选择反应监测质谱法进行绝对定量蛋白质
Summary
This protocol describes how to perform absolute quantification assays of target proteins within complex biological samples using selected reaction monitoring. It was used to accurately quantify proteins of the mouse macrophage chemotaxis signaling pathway. Target peptide selection, assay development, and qualitative and quantitative assays are described in detail.
Abstract
Absolute quantification of target proteins within complex biological samples is critical to a wide range of research and clinical applications. This protocol provides step-by-step instructions for the development and application of quantitative assays using selected reaction monitoring (SRM) mass spectrometry (MS). First, likely quantotypic target peptides are identified based on numerous criteria. This includes identifying proteotypic peptides, avoiding sites of posttranslational modification, and analyzing the uniqueness of the target peptide to the target protein. Next, crude external peptide standards are synthesized and used to develop SRM assays, and the resulting assays are used to perform qualitative analyses of the biological samples. Finally, purified, quantified, heavy isotope labeled internal peptide standards are prepared and used to perform isotope dilution series SRM assays. Analysis of all of the resulting MS data is presented. This protocol was used to accurately assay the absolute abundance of proteins of the chemotaxis signaling pathway within RAW 264.7 cells (a mouse monocyte/macrophage cell line). The quantification of Gi2 (a heterotrimeric G-protein α-subunit) is described in detail.
Introduction
使用质谱（MS）蛋白质组实验可以设计为使用非靶向（散弹枪）或有针对性的方法。发现蛋白质组学通常依赖于自下而上猎枪质谱，或者通过使用一个传统的数据依赖性获取模式，或者通过使用最近开发的与数据无关的技术之一（ 例如 ，MS E，小水）1,2-。鸟枪蛋白质组学是一个功能强大的工具，高通量肽鉴定和相对定量，但它通常是不适合的绝对定量或用于靶向蛋白质的小，定义集（〜数万）。最常用于目标蛋白质组的MS法被选择，因为它的高灵敏度，速度和动态范围3-5的反应监测（SRM）。替代品SRM包括平行反应监测，这需要高分辨率，全MS扫描6的优势。
