南方科技大学知识苑(SUSTech KC): 面向大规模血浆样本的自动化和深度蛋白质组学前处理技术

题名	面向大规模血浆样本的自动化和深度蛋白质组学前处理技术
其他题名	AUTOMATED，IN-DEPTH PROTEOMICS PREPARATION TECHNOLOGY FOR LARGE-SCALE PLASMA SAMPLES
姓名	崔晓振
姓名拼音	CUI Xiaozhen
学号	12032753
学位类型	硕士
学位专业	070302 分析化学
学科门类/专业学位类别	07 理学
导师	田瑞军
导师单位	化学系
论文答辩日期	2023-05-29
论文提交日期	2023-06-26
学位授予单位	南方科技大学
学位授予地点	深圳
摘要	基于生物质谱的蛋白质组学技术试图在系统水平上分析细胞、组织或体液中表达的蛋白质，进而全面研究生物过程，目前已经成为一种通用的分析手段。血浆是医学上常见的检测样本，可以实时动态地反映个体的健康状态。蛋白质组学技术可以无差别地定量血浆中所有的蛋白质信息，然而超过 10 个数量级的蛋白质动态范围和大量的样本处理实验阻碍了血浆蛋白质组研究。因此，开发深度、高通量、自动化的血浆蛋白质组学样品前处理技术成为临床大规模应用研究的迫切需求。本论文首先在自动化移液工作站上开发了一种基于溶液酶解的高通量、自动化血浆蛋白质组学样品前处理技术，以满足临床大队列研究的需求。该前处理技术可以在 5 小时内完成 32 个血浆样本的前处理操作；在无分级和去除高丰度蛋白的情况下，使用微升流速 LC-MS/MS 分析实现单个样本超过 300 个蛋白质的定性和定量分析，其中包括 35 个 FDA 批准的血浆生物标志物，且定量到的蛋白中超过 86%的 CV 值小于 20%。结肠癌不同年龄样本的概念性验证队列分析证明了该技术在临床大队列研究中的可靠性。在上述前处理技术基础上，通过两种磁性离子交换材料的合成以及与自动化移液工作站的结合，本论文最终实现了深度、高通量、自动化血浆蛋白质组学样品前处理技术的开发。该技术可以在6 小时内处理32个血浆样本，单个样品质谱鉴定蛋白量接近1000，其中包括45个 FDA 批准的血浆生物标志物。在96 例乳腺癌新辅助化疗血浆样本的概念性验证队列分析中发现了45 个显著性变化的蛋白质，其中 11 个已经被报道与乳腺癌的发生发展密切相关，显示出该前处理技术在发现生物标志物方面的应用潜力。
其他摘要	MS-based proteomics attempts to analyze proteins expressed in a cell, tissue or body fluid at a systematic level, and then comprehensively study biological processes. It has become a common analytical method in laboratory and clinic. Plasma, as a common sample in medicine, can dynamically reflect individual health state in real time. MS-based proteomics can quantify all proteins in plasma without bias, but excessive dynamic range and clinical sample of plasma proteome affect its large-scale analysis. So now, the development of the in-depth, high-throughput and automated plasma proteomics preparation technology has become an urgent need for large-scale clinic research. We firstly developed a high-throughput, automated plasma proteomics sample preparation technology based on in-solution digestion on a liquid-handling system to meet the need of large cohort study. It could complete the preparation of 32 plasma samples within 5 hours. Without fraction and depletion, the μLC-MS/MS analysis could quantify more than 300 protein groups in a single sample, including 35 FDA approved plasma protein biomarkers, with over 86% of the proteins having CV less than 20%. The proof-of-concept study of colon cancer samples proved that the technology was stable and reliable in the study of clinical large cohort. On the basis of the above preparation technology, we synthesized two magnetic microparticles modified with ion exchange group on the surface and combined them with the liquid-handling system again, finally realized the construction of an in-depth, high-throughput and automated plasma proteomics sample preparation technology. The technology could prepare 32 plasma samples within 6 hours and quantify about 1000 protein groups per sample, including 45 FDA approved plasma protein biomarkers. In the cohort study of nearly 100 breast cancer NC plasma samples, 45 differentially expressed proteins were quantified, 11 of them have been reported to be related to the breast cancer, showing the application ability of the preparation platform we developed in the discovery of biomarkers.
