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.

概括 基于质谱（MS）的蛋白质组学在过去几年中取得了显着进步，现在 在单个分析中或以广泛的分馏，启用8,000蛋白的检测，蛋白质检测 现在可以达到接近转录组学的水平。尽管有这样的进步，但很大的偏见 由于近乎独家使用胰蛋白酶对蛋白水解，MS常规检测到的蛋白质组存在于蛋白质组中 样品制备过程中的裂解。胰蛋白酶是蛋白质组学的能蛋白酶，因为它会产生肽 但是，具有有利的化学成分用于MS分析，它还锁定了一定数的考虑 Petides中的蛋白质组太小或太大而无法检测到MS检测 迄今为止，蛋白质组学实验的> 99％的蛋白质组有效地看不见。只有少数 全球蛋白质组学实验报告了替代蛋白酶的使用，主要是由于通常是由于 胰蛋白酶的出色性能以及分析其他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，特别是数据依赖性收购 （DIA），在新的实验和计算范式下运行，这些范式依赖于高度的反卷积 复杂的MS光谱并与肽或光谱数据库匹配以进行检测。这个新的范式礼物 单个MS中各种不同蛋白酶产生的多种蛋白质组学样品的机会 分析。但是，迄今为止，DIA分析是针对胰蛋白酶Petides开发的。我们在这里提出 一种创新的DIA获取和计算分析方法，用于多种多种蛋白酶并解锁 隐藏的protectome。为了实现该建议的目标，我们将首先优化非tryprypsin蛋白的DIA， 然后将这些优化的条件在DIA多路复用的设置中使用不同的蛋白酶的混合物。 最后，我们将进一步开发并将此框架应用于翻译后修饰的分析（例如， 磷酸化）其中增加蛋白质组覆盖范围对于修饰检测和定位至关重要。 成功完成这项工作将提供一个强大的框架，以显着增加蛋白质组 在MS分析中常规检测和量化。重要的是，该工作流程的所有细节，都可以从样本处理 对于数据分析的软件，将在StepbyStep在线协议中得到充分记录，并自由分发到 社区能够将这种方法快速整合到现代蛋白质组学工作流程中。

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

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

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.