SRM是使用纳米流逆向工程＆通常进行SED相高效液相色谱法（纳米RP-LC）仪器耦合到纳米电喷雾附着到三重四极质谱仪（QQQ-MS）（纳米ESI）离子源。在典型的实验中，样品的蛋白质被蛋白水解消化，所得的肽色谱分离，解吸和电离。得到的前体离子M / Z过滤由所述第一四极（Q1）和零散的第二四极（Q2）通过与碰撞气体碰撞它们。所得的片段离子是M / Z-过滤在第三四极（Q3）中，并由倍增电极定量。每种前体离子和碎片离子对被称为过渡，并且每个过渡监视在指定的时间段（停留时间;通常2-50毫秒）。在LC-SRM中，通过转换的预定义列表中QQQ-MS周期（占空比是通常≤3秒），并且每个过渡的色谱产生。
替代战略中独立实体为蛋白质定量通常使用的免疫测定，例如斑点印迹，Western印迹，酶联免疫吸附，抗体微阵列，反相蛋白质微阵列，微流体免疫测定，数字的ELISA，以及基于微球的免疫7。最好的免疫测定可以是显著比LC-SRM更加敏感，并且可以通过免疫测定的样品可以是比LC-SRM 5显著更高。然而，开发免疫测定可以是昂贵的和/或费时的，产生的测定法可以是易受交叉反应性和/或干扰，不符合细胞/组织裂解/均匀化的方法，和/或不适合于复用5,8。其中的一些问题可以通过偶联抗体和基于MS的技术来解决。例如，靶蛋白可以通过免疫前蛋白水解和LC-SRM 9-12富集。可替代地，SISCAPA技术采用对蛋白水解随后免疫沉淀在肽LEVE升13,14。除了immunoenrichment策略，高丰度蛋白的免疫耗竭可用于增加LC-SRM灵敏度通过减少干扰由共洗脱被分析物15,16。
基于MS的蛋白质定量可以分为相对和绝对定量，并且也为无标记和稳定同位素标记（ 例如 ，代谢标记，化学标记，以及重标记的蛋白质和肽内标准）。无标记技术可以为相对蛋白定量是有用的，但不适合于精确的绝对定量。通过比较，标记技术具有减少与样品制备和MS方差相关的错误，并且经常被用于相对蛋白定量17。例如，稳定同位素标记蛋白质组（SILAP）标准允许使用潜在生物标志物的相对定量通过人血清18 LC-SRM一个培养的人类细胞制备。精确的绝对蛋白定量由MS需要纯化的，量化的，同位素标记的蛋白质或肽内标准被掺入-成在MS之前的生物样品。重同位素标记的内标准纳入的LC-SRM工作流使已被证明是高度可再现和可转让的实验室16,19之间的绝对定量。
稳定同位素标记的内部标准进行绝对蛋白定量经MS包括肽标准，使用固相合成20，蛋白级联的蛋白酶可切割的肽标准21组成，和全长蛋白质标准22制备。靶蛋白共价修饰和不完整的样品制备（ 即不完整的样品裂解和均质化，和不完全蛋白质溶解，变性，烷基化，蛋白水解和）可以破坏精确的定量。内部protein标准是最不容易受到大多数这些潜在的问题，但是它们通常是最难以制备。另一种方法是分析使用被设计为包括氨基和羧基末端天然侧翼残基的多个内部肽标准每个目标蛋白质。无论哪个内标类型采用的，但是应当掺入-到生物样品在早一个点的样品制备尽可能期间。此外，多个样品制备技术（ 例如 ，不同的变性条件）进行测试。多个正交实验技术（实验交叉验证）的使用是一个可行的战略，以克服最有潜力的量化挑战23-25。
蛋白的LC-SRM量化是一个高度灵活的技术，已在各种各样的应用中被使用。值得注意的是，它已被用于在研究的肽和蛋白质生物标志物临床样品如血清，核心活检，以及细针抽吸5。 LC-SRM也已用于测量蛋白质复合物5,26的化学计量，以检测肉毒神经毒素27，向 信号通路5内量化蛋白磷酸化动力学，并量化改变蛋白质构象28。
我们的实验室使用LC-SRM量化介导的巨噬细胞的趋化，以支持趋途径模拟发展的信号蛋白。该协议的总体方案（ 图1）开始排名暂定目标肽。随后，粗外部肽标准合成并用于开发的LC-SRM测定生物样品的定性分析。如果来源于生物样品的靶肽进行检测，纯化的重标记的内部肽标准进行定量的LC-SRM制备。该协议可以用于交流curately从各种各样的生物样品的定量蛋白质，并支持多种蛋白质目标调查。
Protocol
Representative Results
Discussion
绝对蛋白定量是生物医学应用，如生物标志物验证和信号转导通路造型非常多元化至关重要。近日，采用LC-SRM针对性的蛋白质组学得益于改进众多技术，包括肽标准配制，HPLC，QQQ-MS和LC-SRM数据分析。因此，它已成为一个强大的替代免疫。免疫测定可以是非常敏感的，高通量的，但开发强大的免疫测定可以是非常具有挑战性的，因为免疫测定可以是易受交叉反应性和/或干扰，不符合细胞/组织裂解/…
Disclosures
The authors have nothing to disclose.
Acknowledgements
This research was supported by the Intramural Research Program of the NIH, National Institute of Allergy and Infectious Diseases.
