博碩士論文 109223024 詳細資訊



以作者查詢圖書館館藏 、以作者查詢臺灣博碩士 、以作者查詢全國書目 、勘誤回報 、線上人數：5 、訪客IP：3.134.104.173
姓名 張柏毅(Bo-Yi Zhang)  查詢紙本館藏   畢業系所 化學學系
論文名稱 磷酸化蛋白體質譜法應用於微量組織
(Microscale tissue profiling by mass spectrometry-based phosphoproteomics)
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2027-8-15以後開放)
摘要(中) 蛋白質磷酸化是一種重要的轉譯後修飾，負責調控訊息傳遞以及細胞生長等重要生物資訊，先進的質譜技術已經被證實是一種能夠深度分析磷酸化蛋白質體的方法，在細胞與生物樣本中達到31,000個磷酸化修飾位點。然而，目前對於臨床檢體的深度磷酸化蛋白質體分析仍然需要大量的組織(>30毫克)以及耗時的樣品製備步驟。具有高靈敏度且全面性的分析臨床樣本，例如福馬林石蠟包埋法(1毫克/部分)與活體穿刺(9到15毫克) 的策略仍然等待開發。
因此，我們針對微量組織樣品發展與驗證一個高靈敏且流程精簡的磷酸化蛋白體分析方法，除此之外，我們更進一步將微量磷酸化蛋白體方法應用到人體乳癌組織檢體(0.5到2毫克)，其樣品量與福馬林石蠟包埋法所取得的樣品量相當，並且在癌症路徑中發現HER2和EGFR等重要的蛋白質。
我們開發了一套利用Sodium laurate (SL)萃取蛋白質的方法，能夠省略耗時的蛋白質沉澱步驟並且同時保持高達百分之十的蛋白萃取效率。首先，我們使用人體肺癌細胞株PC9去驗證此方法的靈敏度及重複性，實驗結果顯示在1微克胜肽樣品中，我們能夠鑑定到超過7000條的磷酸化胜肽，並且保持高度的重複性(三重複的中位數CV<2 %)。由於在Stage-tip中組織樣品量的限制下，此方法只能應用到樣品量>0.5毫克的組織中。儘管樣品量僅有0.5毫克，我們仍然能鑑定到6300條的磷酸化胜肽，並且保有高達95%的磷酸化萃取專一性。為了進一步增加磷酸化蛋白體的分析深度，我們建立了一個老鼠肺臟圖譜的資料庫，包含DDA以及DIA原始圖譜)以進行Library-based的非數據依賴擷取方法(Data-Independent Acquisition，DIA)，此方法在0.5毫克的組織中能夠鑑定到23,272條磷酸化胜肽，相對於傳統的數據依賴擷取策略(Data-Dependent Acquisition，DDA)，在磷酸化胜肽鑑定上顯著提升3.7倍。最後，我們成功的將微量磷酸化蛋白體方法應用到人體臨床檢體上，研究癌症路徑中PI3K及PTEN等致癌基因，並且能夠在微量條件下以磷酸化修飾位點的訊息區分正常與癌症組織。總而言之，此微量組織策略能夠在24小時內完成(包含質譜儀器分析時間)，對於日常檢體分析具有龐大的優勢，未來這個方法將會應用到更多微量臨床組織與檢體，期許對於醫療診斷方面能夠提供更多的生物醫療的資訊。
摘要(英) Protein phosphorylation is one of the most important post-translational modifications (PTMs) regulating signal transduction in all aspects of life. Advanced mass spectrometry has been demonstrated as a promising tool for large-scale phosphoproteomics with the depth of 31,000 phosphosites in cells and clinical specimens. However, recent studies for deep phosphoproteomics in clinical specimens still required a large amount of tissues (>30 mg) and dozens of sample preparation steps. A strategy with high sensitivity capable of robust profiling for clinical specimens such as FFPE (1.0 mg/section) and needle biopsy (9.0-15 mg) awaits to be established. In this thesis, we developed a highly sensitive and streamlined approach for phosphoproteomics profiling of microscale tissues. We further applied this microscale phosphoproteomics method to human breast cancer tissues (0.5-2.0 mg) which the sample amount was equivalent to FFPE specimens and then revealed HER2 and EGFR important proteins in the cancer pathway.
