請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81925完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳玉如(Yu-Ju Chen) | |
| dc.contributor.author | Yu-Heng Hsieh | en |
| dc.contributor.author | 謝宇衡 | zh_TW |
| dc.date.accessioned | 2022-11-25T03:06:46Z | - |
| dc.date.available | 2024-09-03 | |
| dc.date.copyright | 2021-11-06 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-28 | |
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Lin, Y., et al., Sodium-deoxycholate-assisted tryptic digestion and identification of proteolytically resistant proteins. Analytical Biochemistry, 2008. 377(2): p. 259-266. 51. Potel, C.M., et al., Defeating Major Contaminants in Fe(3+)- Immobilized Metal Ion Affinity Chromatography (IMAC) Phosphopeptide Enrichment. Mol Cell Proteomics, 2018. 17(5): p. 1028-1034. 52. Shevchenko, G., et al., Comparison of extraction methods for the comprehensive analysis of mouse brain proteome using shotgun-based mass spectrometry. J Proteome Res, 2012. 11(4): p. 2441-51. 53. Lin, Y., et al., Sodium laurate, a novel protease- and mass spectrometry-compatible detergent for mass spectrometry-based membrane proteomics. PLoS One, 2013. 8(3): p. e59779. 54. Ye, J., et al., Optimized IMAC-IMAC protocol for phosphopeptide recovery from complex biological samples. J Proteome Res, 2010. 9(7): p. 3561-73. 55. Gustafsson, O.J., G. Arentz, and P. Hoffmann, Proteomic developments in the analysis of formalin-fixed tissue. Biochim Biophys Acta, 2015. 1854(6): p. 559-80. 56. Dapic, I., et al., Proteome analysis of tissues by mass spectrometry. Mass Spectrom Rev, 2019. 38(4-5): p. 403-441. 57. Manickavasagar, T., et al., HER3 expression and MEK activation in non-small-cell lung carcinoma. Lung Cancer Management, 2021. 10(2): p. LMT48. 58. Harada, D., N. Takigawa, and K. Kiura, The Role of STAT3 in Non-Small Cell Lung Cancer. Cancers (Basel), 2014. 6(2): p. 708-22. 59. Donnem, T., et al., Prognostic impact of platelet-derived growth factors in non-small cell lung cancer tumor and stromal cells. J Thorac Oncol, 2008. 3(9): p. 963-70. 60. Zhang, J., et al., SRC-family kinases are activated in non-small cell lung cancer and promote the survival of epidermal growth factor receptor-dependent cell lines. The American journal of pathology, 2007. 170(1): p. 366-376. 61. Yang, Y., et al., In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics. Nature Communications, 2020. 11(1): p. 146. 62. Giusti, L., C. Angeloni, and A. Lucacchini, Update on proteomic studies of formalin-fixed paraffin-embedded tissues. Expert Review of Proteomics, 2019. 16(6): p. 513-520. 63. O'Rourke, M.B. and M.P. Padula, Analysis of formalin-fixed, paraffin-embedded (FFPE) tissue via proteomic techniques and misconceptions of antigen retrieval. Biotechniques, 2016. 60(5): p. 229-38. 64. Strom, S.P., Fundamentals of RNA Analysis on Biobanked Specimens. Methods Mol Biol, 2019. 1897: p. 345-357. 65. Arreaza, G., et al., Pre-Analytical Considerations for Successful Next-Generation Sequencing (NGS): Challenges and Opportunities for Formalin-Fixed and Paraffin-Embedded Tumor Tissue (FFPE) Samples. International journal of molecular sciences, 2016. 