Materials
| Acetonitrile (ACN), LC-MS grade | Fisher | A955-1 | |
| BCA (bicinchoninic acid) protein assay kit | Fisher | 23235 | |
| Beads for bead beating, zirconia-silica, 0.1mm | BioSpec Products | 11079101z | |
| Bestatin hydrochloride | Sigma | B8385-10MG | |
| Cell culture DMEM (with glucose, without L-glutamine) | Lonza | 12-614F | |
| Cell culture EDTA, 500mM, pH8 | Gibco | 15575 | |
| Cell culture fetal bovine serum (FBS) | Atlanta Biologicals | S11550 | |
| Cell culture L-glutamine | Sigma | G8540-25G | |
| Cell culture phosphate buffered saline (PBS) pH 7.4 | Gibco | 10010-049 | |
| Cell culture Trypan Blue viability stain, 0.4% w/v | Lonza | 17-942E | |
| Cellometer Auto T4 cell counter | Nexcelom Bioscience | Cellometer Auto T4 | |
| Cellometer Auto T4 disposable counting chambers | Nexcelom Bioscience | CHT4-SD100-014 | |
| Dithiothreitol (DTT) | Sigma | D5545-5G | |
| Formic acid, LC-MS grade, ampules | Fisher | A117-10X1AMP | |
| Hemocytometer, Neubauer-improved, 0.1mm deep | Marienfeld-Superior | 0640030 | |
| HEPES, 1M, pH 7.2 | Mediatech | 25-060-CI | |
| Hydrochloric acid, 37% w/w | VWR | BDH3028-2.5LG | |
| Iodoacetamide | Sigma | I1149-5G | |
| Laser Based Micropipette Puller | Sutter Instrument Co. | P-2000 | |
| LC Magic C18AQ, 5µm, 200Å, loose media | Michrom Bioresources | PM5/61200/00 | |
| LC Halo ES-C18, 2.7µm, 160Å, loose media | Michrom Bioresources | PM3/93100/00 | |
| LC coated silica capillary, 50µm id | Polymicro Technologies | 1068150017 | |
| LC vial, autosampler, 12x32mm polypropylene | SUN SRI | 200-268 | |
| LC vial screw cap, autosampler, pre-slit PTFE/silicone | SUN SRI | 500-061 | |
| Luciferase, from Photinus pyralis | Sigma | L9506-1MG | |
| Pepstatin A | EMD Millipore | 516481-25MG | |
| pH strips colorpHast (pH 0.0-6.0) | EMD Chemicals | 9586-1 | |
| PhosStop phosphatase inhibitor cocktail | Roche | 04906837001 | |
| RapiGest SF | Waters | 186001861 | |
| Sep-Pak SPE, C18 1ml 100mg cartridge | Waters | WAT023590 | |
| Sep-Pak SPE, extraction manifold, 20 position | Waters | WAT200609 | |
| Sep-Pak SPE, flat-surfaced rubber bulb | Fisher | 03-448-25 | |
| Sodium hydroxide (NaOH) | Fisher | S318-500 | |
| SpeedVac vacuum concentrator | Fisher | SPD111V | |
| Trifluoroacetic acid (TFA), LC-MS grade | Fisher | A116-50 | |
| Trypsin, sequencing grade, modified | Promega | V5113 | |
| Tube decapper for Micronic tubes | USA Scientific | 1765-4000 | |
| Tubes, 2ml microcentrifuge, o-ring screw-cap, sterile | Sarstedt | 72.694.006 | |
| Urea | Sigma | U0631-500g | |
| Water, LC-MS grade | Fisher | W6-1 |
References
- Cox, J., Mann, M. Quantitative high-resolution proteomics for data-driven systems biology. Annu Rev Biochem. 80, 273-299 (2011).
- Zhang, Y., Fonslow, B. R., Shan, B., Baek, M. C., Yates, J. R. Protein analysis by shotgun/bottom-up proteomics. Chem Rev. 113, 2343-2394 (2013).
- Boja, E. S., Rodriguez, H. Mass spectrometry-based targeted quantitative proteomics: achieving sensitive and reproducible detection of proteins. Proteomics. 12, 1093-1110 (2012).
- Gillette, M. A., Carr, S. A. Quantitative analysis of peptides and proteins in biomedicine by targeted mass spectrometry. Nat Methods. 10, 28-34 (2013).
- Picotti, P., Aebersold, R. Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat Methods. 9, 555-566 (2012).
- Lesur, A., Domon, B. Advances in high-resolution accurate mass spectrometry application to targeted proteomics. Proteomics. , (2015).
- Wild, D. . The immunoassay handbook : theory and applications of ligand binding ELISA., and related techniques. , (2013).
- Sturgeon, C. M., Viljoen, A. Analytical error and interference in immunoassay: minimizing risk. Ann Clin Biochem. 48, 418-432 (2011).