We have developed a streamlined approach using sodium laurate which could skip the time-consuming protein precipitation step and maintain high protein extraction efficiency. The PC9 cell line was used to evaluate the sensitivity and reproducibility. Identified 7,830 ± 57 phosphopeptides in only 1 μg peptide input and showed highly reproducibility in triplicates (median CV was less than 2 %). Due to the challenges of tissue sampling in the Stage-tip, application of this approach was limited to >0.5 mg (wet weight of tissue). Using 0.5 mg microscale mouse tissues, over 6,000 phosphopeptides were identified with high phosphopeptide enrichment specificity (95 %). To further increase the profiling coverage of tissue phosphoproteomics, we constructed a hybrid spectral library to incorporate library-based DIA strategy. Using 0.5 mg mouse samples, the strategy enhanced 3.7-fold coverage to reach 23,272 phosphopeptide identification than the conventional DDA method. Finally, we successfully applied this microscale approach to human breast cancer tissues to study the PI3K and PTEN oncogenes in cancer pathways that also separated normal and tumor samples with phosphosite distribution. Overall, the whole approach including LC-MS/MS analysis could be achieved in 24 hours, which provided a benefit for daily clinical research. The pipeline can be a general approach for phosphoproteomics profiling for microscale clinical tissues.
關鍵字(中) ★ 磷酸化蛋白體學
★ 微量組織
★ 非數據依賴擷取方法		 關鍵字(英)
論文目次 中文摘要 i
Abstract iii
目錄 v
圖目錄 vii
表目錄 ix
Chapter 1 Introduction 1
1.1 Importance of protein phosphorylation in cellular functions 1
1.2 Phosphoproteomics workflow and challenge 2
1.3 The role of phosphopeptide enrichment methods 3
1.4 Data acquisition strategies by LC-MS/MS 4
1.4.1 Data-dependent acquisition (DDA) mode 4
1.4.2 Data-independent acquisition (DIA) mode 5
1.5 Current status of tissue phosphoproteomics 6
1.6 Challenge of microscale tissue phosphoproteomics 8
1.7 Thesis objective 10
Chapter 2 Material and Method 12
2.1 Chemical and material 12
2.2 Cell culture 12
2.3 PC9 cell line sample preparation 12
2.4 Tissue sample preparation 13
2.5 Bicinchoninic acid (BCA) protein quantification 14
2.6 Peptide desalting by Stage-tip 14
2.7 Phosphopeptide enrichment by immobilized metal affinity chromatography 15
2.8 Liquid chromatography-mass spectrometry analysis (LC-MS/MS) 16
2.8.1 Data dependent acquisition (DDA) method 16
2.8.2 Data independent acquisition (DIA) method 16
2.9 Phosphoproteome identification and quantification 17
2.10 Bioinformatics analysis 18
Chapter 3 Result 19
3.1 Workflow of microscale phosphoproteomics 19
3.2 Evaluation of microscale phosphoproteomics by PC9 cell line 20
3.3 Experiment workflow of tissue sample preparation 21
3.4 Application to mouse lung tissue samples 22
3.4.1 Sensitivity and reproducibility test for tissue analysis 22
3.4.2 Comparative phosphoproteomics profiling of normal and tumor tissues 23
3.4.3 Comparison of DDA and DIA strategies for tissue phosphoproteomics 24
3.5 Construction of hybrid phosphopeptide spectra library from mouse tissue to enhance deep DIA-based phosphoproteomics profiling 26
3.6 Application to human breast cancer tissues 29
3.6.1 Sensitivity and reproducibility of phosphoproteomics profiling for human breast cancer tissues 29
3.6.2 Deep bioinformatic profiling of human breast cancer tissues 31
3.6.3 Comparison of phosphoproteomics results using different libraries 32
3.6.4 Deep coverage of breast cancer-related pathways 33
3.7 Overview of microscale tissue phosphoproteomics 34
Chapter 4 Discussion 35
Chapter 5 Conclusion 37
Chapter 6 Future perspective 38
Reference 39
參考文獻 1.Viéitez, Cristina, et al. “Towards a systematic map of the functional role of protein phosphorylation”. bioRxiv, 2019, 872770.
