高通量、全自动化蛋白质组样品前处理平台建立和高通量LC-MS方法的评估

基于质谱的蛋白质组学技术已经日趋成熟，可以对细胞和组织中的成千上万种蛋白质进行全面的定性和定量分析，逐步实现了“深度覆盖”。样品前处理是整个蛋白质组学分析流程的关键步骤，决定了整个分析过程的灵敏度、准确性和稳定性。随着生物医学日益增长的大队列蛋白质组学分析需求，如何实现全自动化、高通量的样品前处理以及发展高通量和稳定性的 LC-MS/MS 方法已成为当前亟需解决的关键问题。

针对目前大样本制备缺乏全自动化的方法，利用全集成式的样品前处理技术 SISPROT 与安捷伦自动化移液工作站联用，本文发展了全自动化、高通量的蛋白质组样品前处理平台 autoSISPROT。对 autoSISPROT 的样本制备性能进行了表征，其中烷基化效率大于 97%，酶解位点 0 漏切比例大于 80%，在 2.5 h 之内可以 实现 96 个样品的 全 自动化 处理。利用 autoSISPROT 制备了 3 批次 96 通道的样品，不同批次间和批次内的蛋白质定量数目平均 CV 值均小于 4%，批次内蛋白质定量强度 CV 中位数小于14%，证明我们建立的平台可以成为大队列蛋白质组样本预处理分析的有力支撑，具有很强的应用前景及实际价值。

为了实现高通量蛋白质组学分析，搭建了毛细管流（capLC-MS/MS, 使用 150 μm 内径色谱柱，流速为 1 μL/min）和微流（μLC-MS/MS，使用 1 mm  内径色谱柱，流速为 50 μL/min）液相色谱串联质谱平台，并从灵敏度、分离效率、通量和稳健性方面进行对比分析。其中，capLC-MS/MS 的灵敏度比μLC-MS/MS 高 10 倍左右，而μLC-MS/MS 具有更高的分离效率和喷雾稳定性。此外，与 capLC-MS/MS 相比，μLC-MS/MS 能够实现更高的分析通量。在 7 天的长期性能测试中，capLC-MS/MS 和 μLC-MS/MS 在色谱峰全宽（RSD<3%）、保留时间（RSD<0.7%）和蛋白质鉴定（RSD<3%）方面都表现出良好的重现性，表明二者在高通量蛋白质组学分析应用中的可行性及潜力，进一步与大队列临床蛋白质组学分析相匹配。

Mass spectrometry-based proteomics technologies have become increasingly mature and can provide comprehensive qualitative and quantitative analysis of thousands of proteins in cells and tissues, gradually achieving "deep coverage". Sample preparation is a key step in the whole proteomics process, which determines the sensitivity, accuracy and stability of the whole analysis process. With the growing demand for large cohort proteomics analysis in biomedicine, how to achieve fully automated, high-throughput sample preparation and the development of high-throughput and robust LC-MS/MS methods have become the key issues that need to be addressed urgently.

To solve the problem of the lack of fully automated preparation methods for large sample preparation, a fully automated, high-throughput proteomic sample preparation platform, autoSISPROT, was developed using a fully integrated sample preparation technology, SISPROT, and an Agilent automated pipetting workstation. The efficiency of alkylation and digestion was more than 97% and 80%, respectively, and autoSISPROT could achieve fully automated processing of 96 samples in 2.5 hours. Three batches of 96-channel samples were prepared using autoSISPROT, and the CV of protein quantification of inter-batch and intra-batch were both less than 4%, and the median CV of protein quantification intensity was less than 14%. It demonstrates that the platform we have built can be a powerful support for preparation of cohort proteomic samples. In order to achieve the high-throughput proteome profiling, we established the capillary-flow (capLC-MS/MS, 150 μm i.d. column, 1 μL/min) and micro-flow (μLC-MS/MS, 1 mm i.d. column, 50 μL/min) liquid chromatography tandem mass spectrometry. We evaluated capLC-MS/MS and μLC-MS/MS in terms of sensitivity, separation efficiency, throughput and robustness. The capLC-MS/MS was about 10 times more sensitive than μLC-MS/MS, and μLC-MS/MS showed higher separation efficiency and spray stability. Besides, compared with capLC-MS/MS, μLC-MS/MS was able to achieve higher throughput. During the 7 days of the long-term performance test, both capLC-MS/MS and μLC-MS/MS showed excellent reproducibility of chromatographic full width (RSD<3%), retention time (RSD<0.7%), and protein identification (RSD<3%). In summary, the results show the feasibility and potential of both LC-MS analysis methods for high-throughput proteomics analysis applications.

