A universal multiplexing approach to unlock the hidden proteome

摘要 基于质谱学(MS)的蛋白质组学在过去几年中取得了显著的进展，现在 能够在一次分析中检测8,000种蛋白质，或通过广泛的分离检测蛋白质 现在可以实现接近转录组水平的水平。尽管取得了这些进展，但重大的偏见 存在于由MS常规检测到的蛋白质组中，这是因为几乎完全使用胰酶来分解蛋白质 样品制备过程中的卵裂。胰酶是一种非常适合蛋白质组学的蛋白水解酶，因为它能产生多肽 然而，由于具有用于MS分析的有利的化学成分，它也锁定了相当一部分 多肽中的蛋白质组太小或太大而不能被MS检测-从而使这些片段 到目前为止，在进行的蛋白质组学实验中，99%的蛋白质组实际上是看不见的。只有少数几个 全球蛋白质组学实验已经报道了替代蛋白水解酶的使用，主要是由于通常 胰酶的卓越性能，以及分析额外样品所需的仪器时间增加。a 对大多数实验室来说都是限制因素。最近开发的MS方法，特别是独立于数据的获取 (Dia)，在新的实验和计算模式下运行，这些模式依赖于高度卷积的 复杂的MS谱，并与用于检测的多肽或光谱数据库匹配。这一新的范式呈现出 有机会在单个MS中对由各种不同的蛋白酶产生的蛋白质组样本进行多重分析 分析。然而，到目前为止，DIA分析是专门针对胰蛋白酶多肽开发的。在这里，我们建议 一种新颖的DIA获取和计算分析方法，用于复合多个蛋白酶和解锁 隐藏的蛋白质组。为了实现这项建议的目标，我们将首先优化非胰蛋白酶的DIA， 然后将这些优化条件应用于混合了不同蛋白酶的DIA多路传输环境中。 最后，我们将进一步发展和应用这一框架来分析翻译后修饰(例如： 磷酸化)，其中增加蛋白质组覆盖率对于修饰、检测和定位是必不可少的。 这项工作的成功完成将提供一个强大的框架来显著增加蛋白质组 在MS分析中常规检测和定量。重要的是，这个工作流程的所有细节，从样本处理 到用于数据分析的软件，将以逐步在线协议的形式很好地记录下来，并免费分发到 社区使这一方法能够快速整合到现代蛋白质组学工作流程中。

SUMMARY Mass spectrometry (MS) based proteomics has made remarkable advances over the past several years, now enabling the detection of 8,000 proteins in a single analysis, or with extensive fractionation, protein detection levels approaching that of transcriptomics can now be achieved. Despite such advances, a significant bias exists in the proteome routinely detected by MS due to the near exclusive use of trypsin for proteolytic cleavage during sample preparation. Trypsin is a well­suited protease for proteomics, as it produces peptides with favorable chemical composition for MS analysis, however, it also locks a considerable fraction of the proteome in peptides either too small or too large for MS detection ­ thus rendering these segments of the proteome effectively invisible in >99% of the proteomics experiments performed to date. Only a handful of global proteomics experiments have reported the use of alternative proteases, primarily due to the generally superior performance of trypsin, and the increased instrument time required to analyze additional samples ­ a limiting factor for most labs. Recently developed MS approaches, specifically data­independent acquisition (DIA), operate under new experimental and computational paradigms which rely on deconvolution of highly complex MS spectra and matching to peptide or spectral databases for detection. This new paradigm presents the opportunity to multiplex proteomic samples generated from a variety of different proteases in a single MS analysis. However, to date DIA analysis has been exclusively developed for tryptic peptides. Here we propose an innovative DIA acquisition and computational analysis approach to multiplex multiple proteases and unlock the hidden proteome. To achieve the goals of this proposal we will first optimize DIA for non­trypsin proteases, and then apply these optimized conditions in a DIA multiplexed setting with a mixture of different proteases. Lastly, we will further develop and apply this framework to the analysis of post­translational modifications (e.g. phosphorylation) where increased proteome coverage is essential for modification detection and localization. Successful completion of this work will provide a robust framework to dramatically increase the proteome routinely detected and quantified in MS analysis. Importantly, all details of this workflow, from sample handling to software for data analysis, will be well documented in step­by­step online protocols and freely distributed to the community to enable rapid integration of this approach into modern proteomics workflows.