2.Cohen, Philip. “The role of protein phosphorylation in neural and hormonal control of cellular activity”. Nature, 296(5858), 1982, 613-620.
3.Ardito, Fatima, et al. “The crucial role of protein phosphorylation in cell signaling and its use as targeted therapy”. International journal of molecular medicine, 40(2), 2017, 271-280.
4.Sharma, Kirti, et al. “Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling”. Cell reports, 8(5), 2014, 1583-1594.
5.Lahiry, Piya, et al. “Kinase mutations in human disease: interpreting genotype–phenotype relationships”. Nature Reviews Genetics, 11(1), 2010, 60-74.
6.Harsha, H. C., and Akhilesh Pandey. “Phosphoproteomics in cancer”. Molecular oncology, 4(6), 2010, 482-495.
7.Singh, Vishakha, et al. “Phosphorylation: implications in cancer”. The protein journal, 36(1), 2017, 1-6.
8.Shien, Kazuhiko, et al. “Drug resistance to EGFR tyrosine kinase inhibitors for non-small cell lung cancer”. Acta Medica Okayama, 68(4), 2014, 191-200.
9.Wee, Ping, and Zhixiang Wang. “Epidermal growth factor receptor cell proliferation signaling pathways”. Cancers, 9(5), 2017, 52.
10.Magdeldin, Sameh, et al. “Basics and recent advances of two dimensional-polyacrylamide gel electrophoresis”. Clinical proteomics, 11(1), 2014, 1-10.
11.Gorr, Thomas A., and Johannes Vogel. “Western blotting revisited: critical perusal of underappreciated technical issues”. Proteomics–Clinical Applications, 9(3-4), 2015, 396-405.
12.Yang, Chenxi, Xuefei Zhong, and Lingjun Li. “Recent advances in enrichment and separation strategies for mass spectrometry‐based phosphoproteomics”. Electrophoresis, 35(24), 2014, 3418-3429.
13.Hogrebe, Alexander, et al. “Benchmarking common quantification strategies for large-scale phosphoproteomics”. Nature communications, 9(1), 2018, 1-13.
14.Solari, Fiorella A., et al. “Why phosphoproteomics is still a challenge”. Molecular BioSystems, 11(6), 2015, 1487-1493.
15.Jensen, Søren S., and Martin R. Larsen. “Evaluation of the impact of some experimental procedures on different phosphopeptide enrichment techniques”. Rapid Communications in Mass Spectrometry: An International Journal Devoted to the Rapid Dissemination of up‐to‐the‐Minute Research in Mass Spectrometry, 21(22), 2007, 3635-3645.
16.Yue, Xiaoshan, Alissa Schunter, and Amanda B. Hummon. “Comparing multistep immobilized metal affinity chromatography and multistep TiO2 methods for phosphopeptide enrichment”. Analytical chemistry, 87(17), 2015, 8837-8844.
17.Beltran, Luisa, and Pedro R. Cutillas. “Advances in phosphopeptide enrichment techniques for phosphoproteomics”. Amino acids, 43(3), 2012, 1009-1024.
18.Beltran, Luisa, and Pedro R. Cutillas. “Advances in phosphopeptide enrichment techniques for phosphoproteomics”. Amino acids, 43(3), 2012, 1009-1024.
19.Tsai, Chia-Feng, et al. “Immobilized metal affinity chromatography revisited: pH/acid control toward high selectivity in phosphoproteomics”. Journal of proteome research, 7(9), 2008, 4058-4069.
20.Aryal, Uma K., and Andrew RS Ross. “Enrichment and analysis of phosphopeptides under different experimental conditions using titanium dioxide affinity chromatography and mass spectrometry”. Rapid Communications in Mass Spectrometry: An International Journal Devoted to the Rapid Dissemination of Up‐to‐the‐Minute Research in Mass Spectrometry, 24(2), 2010, 219-231.
21.Bateman, Nicholas W., et al. “Maximizing peptide identification events in proteomic workflows using data-dependent acquisition (DDA)”. Molecular & Cellular Proteomics, 13(1), 2014, 329-338.
22.Zhang, Chunxia, et al. “Integration of data-dependent acquisition (DDA) and data-independent high-definition MSE (HDMSE) for the comprehensive profiling and characterization of multicomponents from Panax japonicus by UHPLC/IM-QTOF-MS”. Molecules, 24(15), 2019, 2708.