- Adrait, A., et al. Development of a Protein Standard Absolute Quantification (PSAQ) assay for the quantification of Staphylococcus aureus enterotoxin A in serum. J Proteomics. 75, 3041-3049 (2012).
- Lin, D., Alborn, W. E., Slebos, R. J., Liebler, D. C. Comparison of protein immunoprecipitation-multiple reaction monitoring with ELISA for assay of biomarker candidates in plasma. J Proteome Res. 12, 5996-6003 (2013).
- Weiss, F., et al. Catch and measure-mass spectrometry-based immunoassays in biomarker research. Biochim Biophys Acta. 1844, 927-932 (2014).
- Yassine, H., et al. Mass spectrometric immunoassay and MRM as targeted MS-based quantitative approaches in biomarker development: potential applications to cardiovascular disease and diabetes. Proteomics Clin Appl. 7, 528-540 (2013).
- Zhao, L., et al. Quantification of proteins using peptide immunoaffinity enrichment coupled with mass spectrometry. J Vis Exp. , (2011).
- Becker, J. O., Hoofnagle, A. N. Replacing immunoassays with tryptic digestion-peptide immunoaffinity enrichment and LC-MS/MS. 4, 281-290 (2012).
- Wasinger, V. C., Zeng, M., Yau, Y. Current status and advances in quantitative proteomic mass spectrometry. Int J Proteomics. 2013, 180605 (2013).
- Abbatiello, S. E., et al. Large-scale inter-laboratory study to develop, analytically validate and apply highly multiplexed, quantitative peptide assays to measure cancer-relevant proteins in plasma. Mol Cell Proteomics. , (2015).
- Rodriguez-Suarez, E., Whetton, A. D. The application of quantification techniques in proteomics for biomedical research. Mass Spectrom Rev. 32, 1-26 (2013).
- Wehr, A. Y., Hwang, W. T., Blair, I. A., Yu, K. H. Relative quantification of serum proteins from pancreatic ductal adenocarcinoma patients by stable isotope dilution liquid chromatography-mass spectrometry. J Proteome Res. 11, 1749-1758 (2012).
- Kennedy, J. J., et al. Demonstrating the feasibility of large-scale development of standardized assays to quantify human proteins. Nat Methods. 11, 149-155 (2014).
- Jensen, K. J., Shelton, P. T., Pedersen, S. L. . Peptide synthesis and applications. , (2013).
- Pratt, J. M., et al. Multiplexed absolute quantification for proteomics using concatenated signature peptides encoded by QconCAT genes. Nat Protoc. 1, 1029-1043 (2006).
- Brun, V., et al. Isotope-labeled protein standards: toward absolute quantitative proteomics. Mol Cell Proteomics. 6, 2139-2149 (2007).
- . . Guidance for Industry: Bioanalytical Method Validation. , (2001).
- . . Guidance for Industry: Bioanalytical Method Validation. , (2013).
- Carr, S. A., et al. Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach. Mol Cell Proteomics. 13, 907-917 (2014).
- Ori, A., Andres-Pons, A., Beck, M. The use of targeted proteomics to determine the stoichiometry of large macromolecular assemblies. Methods Cell Biol. 122, 117-146 (2014).
- Rosen, O., Feldberg, L., Gura, S., Zichel, R. A new peptide substrate for enhanced botulinum neurotoxin type B detection by endopeptidase-liquid chromatography-tandem mass spectrometry/multiple reaction monitoring assay. Anal Biochem. , (2015).
- Feng, Y., et al. Global analysis of protein structural changes in complex proteomes. Nat Biotechnol. 32, 1036-1044 (2014).
- MacLean, B., et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics. 26, 966-968 (2010).
- Mohammed, Y., et al. PeptidePicker: a scientific workflow with web interface for selecting appropriate peptides for targeted proteomics experiments. J Proteomics. 106, 151-161 (2014).
- Rodriguez, J., Gupta, N., Smith, R. D., Pevzner, P. A. Does trypsin cut before proline. J Proteome Res. 7, 300-305 (2008).