17(9): p. 1579. 66. Donczo, B. and A. Guttman, Biomedical analysis of formalin-fixed, paraffin-embedded tissue samples: The Holy Grail for molecular diagnostics. J Pharm Biomed Anal, 2018. 155: p. 125-134. 67. Zhu, Y., et al., High-throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification. Mol Oncol, 2019. 68. Roskoski, R., Jr., Properties of FDA-approved small molecule protein kinase inhibitors: A 2020 update. Pharmacol Res, 2020. 152: p. 104609. 69. Kohale, I.N., et al., Quantitative analysis of tyrosine phosphorylation from FFPE tissues reveals patient specific signaling networks. bioRxiv, 2020: p. 2020.09.10.291922. 70. Aballo, T.J., et al., Ultrafast and Reproducible Proteomics from Small Amounts of Heart Tissue Enabled by Azo and timsTOF Pro. J Proteome Res, 2021. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81925 | - |
| dc.description.abstract | "蛋白磷酸化是重要轉譯後修飾之一，能調節體內細胞訊息傳遞及生物功能。先進的質譜與大規模胜肽分離技術已成為一種深具潛力涵括全基因體深度磷酸化蛋白體分析工具。然而，目前要獲得臨床檢體枝深度磷酸化蛋白體分析仍然需要相當大的起始量（>50毫克腫瘤組織及>200微克胜肽用於磷酸化胜肽萃取）以及耗時的樣品製備、質譜數據採集與資料處理。為了增進具全基因體深度之組織磷酸化蛋白體分析，我們開發一種高靈敏且流程精簡的磷酸化蛋白體樣品製備並應用於微量組織檢體（<10毫克組織），如抽吸針組織活檢及石蠟包埋組織切片等。 首先，我們使用新鮮老鼠肺臟組織來優化組織蛋白萃取，並在優化樣品處理過程以省略去除界面活性劑步驟減少樣品損失。我們選擇三種組織裂解液分別為月桂酸鈉、尿素和乙腈，並加入TCEP與CAA達到快速還原/烷基化。相較於尿素(9%)和乙腈(4%)蛋白萃取率，利用月桂酸鈉進行組織裂解可顯著提升蛋白萃取率達11%。此外，與用尿素與乙腈來裂解組織進行磷酸化蛋白體分析相比，使用月桂酸鈉顯著增加了磷酸化蛋白體鑑定深度達9,539條磷酸化胜肽（尿素：6,389條磷酸化胜肽；乙腈：2,248條磷酸化胜肽）。以5毫克到0.5毫克老鼠肺組織評估月桂酸鈉裂解方法的靈敏度，此方法可獲取>10%的蛋白萃取率，並證實微量組織利用月桂酸鈉組織裂解法可獲得較高蛋白萃取率。 第二個部分則是優化控制酸鹼度之固向化金屬親和層析磷酸化胜肽萃取法以精簡實驗流程。我們將6%醋酸(pH 3.0)樣本緩衝液代替為80% ACN, 0.1% TFA，進而省略磷酸化胜肽萃取中的樣本緩衝液置換步驟。我們利用30微克老鼠肺臟組織胜肽進行測試，使用80% ACN, 0.1% TFA能鑑定5,969條磷酸化胜肽，並將萃取專一性提高到99%且具有高再現性（Pearson 相關係數：0.912-0.949）。此外，去除樣本緩衝液置換步驟可以減少>4小時樣本製備時間。在0.5毫克組織中，此優化磷酸化樣品製備流程鑑定6,698條磷酸化胜肽和99%萃取專一性，有高再現性。綜上所述，我們優化後的磷酸化蛋白體樣本製備流程應用於微量組織樣品，具有高再現性，整體製備流程顯著縮短至1天內。 為了在微量組織中進行深度磷酸化蛋白體學分析，我們將single-shot非數據依賴擷取 (Data-Independent Acquisition，DIA)方法納入上述磷酸化蛋白體製備方法。我們以50微克老鼠肺臟組織胜肽評估DIA方法。利用DIA方法鑑定>32,000條磷酸化胜肽和14,000個class 1磷酸化位點。與DDA方法相比，DIA方法提升三倍磷酸化肽鑑定數和兩倍可定量之磷酸化位點。在DIA方法中，所有可定量磷位點的信號動態範圍超過6個數量級，而數據依賴擷取方法(Data-dependent acquisition, DDA)僅為4個級數，此結果證明DIA 方法具有更廣的動態範圍，可以檢測低含量的磷酸化胜肽。與DDA方法具43%資料缺失相比，DIA方法僅有12%資料缺失，能進一步提升多重樣品的定量再現性。我們同時也優化液相層析梯度，並用於微量組織磷酸化蛋白體學分析，加以提高樣品分析效率。使用120分鐘液相層析梯度，可鑑定超過30,000條磷酸化胜肽（超過14,000個class 1磷酸化位點），其中涵蓋可應用於非小細胞肺癌標靶治療之16個藥物靶點。 最後，我們將優化的磷酸化蛋白體學實驗流程和 DIA 方法應用於人類肺癌石蠟組織包埋切片(FFPE section)。在利用libDIA以及 directDIA 的結果中，鑑定出 >15,000條磷酸化胜肽，在非小細胞肺癌訊息傳遞路徑（16個磷酸化蛋白，71個磷酸位點）和人類激酶體（164個激酶）中具有高覆蓋率。與先前研究相比，我們的優化方法將磷酸化蛋白體學深度提升至>10,000條磷酸化胜肽，並鑑定與定量出6個肺癌藥物靶點。我們的結果證明深度磷酸化蛋白體學分析運用在石蠟組織包埋切片的實用性。總體而言，此樣品製備和分析流程具有高度再現性和靈敏度，可應用於微量組織進行深度磷酸化蛋白質體學分析。 " | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T03:06:46Z (GMT). No. of bitstreams: 1 U0001-2109202119395600.pdf: 3477318 bytes, checksum: 643357b68df298a153d225fc67f65604 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "中文摘要 i Abstract iii 目錄 vi 圖目錄 viii 表目錄 x Chapter 1 Introduction 1 1.1 Significance of protein phosphorylation and phosphoproteomics 1 1.2 Challenge of phosphoproteomic profiling 2 1.3 Sample preparation approaches for phosphoproteomics 3 1.4 Data acquisition strategies by LC-MS/MS 5 1.4.1 Data dependent acquisition in phosphoproteomics 5 1.4.2 Data independent acquisition (DIA) for phosphoproteomics 7 1.5 Current status of tissue phosphoproteomics 8 1.6 Challenge in microscale tissue phosphoproteome. 