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系統識別號 U0026-1608201214320800
論文名稱(中文) 利用IDEAL-Q、Progenesis LC-MS和MaxQuant三軟體進行與肺癌轉移相關的非標記分泌蛋白質體定量分析並比較各軟體之數據結果
論文名稱(英文) Comparative evaluation of datasets derived from label-free quantitative secretome analysis related to lung cancer metastasis using IDEAL-Q, Progenesis LC-MS and MaxQuant softwares
校院名稱 成功大學
系所名稱(中) 環境醫學研究所
系所名稱(英) Institute of Environmental and Occupational Health
學年度 100
學期 2
出版年 101
研究生(中文) 孫若齡
研究生(英文) Juo-Ling Sun
學號 S76994053
學位類別 碩士
語文別 英文
論文頁數 35頁
口試委員 指導教授-廖寶琦
召集委員-游一龍
口試委員-吳謂勝
口試委員-蕭世良
中文關鍵字 非標記定量蛋白質體分析  非標記定量軟體 
英文關鍵字 label-free quantitative proteomics analysis  label-free quantitative software 
學科別分類
中文摘要 近年來,蛋白質體學的定量分析技術在生物醫學研究上扮演著很重要的角色,藉由差異蛋白質的分析可以幫助我們了解相關生物性的變化,進而找出可應用於臨床領域之生物標籤,有鑑於此,尋找出快速、且具有良好之重複性和準確度的非標定定量策略是當前所必須的。本研究中,我們使用了目前實驗室正在進行研究的真實性複雜樣品,藉此比較近年來於比較性蛋白體學研究領域裡廣泛被使用的三個非標記定量軟體 (IDEAL-Q、Progenesis LC-MS、 MaxQuant) 之定量結果。簡而言之,實驗室於先前已收集了六個生物性重複的分泌蛋白質樣本,其來自於分別具有較低和較高侵襲能力的非小細胞肺癌細胞株CL1-0 與 CL1-5,並以液相層析串聯質譜法進行分析。根據蛋白質身分鑑定之結果得知,72.6%的蛋白質重覆出現於兩細胞株各別一半以上的樣本中。除此之外,約有68.3%的蛋白質共同存在於此兩細胞中,此結果顯示,本研究所採用的數據集於不同細胞株間,其蛋白質的組成具有良好重複性。且此三個軟體皆提供了非常友善的使用介面幫助使用者進行蛋白質和胜肽的定量分析。接著,我們進行此數據集經由不同軟體演算法的分析定量結果之比較,主要以可被定量到的蛋白質和胜肽數目、已知濃度內標準品蛋白質的定量結果,以及三個軟體間蛋白質定量結果表現量一致性分析為互相比較的準則。最後,我們並將此次非標記定量分析的結果和先前實驗室使用iTRAQ標記分析且已發表之定量結果進行比較。經由此數據結果和先前發表之文獻顯示,於此二定量蛋白質體分析策略,蛋白質定量結果具有良好之一致性 (> 60%)。
英文摘要 In recent years, the proteomics quantitative analysis plays an important role in biomedical research, analyzing the differential protein expression help us to understand the biological changes or biomarkers discoveries in clinical applications. It is necessary to discover a rapid, highly reproducible, and accurate quantitative software to do label-free quantitative proteomics research. Here we used the real complex samples that are investigated in the present lab to compare the results of the quantitative softwares (IDEAL-Q, Progenesis LC-MS and MaxQuant). Briefly, the six biological replicates of secretome samples from non-small cell lung cancer cell lines CL1-0 and CL1-5, which with low and high invasive abilities, respectively, were collected and analyzed via LC-MS/MS. Approximately, 72.6% of total identified proteins revealed in more than half samples in each cell line. Additionally, 68.3 % proteins were present in both cells. The protein identified results show that the data set had high reproducibility of protein components and good data quality. And the three softwares also provide user-friendly interfaces to facilitate protein and peptide quantitation. Next we compared the quantitation results of the datasets based on the number of quantifiable peptides/proteins, the spiked internal standard protein, and the protein expression levels. Finally, we compare the label-free quantitation results of the softwares with previously published data using iTRAQ labeling. Both the two quantitative proteomics strategy, label-free and iTRAQ labeling quantitative analysis, showed a high consistency (> 60%) of protein abundances.
論文目次 摘要..................................................I
Abstract.............................................II
Contents............................................III
List of Table........................................IV
List of Figure........................................V
Abbreviations........................................VI
Chapter 1 Overview of the Research ...................1
1-1 Mass Spectrometry-Based Label-Free Quantitative Proteomics ...........................................1
1-2 Relative Quantification by Peak Intensity of LC-MS ......................................................2
1-3 Softwares Facilitating Label-free Protein Quantitation ......................................................4
1-3-1 Freely Available Softwares for Label-free Quantitative Proteomics ...........................................4
1-3-2 Commercially Available Softwares for Label-free Quantitative Proteomics ......................................................6
1-4 The Three Label-free Quantitative Proteomics Softwares in the Research (IDEAL-Q, MaxQuant, and Progenesis LC-MS) ..................................................... 7
1-5 Introduction of Lung Cancer.......................10
1-6 The Importance of Secretome in Cancer Metastasis ......................................................10
Chapter 2 Objective ..................................11
Chapter 3 Material and Methods .......................12
3-1 Experimental Workflow ............................12
3-2 Experimental Data ................................13
3-3 Protein Identification ...........................13
3-4 Protein Quantification ...........................15
Chapter 4 Results and Discussion .....................19
4-1 Protein Identity Results..........................19
4-2 Comparisons of the Label-free Quantitation Results of the Datasets .........................................20
4-2-1 Quantifiable Proteins of the Datasets Using the Three Softwares ............................................20
4-2-2 Quantitation Results of the Internal Standard Protein, BSA ..................................................22
4-2-3 Compare the Protein Quantitation Results of the Datasets Using the Three Softwares ......................................................23
4-3 Compare the Quantitation Results with Previously Published Results Using iTRAQ Labeling ......................................................24
Chapter 5 Conclusions ................................28
Chapter 6 References .................................29
Chapter 7 Appendixes .................................34
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