National Center for Quantitative Biology of Complex Systems

项目概要- TR&D 3 MS是一种用于测量蛋白质组、代谢组和脂质组的流行且强大的技术。中的每 这些类别，MS分析是非常相似的，包括色谱分离，质量 尽管有这种共性，但几乎所有的MS实验室都专门从事质谱分析。 一个人，排斥其他人。我们描述的集成工作流程将通过以下方式克服这一限制： 提供能够分离和表征复杂混合物的色谱和MS技术 含有肽、代谢物和脂质。我们设想这将在几个小时的分析时间内发生， 全面覆盖各大经济体。在这里，我们通过消除现有的 蛋白质组学和脂质组学技术的缺点，并开始在综合多组学平台上开展工作 它允许同时分析肽、脂质和代谢物。首先，我们和其他人的工作 表明，最新的高MS/MS采样率，主要限制，防止更深的单- 蛋白质组分析是肽分离。第二，我们已经确定了两个主要障碍， 脂质组学分析：（1）同分异构体和同量异序脂质种类的分离较差，它们通常共混， 无法确定独特脂质种类的数量及其数量。(2)MS/MS带宽有限， 通常被源内片段、加合物和二聚体的冗余采样所消耗。目前，大多数 多组学研究将生物样本分开，以便每个组学分析单独进行-从样本 准备数据分析-由不同的人用不同的MS系统。这种分裂导致 大量的动手样品制备劳动和仪器采集时间。样品间 异质性、独立样品处理和仪器间方差都产生噪声， 从而掩盖了原本强的生物分子缔合。

PROJECT SUMMARY – TR&D 3 MS is a popular and powerful technique for measuring proteomes, metabolomes, and lipidomes. For each of these classes, the MS analysis is remarkably similar, consisting of chromatographic separation, mass measurement, and tandem MS. Despite this commonality, nearly all MS laboratories specialize in analysis of a single ome, at the exclusion of the others. The integrated workflow we describe will overcome this limitation by providing a chromatography and MS technology capable of separating and characterizing a complex mixture containing peptides, metabolites, and lipids. We envision this to happen in a few hours of analysis time and to provide comprehensive coverage across omes. Here we lay the foundation for this work by eliminating existing shortcomings in proteomics and lipidomic technologies and commence work on an integrated multi-omic platform that allows for simultaneous analysis of peptides, lipids, and metabolites. First, work by us and others has demonstrated that with the latest high MS/MS sampling rates, the primary limitation preventing deeper single- shot proteome analysis is peptide separations. Second, we have identified two primary obstacles limiting lipidomic analyses: (1) Poor separation of isomeric and isobaric lipid species, which often co-elute such that the number of unique lipid species and their quantities cannot be determined. (2) MS/MS bandwidth is limited and often consumed by redundant sampling of in-source fragments, adducts, and dimers. Finally, presently, most multi-omic studies split biological samples so that each omic analysis is conducted separately – from sample preparation to data analysis – by different people with different MS systems. This fragmentation results in substantial hands-on sample preparation labor and instrument acquisition time. Sample-to-sample heterogeneity, independent sample handling, and instrument-to-instrument variance all contribute noise, thereby obscuring otherwise strong biomolecular associations.