10 1.7 Objectives of this study 12 Chapter 2 Materials and Method 15 2.1 Chemical and Material 15 2.2 Cell culture 15 2.3 Cell line sample preparation 15 2.4 Tissue sample preparation 17 2.4.1 Fresh frozen tissue 17 2.4.2 Formalin-fixed, paraffin-embedded (FFPE) tissue 18 2.5 Peptide desalting by stage-tip 19 2.5.1 SDB-XC stage-tip desalting 19 2.5.2 C18 stage-tip desalting 19 2.6 Phosphopeptide enrichment 19 2.6.1 pH control IMAC phosphopeptide enrichment method 20 2.6.2 Optimized IMAC phosphopeptide enrichment method 20 2.7 Liquid chromatography‐mass spectrometry analysis 21 2.7.1 Data dependent acquisition method (DDA) 21 2.7.2 Data independent acquisition method (DIA) 21 2.8 Phosphoproteome identification and quantification 22 2.9 Bioinformatics analysis 23 Chapter 3 Results 25 3.1. Optimization of tissue phosphoproteomic sample preparation 25 3.1.1 Choice of detergent for optimal protein extraction efficiency 25 3.1.2 High selectivity and reproducibility of Fe-IMAC phosphopeptide enrichment 27 3.1.3 Comparison of phosphopeptide enrichment protocols 31 3.1.4 Evaluation of optimal tissue sample preparation pipeline for microscale tissue analysis 33 3.2. Deep microscale tissue phosphoproteome by data-independent acquisition 36 3.2.1 Evaluate DDA and DIA for tissue phosphoproteomic profiling 36 3.2.2 Reduce gradient of LC-MS/MS analysis 39 3.2.3 Comparison of benchmark protocol 42 3.3. Application of formalin-fixed, paraffin-embedded (FFPE) tissue sample 46 Chapter 4 Discussion 50 Chapter 5 Conclusion 53 Reference 54" | |
| dc.language.iso | en | |
| dc.subject | 固定金屬離子親和性層析 | zh_TW |
| dc.subject | 微量組織 | zh_TW |
| dc.subject | 磷酸化蛋白體學 | zh_TW |
| dc.subject | 數據非依賴性採集質譜 | zh_TW |
| dc.subject | Phosphoproteomics | en |
| dc.subject | Microscale tissue | en |
| dc.subject | Data independent acquisition (DIA) mass spectrometry | en |
| dc.subject | Immobilized metal affinity chromatography (IMAC) | en |
| dc.title | 開發磷酸化蛋白體質譜法應用於微量組織 | zh_TW |
| dc.title | Developing mass spectrometry-based phosphoproteomics for microscale tissue | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 俞松良 (Hsin-Tsai Liu),涂熊林(Chih-Yang Tseng) | |
| dc.subject.keyword | 微量組織,磷酸化蛋白體學,固定金屬離子親和性層析,數據非依賴性採集質譜, | zh_TW |
| dc.subject.keyword | Microscale tissue,Phosphoproteomics,Immobilized metal affinity chromatography (IMAC),Data independent acquisition (DIA) mass spectrometry, | en |
| dc.relation.page | 92 | |
| dc.identifier.doi | 10.6342/NTU202103270 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-09-29 | |
| dc.contributor.author-college | 生命科學院 | zh_TW |
| dc.contributor.author-dept | 基因體與系統生物學學位學程 | zh_TW |
| dc.date.embargo-lift | 2024-09-03 | - |
| 顯示於系所單位： | 基因體與系統生物學學位學程 | |
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| U0001-2109202119395600.pdf | 3.4 MB | Adobe PDF | 檢視/開啟 |
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