23.Kitata, R. B., Yang, J. C., & Chen, Y. J. “Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape”. Mass Spectrometry Reviews, 2022, e21781.
24.Li, Ka Wan, et al. “Recent developments in data independent acquisition (DIA) mass spectrometry: application of quantitative analysis of the brain proteome”. Frontiers in molecular neuroscience, 2020, 248.
25.Li, Jiapeng, Logan S. Smith, and Hao-Jie Zhu. “Data-independent acquisition (DIA): An emerging proteomics technology for analysis of drug-metabolizing enzymes and transporters”. Drug Discovery Today: Technologies, 2021.
26.Zhang, Fangfei, et al. “Data‐independent acquisition mass spectrometry‐based proteomics and software tools: A glimpse in 2020”. Proteomics, 20(17-18), 2020, 1900276.
27.Ludwig, Christina, et al. “Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial”. Molecular systems biology, 14(8), 2018, e8126.
28.Kitata, Reta Birhanu, et al. “A data-independent acquisition-based global phosphoproteomics system enables deep profiling”. Nature communications, 12(1), 2021, 1-14.
29.Collins, Ben C., et al. “Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry”. Nature communications, 8(1), 2017, 1-12.
30.Mantsiou, Anna, et al. “Proteomics analysis of formalin fixed paraffin embedded tissues in the investigation of prostate cancer”. Journal of Proteome Research, 19(7), 2019, 2631-2642.
31.Egeblad, Mikala, Elizabeth S. Nakasone, and Zena Werb. “Tumors as organs: complex tissues that interface with the entire organism”. Developmental cell, 18(6), 2010, 884-901.
32.Mertins, Philipp, et al. “Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography–mass spectrometry”. Nature protocols, 13(7), 2018, 1632-1661.
33.Clark, David J., et al. “Integrated proteogenomic characterization of clear cell renal cell carcinoma”. Cell, 179(4), 2019, 964-983.
34.Zhang, Hui, et al. “Integrated proteogenomic characterization of human high-grade serous ovarian cancer”. Cell, 166(3), 2016, 755-765.
35.Zhang, Bing, et al. “Proteogenomic characterization of human colon and rectal cancer”. Nature, 513(7518), 2014, 382-387.
36.Chen, Yi-Ju, et al. “Proteogenomics of non-smoking lung cancer in East Asia delineates molecular signatures of pathogenesis and progression”. Cell, 182(1), 2020, 226-244.
37.Kawashima, Yusuke, et al. “Single-Shot 10K Proteome Approach: Over 10,000 Protein Identifications by Data-Independent Acquisition-Based Single-Shot Proteomics with Ion Mobility Spectrometry”. Journal of Proteome Research, 2022.
38.Mason, Jeffrey T. “Proteomic analysis of FFPE tissue: barriers to clinical impact”. Expert Review of Proteomics, 13.9, 2016, 801-803.
39.Inamura, Kentaro. “Update on immunohistochemistry for the diagnosis of lung cancer”. Cancers, 10.3, 2018, 72.
40.Mino-Kenudson, Mari. “Immunohistochemistry for predictive biomarkers in non-small cell lung cancer”. Translational Lung Cancer Research, 6.5, 2017, 570.
41.Leighl, Natasha B., et al. “Molecular testing for selection of patients with lung cancer for epidermal growth factor receptor and anaplastic lymphoma kinase tyrosine kinase inhibitors: American Society of Clinical Oncology endorsement of the College of American Pathologists/International Association for the study of lung cancer/association for molecular pathology guideline”. Journal of Clinical Oncology, 32(32), 2014, 3673.
42.Yu, Jian, et al. “Mutation-specific antibodies for the detection of EGFR mutations in non–small-cell lung cancer”. Clinical Cancer Research,15.9, 2009, 3023-3028.
43.Cagle, Philip T., et al. “Impact of recent developments in lung cancer on the practice of pathology”. Archives of Pathology and Laboratory Medicine, 140(4), 2016, 322-325.
44.Furrer, Daniela, et al. “Advantages and disadvantages of technologies for HER2 testing in breast cancer specimens”. American journal of clinical pathology, 144(5), 2015, 686-703.
