A universal multiplexing approach to unlock the hidden proteome

解锁隐藏蛋白质组的通用多重方法

• 批准号: 10478967
• 负责人: Danielle L Swaney
• 金额: $ 33.08万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10478967
• 关键词: Address; Affect; Amino Acids; Biological; Biological Models; Biology; Cells; Chemicals; Communities; Complex; Computer Analysis; Computer software; Consensus; Data; Data Analyses; Databases; Detection; Enzymes; Fractionation; Future; Goals; Human; Human Biology; Libraries; Mass Spectrum Analysis; Modernization; Modification; Outcome; Peptide Hydrolases; Peptides; Performance; Phosphorylation; Phosphorylation Site; Post Translational Modification Analysis; Post-Translational Modification Site; Preparation; Property; Protein Region; Proteins; Proteome; Proteomics; Protocols documentation; Reporting; Reproducibility; Resolution; Sampling; Signal Transduction; Site; Technology; Testing; Time; Trypsin; Work; Yeasts; base; combinatorial; computerized tools; data acquisition; experimental study; human disease; innovation; instrument; metaproteomics; microbiome; novel; novel strategies; phosphoproteomics; transcriptomics

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.

总结 基于质谱的蛋白质组学在过去的几年里取得了显著的进展， 能够在一次分析中检测8，000种蛋白质，或者通过广泛的分级分离， 现在可以达到接近转录组学的水平。尽管取得了这些进展， 存在于通过MS常规检测的蛋白质组中，这是由于几乎专门使用胰蛋白酶进行蛋白水解 样品制备过程中的裂解。胰蛋白酶是一种非常适合蛋白质组学的蛋白酶，因为它产生肽 然而，由于具有用于MS分析的有利的化学成分，它也锁定了相当大一部分的 肽中的蛋白质组对于MS检测来说太小或太大，从而使得这些片段的蛋白质组的蛋白质组片段的 在迄今为止进行的>99%的蛋白质组学实验中，蛋白质组有效地不可见。只有少数 全球蛋白质组学实验已经报道了替代蛋白酶的使用，这主要是由于蛋白酶通常 胰蛋白酶的上级性能，以及分析额外样品所需的仪器时间增加， 大多数实验室的限制因素最近开发的MS方法，特别是数据采集独立采集 (DIA)，在新的实验和计算范例下操作，这些范例依赖于高度非线性的去卷积。 复杂的MS光谱和匹配肽或光谱数据库进行检测。这种新的模式提出了 在单个MS中从多种不同蛋白酶产生的多重蛋白质组样品的机会 分析.然而，迄今为止，DIA分析仅针对胰蛋白酶肽开发。在这里我们建议 一种创新的DIA采集和计算分析方法，用于多路复用多种蛋白酶， 隐藏的蛋白质组为了实现该提议的目标，我们将首先优化非胰蛋白酶的DIA， 然后将这些优化的条件应用于具有不同蛋白酶的混合物的DIA多重设置中。 最后，我们将进一步发展和应用这个框架来分析翻译后修饰（例如， 磷酸化），其中增加的蛋白质组覆盖对于修饰检测和定位是必要的。 这项工作的成功完成将提供一个强大的框架，大大增加蛋白质组 在MS分析中常规检测和定量。重要的是，该工作流程的所有细节，从样品处理 到数据分析软件，将被详细记录在一步一步的在线协议中，并免费分发给 社区，使这种方法快速集成到现代蛋白质组学工作流程。

项目成果

Recurrent Co-Option and Recombination of Cytokine and Three Finger Proteins in Multiple Reproductive Tissues Throughout Salamander Evolution.

• DOI: 10.3389/fcell.2022.828947
• 发表时间: 2022
• 期刊: Frontiers in cell and developmental biology
• 影响因子: 5.5
• 作者: Wilburn DB;Kunkel CL;Feldhoff RC;Feldhoff PW;Searle BC
• 通讯作者: Searle BC

Optimizing Linear Ion-Trap Data-Independent Acquisition toward Single-Cell Proteomics.

• DOI: 10.1021/acs.analchem.3c00842
• 发表时间: 2023-06
• 期刊: Analytical chemistry
• 影响因子: 7.4
• 作者: Teeradon Phlairaharn;Zilu Ye;Elena Krismer;A. Pedersen;M. Pietzner;J. Olsen;E. Schoof;B. Searle

CIDer: A Statistical Framework for Interpreting Differences in CID and HCD Fragmentation.

• DOI: 10.1021/acs.jproteome.0c00964
• 发表时间: 2021-04-02
• 期刊: Journal of proteome research
• 影响因子: 4.4
• 作者: Wilburn DB;Richards AL;Swaney DL;Searle BC
• 通讯作者: Searle BC

Data-Independent Acquisition Protease-Multiplexing Enables Increased Proteome Sequence Coverage Across Multiple Fragmentation Modes.

• DOI: 10.1021/acs.jproteome.1c00960
• 发表时间: 2022-04-01
• 期刊: JOURNAL OF PROTEOME RESEARCH
• 影响因子: 4.4
• 作者: Richards, Alicia L.;Chen, Kuei-Ho;Wilburn, Damien B.;Stevenson, Erica;Polacco, Benjamin J.;Searle, Brian C.;Swaney, Danielle L.
• 通讯作者: Swaney, Danielle L.

A Multipathway Phosphopeptide Standard for Rapid Phosphoproteomics Assay Development.

• DOI: 10.1016/j.mcpro.2023.100639
• 发表时间: 2023-10
• 期刊: MOLECULAR & CELLULAR PROTEOMICS
• 影响因子: 7
• 作者: Searle, Brian C.;Chien, Allis;Koller, Antonius;Hawke, David;Herren, Anthony W.;Kim, Jenny Kim;Lee, Kimberly A.;Leib, Ryan D.;Nelson, Alissa J.;Patel, Purvi;Ren, Jian Min;Stemmer, Paul M.;Zhu, Yiying;Neely, Benjamin A.;Patel, Bhavin
• 通讯作者: Patel, Bhavin