[1] PAGE M J, GRIFFITHS T A, BLEACKLEY M R, et al. Proteomics: applications relevant to transfusion medicine [J]. Transfus Med Rev, 2006, 20(1): 63-74.
[2] WILKINS M R, PASQUALI C, APPEL R D, et al. From Proteins to Proteomes: Large Scale Protein Identification by Two-Dimensional Electrophoresis and Amino Acid Analysis [J]. Bio/Technology, 1996, 14(1): 61-5.
[3] WILHELM M, SCHLEGL J, HAHNE H, et al. Mass-spectrometry-based draft of the human proteome [J]. Nature, 2014, 509(7502): 582-7.
[4] WANG D, ERASLAN B, WIELAND T, et al. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues [J]. Mol Syst Biol, 2019, 15(2): e8503.
[5] GEYER P E, HOLDT L M, TEUPSER D, et al. Revisiting biomarker discovery by plasma proteomics [J]. Mol Syst Biol, 2017, 13(9): 942.
[6] GEYER P E, KULAK N A, PICHLER G, et al. Plasma Proteome Profiling to Assess Human Health and Disease [J]. Cell Syst, 2016, 2(3): 185-95.
[7] GEYER P E, VOYTIK E, TREIT P V, et al. Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies [J]. EMBO Mol Med, 2019, 11(11): e10427.
[8] NIU L, GEYER P E, WEWER ALBRECHTSEN N J, et al. Plasma proteome profiling discovers novel proteins associated with non-alcoholic fatty liver disease [J]. Mol Syst Biol, 2019, 15(3): e8793.
[9] GE S, XIA X, DING C, et al. A proteomic landscape of diffuse-type gastric cancer [J]. Nat Commun, 2018, 9(1): 1012.
[10] BADER J M, GEYER P E, MULLER J B, et al. Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer's disease [J]. Mol Syst Biol, 2020, 16(6): e9356.
[11] FENN J, MANN M, MENG C, et al. Electrospray ionization for mass spectrometry of large biomolecules [J]. 1989, 246(4926): 64-71.
[12] KARAS M, HILLENKAMP F. Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons [J]. Analytical chemistry, 1988, 60(20): 2299-301.
[13] TIAN R. Exploring intercellular signaling by proteomic approaches [J]. Proteomics, 2014, 14(4-5): 498-512.
[14] YE X, TANG J, MAO Y, et al. Integrated proteomics sample preparation and fractionation: Method development and applications [J]. TrAC Trends in Analytical Chemistry, 2019, 120: 115667-80.
[15] RAPPSILBER J, ISHIHAMA Y, MANN M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics [J]. Anal Chem, 2003, 75(3): 663-70.
[16] YU Y, SUH M J, SIKORSKI P, et al. Urine sample preparation in 96-well filter plates for quantitative clinical proteomics [J]. Anal Chem, 2014, 86(11): 5470-7.
[17] LU X, WANG Z, GAO Y, et al. AutoProteome Chip System for Fully Automated and Integrated Proteomics Sample Preparation and Peptide Fractionation [J]. Anal Chem, 2020, 92(13): 8893-900.
[18] MÜLLER T, KALXDORF M, LONGUESPÉE R, et al. Automated sample preparation with SP3 for low-input clinical proteomics [M]. 2019.
