北京生物医学工程

基于数据非依赖采集的无色谱数据处理软件系统

A software system for mass spectrometry data processing based on data-independent acquisition without chromatogram

作者：张振航李青润陈冲曾嵘郑浩然

单位：1 中国科学技术大学计算机科学与技术学院(合肥230027) 2 中国科学院上海生命科学研究院（上海200031）

分类号：R318.04

出版年·卷·期（页码）：2020·39·1（42-47）

摘要：

目的如何高效准确地定量蛋白质一直是蛋白质组学的主要关注点，基于液相色谱-数据依赖模式进行谱图采集的质谱方法是目前主流的蛋白质测定方式。但是，当面对复杂样本中蛋白质定量的对比实验，为了使肽段得到有效分离，使用较长时间色谱洗脱的方法占据了谱图生成的大量时间。为了解决此问题，并且能够高效、准确地定性定量肽段，提出一个基于数据非依赖采集（data-independent acquisition, DIA）的无色普数据处理软件系统。方法基于以肽段为中心的蛋白质定量理念，利用现有解决混合图谱的方法对无色谱DIA质谱数据进行定性，随后仿照DIA方法下色谱面积的计算方法完成定量；最后基于分类模型，对最终结果给出统计分析控制。结果本系统能够处理生成无色谱的DIA质谱数据，并且在12 min内从海拉（Henrietta Lacks，Hela）蛋白质样本中定性定量出1954个肽段。结论使用本系统处理无色谱质谱数据，相比于DIA质谱数据，能够在更短的时间内准确定量出足够的肽段，对于在有限时间内测定大规模蛋白质样本有重要的意义。

Objective How to quantify protein efficiently and accurately has always been the focus of proteomics.Liquid chromatography-tandem mass spectrometry is currently the most popular method for identification and quantification of proteins. However, when comparing large-scale samples in protein identification and quantification, multiple mass spectra need be generated, and chromatographic elution would occupy a large amount of time to generate spectra. In order to efficiently and accurately solve this problem, we therefore propose a software system to resolve data-independent acquisition (DIA) mass spectrometry data without chromatographic elution. Methods The method is based on the peptide-centric protein quantification concept. The mass spectrometry data without chromatography is identified by the existing method of solving the mixed spectrum, and then the quantification calculation is performed according to the calculation method of the chromatographic area accomplished by the DIA model. Finally, according to the classification method, we controll the final results by using statistical analysis. Results The system is able to process DIA mass spectrometry data without chromatography and And 1954 peptides is identifiably and quantitatively extracted from the protein samples of Henrietta Lakes (Hela) in 12 minutes. Conclusions Using our system to process non-chromatographic mass spectrometry data is comparable to DIA mass spectrometry data. The system can accurately quantify enough peptides in a short period, which is important for quantify large-scale proteomic samples within an acceptable time spending.

参考文献：

[1] Aebersold R, Mann M. Mass spectrometry-based proteomics[J]. Nature, 2003, 422(6928): 198-207.

[2] de Godoy LMF, Olsen JV, Cox J, et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast[J]. Nature, 2008, 455(7217): 1251-1254.

[3] Meissner F, Mann M. Quantitative shotgun proteomics: considerations for a high-quality workflow in immunology[J]. Nature Immunology, 2014, 15(2): 112-117.

[4] Purvine S, Eppel* JT, Yi EC, et al. Shotgun collision‐induced dissociation of peptides using a time of flight mass analyzer[J]. Proteomics, 2003, 3(6): 847-850.

[5] Houel S, Abernathy R, Renganathan K, et al. Quantifying the impact of chimera MS/MS spectra on peptide identification in large-scale proteomics studies[J]. Journal of Proteome research, 2010, 9(8): 4152-4160.

[6] Wang J, Pérez-Santiago J, Katz J E, et al. Peptide identification from mixture tandem mass spectra[J]. Molecular & Cellular Proteomics, 2010, 9(7): 1476-1485..

[7] Wang J, Tucholska M, Knight JDR, et al. MSPLIT-DIA: sensitive peptide identification for data-independent acquisition[J]. Nature Methods, 2015, 12(12): 1106-1108.

[8] Peckner R, Myers SA, Jacome ASV, et al. Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics[J]. Nature Methods, 2018, 15(5): 371-378.

[9] Reiter L, Rinner O, Picotti P, et al. mProphet: automated data processing and statistical validation for large-scale SRM experiments[J]. Nature Methods, 2011, 8(5): 430-435.

[10] Elias JE, Gygi SP. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry[J]. Nature methods, 2007, 4(3): 207-214.

[11] Ting YS, Egertson JD, Payne SH, et al. Peptide-centric proteome analysis: an alternative strategy for the analysis of tandem mass spectrometry data[J]. Molecular & Cellular Proteomics, 2015, 14(9): 2301-2307.

[12] MacLean B, Tomazela DM, Shulman N, et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments[J]. Bioinformatics, 2010, 26(7): 966-968.

[13] R?st HL, Rosenberger G, Navarro P, et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data[J]. Nature Biotechnology, 2014, 32(3): 219-223.

Bruderer R, Bernhardt O M, Gandhi T, et al. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues[J]. Molecular & Cellular Proteomics, 2015, 14(5): 1400-1410.

服务与反馈：

【文章下载】【加入收藏】

提示：您还未登录，请登录！点此登录