关键词	血浆蛋白质组学自动化样本制备离子交换材料质谱
其他关键词	Plasma Proteomics Automated Sample Preparation Ion Exchange Particle Mass Spectrometer
语种	中文
培养类别	独立培养
入学年份	2020
学位授予年份	2023-06
参考文献列表	[1] WILKINS M R, PASQUALI C, APPEL R D, et al. From Proteins to Proteomes:Large Scale Protein Identification by Two-Dimensional Electrophoresis and ArninoAcid Analysis [J]. Bio/Technology, 1996, 14(1): 61-5. [2] TYERS M, MANN M. From genomics to proteomics [J]. Nature, 2003, 422(6928):193-7. [3] CRAVATT B F, SIMON G M, YATES J R. The biological impact of mass- spectrometry-based proteomics [J]. Nature, 2007, 450(7172): 991-1000. [4] MANN M. The Rise of Mass Spectrometry and the Fall of Edman Degradation [J]. Clinical Chemistry, 2016, 62(1): 293-4. [5] GAO W, ZHANG Q, SU Y, et al. Multiomic analysis of a dried single-drop plasma sample using an integrated mass spectrometry approach [J]. The Analyst, 2020, 145(20): 6441-6. [6] MALMSTROM E, KILSGARD O, HAURI S, et al. Large-scale inference of proteintissue origin in gram-positive sepsis plasma using quantitative targeted proteomics[J]. Nature Communications, 2016, 7(1): 10261. [7] GEYER P E, HOLDT L M, TEUPSER D, et al. Revisiting biomarker discovery byplasma proteomics [J]. Molecular systems biology, 2017, 13(9): 942. [8] ANDERSON N L, ANDERSON N G. The Human Plasma Proteome: History, Character, and Diagnostic Prospects [J]. Molecular & Cellular Proteomics, 2002, 1(11): 845-67. [9] ANDERSON N L, MING L, HUANG P. The Clinical Plasma Proteome: A Survey ofClinical Assays for Proteins in Plasma and Serum [J]. Clinical Chemistry, 2010, 56(2): 177-85. [10] DEUTSCH E W, OMENN G S, SUN Z, et al. Advances and Utility of the HumanPlasma Proteome [J]. Journal of Proteome Research, 2021, 20(12): 5241-63. [11] ZHONG W, EDFORS F, GUMMESSON A, et al. Next generation plasma proteome profiling to monitor health and disease [J]. Nature Communications, 2021, 12(1):2493. [12] ROHLOFF J C, GELINAS A D, JARVIS T C, et al. Nucleic Acid Ligands WithProtein-like Side Chains: Modified Aptamers and Their Use as Diagnostic andTherapeutic Agents [J]. Molecular Therapy-Nucleic Acids, 2014, 3(10): e201. [13] 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. [14] GEYER P E, KULAK N A, PICHLER G, et al. Plasma Proteome Profiling to Assess Human Health and Disease [J]. Cell Systems, 2016, 2(3): 185-95. [15] NIU L, GEYER P E, WEWER A N J, et al. Plasma proteome profiling discoversnovel proteins associated with non-alcoholic fatty liver disease [J]. Molecularsystems biology, 2019, 15(3): e8793. [16] GEYER P E, AREND F M, DOLL S, et al. High-resolution serum proteometrajectories in COVID-19 reveal patient-specific seroconversion [J]. EMBOMolecular Medicine, 2021, 13(8): e14167. [17] MESSNER C B, DEMICHEV V, WENDISCH D, et al. Ultra-High-ThroughputClinical Proteomics Reveals Classifiers of COVID-19 Infection [J]. Cell Systems, 2020, 11(1): 11-24. [18] HUGHES C S, FOEHR S, GARFIELD D A, et al. Ultrasensitive proteome analysisusing paramagnetic bead technology [J]. Molecular systems biology, 2014, 10(10):757. [19] MULLER T, KALXDORF M, LONGUESPEE R, et al. Automated samplepreparation with SP3 for low-input clinical proteomics [J]. Molecular systemsbiology, 2020, 16(1): e9111. [20] LIU Y, YANG Q, DU Z, et al. Synthesis of Surface-Functionalized MolybdenumDisulfide Nanomaterials for Efficient Adsorption and Deep Profiling of the HumanPlasma Proteome by Data-Independent Acquisition [J]. Analytical Chemistry, 2022, 94(43): 14956-64. [21] CHEN W, WANG S, ADHIKARI S, et al. Simple and Integrated Spintip-BasedTechnology Applied for Deep Proteome Profiling [J]. Analytical Chemistry, 2016, 88(9): 4864-71. [22] CHEN W, CHEN L, TIAN R. An integrated strategy for highly sensitivephosphoproteome analysis from low micrograms of protein samples [J]. TheAnalyst, 2018, 143(15): 3693-701. [23] GAO W, LI H, LIU L, et al. An integrated strategy for high-sensitive and multilevel glycoproteome analysis from low micrograms of protein samples [J]. Journalof Chromatography A, 2019, 1600(1): 46-54. [24] LU X, WANG Z, GAO Y, et al. AutoProteome Chip System for Fully Automatedand Integrated Proteomics Sample Preparation and Peptide Fractionation [J]. Analytical Chemistry, 2020, 92(13): 8893-900. [25] 孙秀杰, 唐君, 陈文东, et al. 基于 SCX/SAX 混合填料的集成化蛋白质组学样品前处理方法 [J]. 中国科学:生命科学, 2018, 48(02): 188-94. [26] LIN L, ZHENG J, YU Q, et al. High throughput and accurate serum proteomeprofiling by integrated sample preparation technology and single-run dataindependent mass spectrometry analysis [J]. Journal of Proteomics, 2018, 174(1):9-16. [27] XUE L, LIN L, ZHOU W, et al. Mixed-mode ion exchange-based integratedproteomics technology for fast and deep plasma proteome profiling [J]. Journal ofChromatography A, 2018, 1564(1): 76-84. [28] GWARK S, AHN H, YEOM J, et al. Plasma Proteome Signature to Predict theOutcome of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy [J]. Cancers, 2021, 13(1): 6267. [29] DOCTER D, WESTMEIER D F M, MARKIEWICZ M F S, et al. The nanoparticlebiomolecule corona: lessons learned - challenge accepted? [J]. Chemical Societyreviews, 2015, 44(17): 6094-121. [30] CEDERVALL T, LYNCH I F S, LINDMAN S F T, et al. Understanding thenanoparticle-protein corona using methods to quantify exchange rates and affinities of proteins for nanoparticles [J]. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(7): 2050-5. [31] CAI R, REN J, JI Y, et al. Corona of Thorns: The Surface Chemistry-MediatedProtein Corona Perturbs the Recognition and Immune Response of Macrophages [J]. ACS Applied Materials & Interfaces, 2020, 12(2): 1997-2008. [32] OH J Y, KIM H S, PALANIKUMAR L, et al. Cloaking nanoparticles with proteincorona shield for targeted drug delivery [J]. Nature Communications, 2018, 9(1):4548. [33] CAO M, CAI R, ZHAO L, et al. Molybdenum derived from nanomaterialsincorporates into molybdenum enzymes and affects their activities in vivo [J]. Nature Nanotechnology, 2021, 16(6): 708-16. [34] WANG L, LI J, PAN J, et al. Revealing the Binding Structure of the Protein Corona on Gold Nanorods Using Synchrotron Radiation-Based Techniques: Understanding the Reduced Damage in Cell Membranes [J]. Journal of the American ChemicalSociety, 2013, 135(46): 17359-68. [35] BLUME J E, MANNING W C, TROIANO G, et al. Rapid, deep and preciseprofiling of the plasma proteome with