[19] WILSON S R, VEHUS T, BERG H S, et al. Nano-LC in proteomics: recent advances and approaches [J]. Bioanalysis, 2015, 7(14): 1799-815.
[20] WILM M, MANN M. Analytical properties of the nanoelectrospray ion source [J]. Anal Chem, 1996, 68(1): 1-8.
[21] DUBROVSKII Y, MURASHKO E, CHUPRINA O, et al. Mass spectrometry based proteomic approach for the screening of butyrylcholinesterase adduct formation with organophosphates [J]. Talanta, 2019, 197: 374-82.
[22] DISTLER U, LACKI M K, SCHUMANN S, et al. Enhancing Sensitivity of Microflow-Based Bottom-Up Proteomics through Postcolumn Solvent Addition [J]. Analytical Chemistry, 2019, 91(12): 7510-5.
[23] DE GODOY L M, OLSEN J V, COX J, et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast [J]. Nature, 2008, 455(7217): 1251-4.
[24] GO E P, REBECCHI K R, DESAIRE H. In-solution digestion of glycoproteins for glycopeptide-based mass analysis [J]. Methods Mol Biol, 2013, 951: 103-11.
[25] LIEBLER D C, HAM A J. Spin filter-based sample preparation for shotgun proteomics [J]. Nat Methods, 2009, 6(11): 785; author reply -6.
[26] CHEN W, ADHIKARI S, CHEN L, et al. 3D-SISPROT: A simple and integrated spintip-based protein digestion and three-dimensional peptide fractionation technology for deep proteome profiling [J]. J Chromatogr A, 2017, 1498: 207-14.
[27] RAPPSILBER J, MANN M, ISHIHAMA Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips [J]. Nat Protoc, 2007, 2(8): 1896-906.
[28] MIKULÁŠEK K, KONEČNÁ H, POTĚŠIL D, et al. SP3 Protocol for Proteomic Plant Sample Preparation Prior LC-MS/MS [J]. Front Plant Sci, 2021, 12: 635550.
[29] CHEN W, WANG S, ADHIKARI S, et al. Simple and Integrated Spintip-Based Technology Applied for Deep Proteome Profiling [J]. Analytical chemistry, 2016, 88(9): 4864-71.
[30] XUE L, LIN L, ZHOU W, et al. Mixed-mode ion exchange-based integrated proteomics technology for fast and deep plasma proteome profiling [J]. Journal of chromatography A, 2018, 1564: 76-84.
[31] XU R, TANG J, DENG Q, et al. Spatial-Resolution Cell Type Proteome Profiling of Cancer Tissue by Fully Integrated Proteomics Technology [J]. Anal Chem, 2018, 90(9): 5879-86.
[32] HUANG P, LI H, GAO W, et al. A Fully Integrated Spintip-Based Approach for Sensitive and Quantitative Profiling of Region-Resolved in Vivo Brain Glycoproteome [J]. Anal Chem, 2019, 91(14): 9181-9.
[33] CHEN W, CHEN L, TIAN R. An integrated strategy for highly sensitive phosphoproteome analysis from low micrograms of protein samples [J]. Analyst, 2018, 143(15): 3693-701.
[34] ZHANG X, CHEN W, NING Z, et al. Deep Metaproteomics Approach for the Study of Human Microbiomes [J]. Analytical chemistry, 2017, 89(17): 9407-15.
[35] HUANG P, KONG Q, GAO W, et al. Spatial proteome profiling by immunohistochemistry-based laser capture microdissection and data-independent acquisition proteomics [J]. Analytica chimica acta, 2020, 1127: 140-8.
[36] LIN L, ZHENG J, YU Q, et al. High throughput and accurate serum proteome profiling by integrated sample preparation technology and single-run data independent mass spectrometry analysis [J]. J Proteomics, 2018, 174: 9-16.
