病，应用系统生物学从整体上研究和理解生物系统的功能。由于我们预计代谢组学将在未来的许多领域发挥越来越重要的作用，该研究计划为培养一些高技能和备受追捧的分析化学家提供了很好的机会。