multi-nanoparticle protein corona [J]. Nature Communications, 2020, 11(1): 3662. [36] FERDOSI S, TANGEYSH B, BROWN T R, et al. Engineered nanoparticles enabledeep proteomics studies at scale by leveraging tunable nano–bio interactions [J]. Proceedings of the National Academy of Sciences, 2022, 119(11): e2106053119. [37] BAIMANOV D, WANG J, ZHANG J, et al. In situ analysis of nanoparticle softcorona and dynamic evolution [J]. Nature Communications, 2022, 13(1): 5389. [38] SIMONSEN J B, MUNTER R. Pay Attention to Biological Nanoparticles whenStudying the Protein Corona on Nanomedicines [J]. Angewandte ChemieInternational Edition, 2020, 59(31): 12584-8. [39] KESHISHIAN H, BURGESS M W, SPECHT H, et al. Quantitative, multiplexedworkflow for deep analysis of human blood plasma and biomarker discovery bymass spectrometry [J]. Nature protocols, 2017, 12(8): 1683-701. [40] LUDWIG C, GILLET L, ROSENBERGER G, et al. Data-independent acquisition- based SWATH-MS for quantitative proteomics: a tutorial [J]. Molecular systemsbiology, 2018, 14(8): e8126. [41] 周岳, 杨湘云, 黄敏, et al. Orbitrap Exploris 480 质谱在定量蛋白质组学应用中的优化和评测 [J]. 生物化学与生物物理进展, 2021, 48(02): 214-26. [42] MEIER F, GEYER P E, VIRREIRA W S, et al. BoxCar acquisition method enablessingle-shot proteomics at a depth of 10,000 proteins in 100 minutes [J]. NatureMethods, 2018, 15(6): 440-8. [43] WEWER A N J, GEYER P E, DOLL S, et al. Plasma Proteome Profiling RevealsDynamics of Inflammatory and Lipid Homeostasis Markers after Roux-En-YGastric Bypass Surgery [J]. Cell Systems, 2018, 7(6): 601-12. [44] IGNJATOVIC V, GEYER P E, PALANIAPPAN K K, et al. Mass Spectrometry- Based Plasma Proteomics: Considerations from Sample Collection to AchievingTranslational Data [J]. Journal of Proteome Research, 2019, 18(12): 4085-97. [45] WILSON S R, VEHUS T, BERG H S, et al. Nano-LC in proteomics: recentadvances and approaches [J]. Bioanalysis, 2015, 7(14): 1799-815. [46] VEGVARI Á, WELINDER C, LINDBERG H, et al. Biobank resources for futurepatient care: developments, principles and concepts [J]. Journal of ClinicalBioinformatics, 2011, 1(1): 24. [47] VIHKO P, SAJANTI E, JANNE O, et al. Serum Prostate-specific Acid- phosphatase-development and Validation of a Specific Radioimmunoassay [J]. Clinical Chemistry, 1978, 24(11): 1915-9. [48] MONTES H Z. TNM Classification of Malignant Tumors, 7th edition [J]. International Journal of Radiation Oncology Biology Physics, 2010, 78(4): 1278. [49] NALDRETT M J, ZEIDLER R, WILSON K E, et al. Concentration and desalting ofpeptide and protein samples with a newly developed C18 membrane in a microspin column format [J]. Journal of biomolecular techniques: JBT, 2006, 16(1): 423-8. [50] BIAN Y, ZHENG R, BAYER F P, et al. Robust, reproducible and quantitativeanalysis of thousands of proteomes by micro-flow LC-MS/MS [J]. Naturecommunications, 2020, 11(1): 157. [51] SUI X, WU Q, CUI X, et al. Robust Capillary- and Micro-Flow LiquidChromatography–Tandem Mass Spectrometry Methods for High-ThroughputProteome Profiling [J]. Journal of Proteome Research, 2022, 21(10): 2472-80. [52] COX J, MANN M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification [J]. Nature