- Min, X. J., Butler, G., Storms, R., Tsang, A. OrfPredictor: predicting protein-coding regions in EST-derived sequences. Nucleic Acids Res. 33, W677-W680 (2005).
- Lam, H., et al. Building consensus spectral libraries for peptide identification in proteomics. Nat Methods. 5, 873-875 (2008).
- Craig, R., Cortens, J. P., Beavis, R. C. Open source system for analyzing, validating, and storing protein identification data. J Proteome Res. 3, 1234-1242 (2004).
- Desiere, F., et al. The PeptideAtlas project. Nucleic Acids Res. 34, D655-D658 (2006).
- Vizcaino, J. A., et al. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 41, D1063-D1069 (2013).
- Frank, R. The SPOT-synthesis technique. Synthetic peptide arrays on membrane supports–principles and applications. J Immunol Methods. 267, 13-26 (2002).
- Ong, S. E., Kratchmarova, I., Mann, M. Properties of 13C-substituted arginine in stable isotope labeling by amino acids in cell culture (SILAC). J Proteome Res. 2, 173-181 (2003).
- Mant, C. T., et al. HPLC analysis and purification of peptides. Methods Mol Biol. 386, 3-55 (2007).
- Alterman, M. A., Hunziker, P. . Amino acid analysis : methods and protocols. , (2012).
- Maclean, B., et al. Effect of collision energy optimization on the measurement of peptides by selected reaction monitoring (SRM) mass spectrometry. Anal Chem. 82, 10116-10124 (2010).
- Nesvizhskii, A. I. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. J Proteomics. 73, 2092-2123 (2010).
- Tabb, D. L., Friedman, D. B., Ham, A. J. Verification of automated peptide identifications from proteomic tandem mass spectra. Nat Protoc. 1, 2213-2222 (2006).
- Freshney, R. I. . Culture of animal cells : a manual of basic technique and specialized applications. , (2010).
- Oberg, A. L., Vitek, O. Statistical design of quantitative mass spectrometry-based proteomic experiments. J Proteome Res. 8, 2144-2156 (2009).
- Noble, J. E., Bailey, M. J. Quantitation of protein. Methods Enzymol. 463, 73-95 (2009).
- Kiser, J. Z., Post, M., Wang, B., Miyagi, M. Streptomyces erythraeus trypsin for proteomics applications. J Proteome Res. 8, 1810-1817 (2009).
- Reiter, L., et al. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat Methods. 8, 430-435 (2011).
- Abbatiello, S. E., Mani, D. R., Keshishian, H., Carr, S. A. Automated detection of inaccurate and imprecise transitions in peptide quantification by multiple reaction monitoring mass spectrometry. Clin Chem. 56, 291-305 (2010).
- Callister, S. J., et al. Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. J Proteome Res. 5, 277-286 (2006).
- Karpievitch, Y. V., Dabney, A. R., Smith, R. D. Normalization and missing value imputation for label-free LC-MS analysis. BMC Bioinformatics. 13, S5 (2012).
- Oh, S., Kang, D. D., Brock, G. N., Tseng, G. C. Biological impact of missing-value imputation on downstream analyses of gene expression profiles. Bioinformatics. 27, 78-86 (2011).
- Germain, R. N., Meier-Schellersheim, M., Nita-Lazar, A., Fraser, I. D. Systems biology in immunology: a computational modeling perspective. Annu Rev Immunol. 29, 527-585 (2011).
- Futran, A. S., Link, A. J., Seger, R., Shvartsman, S. Y. ERK as a model for systems biology of enzyme kinetics in cells. Curr Biol. 23, R972-R979 (2013).
- Krokhin, O. V. Sequence-specific retention calculator. Algorithm for peptide retention prediction in ion-pair RP-HPLC: application to 300- and 100-A pore size C18 sorbents. Anal Chem. 78, 7785-7795 (2006).
- Manes, N. P., Angermann, B. R., Koppenol-Raab, M., An, E., Sjoelund, V. H., Sun, J., Ishii, M., Germain, R. N., Meier-Schellersheim, M., Nita-Lazar, A. Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing. . Mol Cell Proteomics. , .