[37] KE M, LIU J, CHEN W, et al. Integrated and Quantitative Proteomic Approach for Charting Temporal and Endogenous Protein Complexes [J]. Analytical chemistry, 2018, 90(21): 12574-83.
[38] WIŚNIEWSKI J R, ZOUGMAN A, NAGARAJ N, et al. Universal sample preparation method for proteome analysis [J]. Nat Methods, 2009, 6(5): 359-62.
[39] ZHANG Z B, DUBIAK K M, HUBER P W, et al. Miniaturized Filter-Aided Sample Preparation (MICRO-FASP) Method for High Throughput, Ultrasensitive Proteomics Sample Preparation Reveals Proteome Asymmetry in Xenopus laevis Embryos [J]. Anal Chem, 2020, 92(7): 5554-60.
[40] RAPPSILBER J, ISHIHAMA Y, MANN M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics [J]. Analytical chemistry, 2003, 75(3): 663-70.
[41] KULAK N A, PICHLER G, PARON I, et al. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells [J]. Nature methods, 2014, 11(3): 319-24.
[42] MESSNER C B, DEMICHEV V, WENDISCH D, et al. Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection [J]. Cell Systems, 2020, 11(1): 11-24.e4.
[43] BURNS A P, ZHANG Y-Q, XU T, et al. A Universal and High-Throughput Proteomics Sample Preparation Platform [J]. Analytical chemistry, 2021.
[44] LEUTERT M, RODRIGUEZ-MIAS R A, FUKUDA N K, et al. R2-P2 rapid-robotic phosphoproteomics enables multidimensional cell signaling studies [J]. Mol Syst Biol, 2019, 15(12): e9021.
[45] MULLER J B, GEYER P E, COLACO A R, et al. The proteome landscape of the kingdoms of life [J]. Nature, 2020.
[46] HUGHES C S, FOEHR S, GARFIELD D A, et al. Ultrasensitive proteome analysis using paramagnetic bead technology [J]. Mol Syst Biol, 2014, 10: 757.
[47] HUGHES C S, MOGGRIDGE S, MULLER T, et al. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments [J]. Nat Protoc, 2019, 14(1): 68-85.
[48] MÜLLER T, KALXDORF M, LONGUESPÉE R, et al. Automated sample preparation with SP3 for low-input clinical proteomics [J]. Molecular Systems Biology, 2020, 16(1).
[49] ANGEL T E, ARYAL U K, HENGEL S M, et al. Mass spectrometry-based proteomics: existing capabilities and future directions [J]. Chem Soc Rev, 2012, 41(10): 3912-28.
[50] XIE F, SMITH R D, SHEN Y. Advanced proteomic liquid chromatography [J]. Journal of Chromatography A, 2012, 1261: 78-90.
[51] SHI Y, XIANG R, HORVATH C, et al. The role of liquid chromatography in proteomics [J]. J Chromatogr A, 2004, 1053(1-2): 27-36.
[52] DAMS M, DORES-SOUSA J L, LAMERS R J, et al. High-Resolution Nano-Liquid Chromatography with Tandem Mass Spectrometric Detection for the Bottom-Up Analysis of Complex Proteomic Samples [J]. Chromatographia, 2019, 82(1): 101-10.
[53] BRUDERER R, MUNTEL J, MULLER S, et al. Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance [J]. Mol Cell Proteomics, 2019, 18(6): 1242-54.
[54] KONG Q, HUANG P, CHU B, et al. High-Throughput and Integrated Chemical Proteomic Approach for Profiling Phosphotyrosine Signaling Complexes [J]. Anal Chem, 2020, 92(13): 8933-42.
[55] HINNEBURG H, CHATTERJEE S, SCHIRMEISTER F, et al. Post-Column Make-Up Flow (PCMF) Enhances the Performance of Capillary-Flow PGC-LC-MS/MS-Based Glycomics [J]. Analytical Chemistry, 2019, 91(7): 4559-67.