Biotechnology, 2008, 26(12): 1367-72. [53] ENG J K, MCCORMACK A L, YATES J R. An approach to correlate tandem massspectral data of peptides with amino acid sequences in a protein database [J]. Journal of the American Society for Mass Spectrometry, 1994, 5(11): 976-89. [54] KALL L, CANTERBURY J D, WESTON J, et al. Semi-supervised learning forpeptide identification from shotgun proteomics datasets [J]. Nature Methods, 2007, 4(11): 923-5. [55] ANDERSON N L. The Clinical Plasma Proteome: A Survey of Clinical Assays forProteins in Plasma and Serum [J]. Clinical Chemistry, 2010, 56(2): 177-85. [56] HOSHINO A, KIM H S, BOJMAR L, et al. Extracellular Vesicle and ParticleBiomarkers Define Multiple Human Cancers [J]. Cell, 2020, 182(4): 1044-61. [57] SANTOS-LOZANO A, VALENZUELA P L, LLAVERO F, et al. Successful aging:insights from proteome analyses of healthy centenarians [J]. Aging-US, 2020, 12(4):3502-15. [58] YE S, MA L, ZHANG R, et al. Plasma proteomic and autoantibody profiles reveal the proteomic characteristics involved in longevity families in Bama, China [J]. Clinical Proteomics, 2019, 16(1): 22. [59] WANG Z, ZHANG R, LIU F, et al. TMT-Based Quantitative Proteomic AnalysisReveals Proteomic Changes Involved in Longevity [J]. PROTEOMICS - ClinicalApplications, 2018, 13(4): 1800024. [60] XU R, GONG C X, DUAN C M, et al. Age-Dependent Changes in the PlasmaProteome of Healthy Adults [J]. The journal of nutrition, health & aging, 2020, 24(8): 846-56. [61] SURINOVA S, CHOI M, TAO S, et al. Prediction of colorectal cancer diagnosisbased on circulating plasma proteins [J]. EMBO Molecular Medicine, 2015, 7(9):1166-78. [62] MESSNER C B, DEMICHEV V, BLOOMFIELD N, et al. Ultra-fast proteomicswith Scanning SWATH [J]. Nature biotechnology, 2021, 39(7): 846-54. [63] DEMICHEV V, MESSNER C B, VERNARDIS S I, et al. DIA-NN: neural networksand interference correction enable deep proteome coverage in high throughput [J]. Nature Methods, 2020, 17(1): 41-4. [64] CHUI S S Y, LO S M F, CHARMANT J P H, et al. A Chemically FunctionalizableNanoporous Material [Cu3(TMA)2(H2O)3]n [J]. Science, 1999, 283(5405): 1148-50. [65] CAVKA J H, JAKOBSEN S, OLSBYE U, et al. A New Zirconium Inorganic Building Brick Forming Metal Organic Frameworks with Exceptional Stability [J]. Journal of the American Chemical Society, 2008, 130(42): 13850-1. [66] SIEGEL R L, MILLER K D, WAGLE N S, et al. Cancer statistics, 2023 [J]. CA: ACancer Journal for Clinicians, 2023, 73(1): 17-48. [67] MARSH L A, CARRERA S, SHANDILYA J, et al. BASP1 interacts with oestrogenreceptor α and modifies the tamoxifen response [J]. Cell Death & Disease, 2017, 8(5): e2771-e. [68] BAUER M, EICKHOFF J C, GOULD M N, et al. Neutrophil gelatinase-associatedlipocalin (NGAL) is a predictor of poor prognosis in human primary breast cancer[J]. Breast Cancer Research and Treatment, 2008, 108(3): 389-97.
所在学位评定分委会	化学
国内图书分类号	O657.63
来源库	人工提交
成果类型	学位论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/544000
专题	理学院_化学系
推荐引用方式 GB/T 7714	崔晓振. 面向大规模血浆样本的自动化和深度蛋白质组学前处理技术[D]. 深圳. 南方科技大学,2023.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可	操作
12032753-崔晓振-化学系.pdf（2669KB）	--	--	限制开放	--	请求全文