45.Kassem, Sara, et al. “Proteomics for Low Cell Numbers: How to Optimize the Sample Preparation Workflow for Mass Spectrometry Analysis”. Journal of Proteome Research, 20(9), 2021, 4217-4230.
46.Müller, Torsten, et al. “Automated sample preparation with SP 3 for low‐input clinical proteomics”. Molecular systems biology, 16(1), 2020, e9111.
47.Satpathy, Shankha, et al. “Microscaled proteogenomic methods for precision oncology”. Nature communications, 11(1), 2020, 1-16.
48.Coscia, Fabian, et al. “A streamlined mass spectrometry–based proteomics workflow for large‐scale FFPE tissue analysis”. The Journal of pathology, 251(1), 2020, 100-112.
49.Wakabayashi, Masaki, et al. “Phosphoproteome analysis of formalin-fixed and paraffin-embedded tissue sections mounted on microscope slides”. Journal of proteome research, 13(2), 2014, 915-924.
50.Ostasiewicz, Paweł, et al. “Proteome, phosphoproteome, and N-glycoproteome are quantitatively preserved in formalin-fixed paraffin-embedded tissue and analyzable by high-resolution mass spectrometry”. Journal of proteome research, 9(7), 2010, 3688-3700.
51.Margolis, Benjamin L., et al. “All autophosphorylation sites of epidermal growth factor (EGF) receptor and HER2/neu are located in their carboxyl-terminal tails: identification of a novel site in EGF receptor”. Journal of Biological Chemistry, 264(18), 1989, 10667-10671.
52.Hsuan, J. J., Nick Totty, and Michael D. Waterfield. “Identification of a novel autophosphorylation site (P4) on the epidermal growth factor receptor”. Biochemical Journal, 262(2), 1989, 659.
53.Morandell, Sandra, et al. “Quantitative proteomics and phosphoproteomics reveal novel insights into complexity and dynamics of the EGFR signaling network”. Proteomics, 8(21), 2008, 4383-4401.
54.Anderson, Neil G., et al. “ZD1839 (Iressa), a novel epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor, potently inhibits the growth of EGFR‐positive cancer cell lines with or without erbB2 overexpression”. International journal of cancer, 94(6), 2001, 774-782.
55.Guo, Yan Jun, et al. “ERK/MAPK signalling pathway and tumorigenesis”. Experimental and therapeutic medicine, 19(3), 2020, 1997-2007.
56.Gridelli, Cesare, et al. “Non-small-cell lung cancer”. Nature reviews Disease primers, 1(1), 2015, 1-16.
57.Cheng, J, Lindsley, et al. “The Akt/PKB pathway: molecular target for cancer drug discovery”. Oncogene 24, 2005, 7482–7492.
58.Yung, Hong Wa et al. “Regulation of AKT phosphorylation at Ser473 and Thr308 by endoplasmic reticulum stress modulates substrate specificity in a severity dependent manner.” PloS one, 6(3), 2011, e17894.
59.Ressa, Anna, et al. “PaDuA: a python library for high-throughput (Phospho) proteomics data analysis”. Journal of proteome research, 18(2), 2018, 576-584.
60.Yousefnia, Saghar, et al. “Mechanistic pathways of malignancy in breast cancer stem cells”. Frontiers in Oncology, 10, 2020, 452.
61.Paplomata, Elisavet, and Ruth O’Regan. “The PI3K/AKT/mTOR pathway in breast cancer: targets, trials and biomarkers”. Therapeutic advances in medical oncology, 6(4), 2014, 154-166.
62.Ostasiewicz, Paweł, et al. “Proteome, phosphoproteome, and N-glycoproteome are quantitatively preserved in formalin-fixed paraffin-embedded tissue and analyzable by high-resolution mass spectrometry”. Journal of proteome research, 9(7), 2010, 3688-3700.
63Piehowski, Paul D., et al. “Residual tissue repositories as a resource for population-based cancer proteomic studies”. Clinical proteomics, 15(1), 2018, 1-12.
指導教授 陳玉如 謝發坤 審核日期 2022-8-31
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題，請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407，或E-mail聯絡  - 隱私權政策聲明