[56] ROUX-DALVAI F, GOTTI C, LECLERCQ M, et al. Fast and Accurate Bacterial Species Identification in Urine Specimens Using LC-MS/MS Mass Spectrometry and Machine Learning [J]. Molecular & Cellular Proteomics, 2019, 18(12): 2492-505.
[57] MESSNER C B, DEMICHEV V, BLOOMFIELD N, et al. Ultra-fast proteomics with Scanning SWATH [J]. Nat Biotechnol, 2021, 39(7): 846-54.
[58] LESUR A, SCHMIT P O, BERNARDIN F, et al. Highly Multiplexed Targeted Proteomics Acquisition on a TIMS-QTOF [J]. Anal Chem, 2021, 93(3): 1383-92.
[59] MEIER F, GEYER P E, VIRREIRA WINTER S, et al. BoxCar acquisition method enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes [J]. Nat Methods, 2018, 15(6): 440-8.
[60] BEKKER-JENSEN D B, MARTINEZ-VAL A, STEIGERWALD S, et al. A Compact Quadrupole-Orbitrap Mass Spectrometer with FAIMS Interface Improves Proteome Coverage in Short LC Gradients [J]. Mol Cell Proteomics, 2020, 19(4): 716-29.
[61] ADONI K R, CUNNINGHAM D L, HEATH J K, et al. FAIMS Enhances the Detection of PTM Crosstalk Sites [J]. J Proteome Res, 2022.
[62] HEBERT A S, PRASAD S, BELFORD M W, et al. Comprehensive Single-Shot Proteomics with FAIMS on a Hybrid Orbitrap Mass Spectrometer [J]. Anal Chem, 2018, 90(15): 9529-37.
[63] MEIER F, BRUNNER A D, KOCH S, et al. Online Parallel Accumulation-Serial Fragmentation (PASEF) with a Novel Trapped Ion Mobility Mass Spectrometer [J]. Mol Cell Proteomics, 2018, 17(12): 2534-45.
[64] MEIER F, BECK S, GRASSL N, et al. Parallel Accumulation-Serial Fragmentation (PASEF): Multiplying Sequencing Speed and Sensitivity by Synchronized Scans in a Trapped Ion Mobility Device [J]. J Proteome Res, 2015, 14(12): 5378-87.
[65] MEIER F, BRUNNER A D, FRANK M, et al. diaPASEF: parallel accumulation-serial fragmentation combined with data-independent acquisition [J]. Nat Methods, 2020, 17(12): 1229-36.
[66] HE Y, RASHAN E H, LINKE V, et al. Multi-Omic Single-Shot Technology for Integrated Proteome and Lipidome Analysis [J]. Anal Chem, 2021, 93(9): 4217-22.
[67] MAJUTA S N, DEBASTIANI A, LI P, et al. Combining Field-Enabled Capillary Vibrating Sharp-Edge Spray Ionization with Microflow Liquid Chromatography and Mass Spectrometry to Enhance 'Omits Analyses [J]. J Am Soc Mass Spectr, 2021, 32(2): 473-85.
[68] LENCO J, VAJRYCHOVA M, PIMKOVA K, et al. Conventional-Flow Liquid Chromatography-Mass Spectrometry for Exploratory Bottom-Up Proteomic Analyses [J]. Anal Chem, 2018, 90(8): 5381-9.
[69] BIAN Y, ZHENG R, BAYER F P, et al. Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC-MS/MS [J]. Nat Commun, 2020, 11(1): 157.
[70] BIAN Y, THE M, GIANSANTI P, et al. Identification of 7000-9000 Proteins from Cell Lines and Tissues by Single-Shot Microflow LC-MS/MS [J]. Anal Chem, 2021, 93(25): 8687-92.
[71] BIAN Y, BAYER F P, CHANG Y C, et al. Robust Microflow LC-MS/MS for Proteome Analysis: 38000 Runs and Counting [J]. Anal Chem, 2021, 93(8): 3686-90.