相較於其他的物系，人類蛋白質體由於在同一時間中所能表現的蛋白質種類極多，再加上蛋白質異構物(isoforms)與轉譯後修飾作用(post-translational modifications)，其複雜性難以估計。為了增加完整蛋白質體鑑定深度，發展更靈敏的蛋白質體學分析方法仍然是必須的。過去十年，質譜法已成功地大規模鑑定與定量分析細胞內的蛋白質體；此外，數據依賴擷取(data dependent acquisition)，多重反應監測(multiple reaction monitoring)與近期蓬勃發展的非數據依賴擷取(data independent acquisition)等方法，更提供研究生物系統的途徑。在本篇研究中，我們發展可以全面性分析蛋白質體的方法
在3000個缺乏蛋白質表現證據的蛋白質譯碼基因(protein coding genes)中，我們發現超過三分之一被預測為膜蛋白。其原因可能為分析膜蛋白質的困難包括溶解試劑干擾、含量較低、並且較不易被蛋白酶水解。本研究的第一部分，我們利用一個快速且靈敏的高pH 逆向層析(HpRP)StageTip技術進行胜肽的分離。結合數據依賴擷取法(四極柱串聯時間飛行式質譜儀)，分11個非小細胞肺癌(NSCLC)細胞株與20對肺癌組織。於小於1%胜肽圖譜數(PSM)、胜肽與蛋白質錯誤鑑定率(FDR )下，我們鑑定到的7702蛋白質中(66%為膜蛋白)，其中發現178個“失蹤蛋白質”(74個為膜蛋白)。於178個失蹤蛋白質中，78%具有mRNA表現數據。更進一步利用以合成胜肽及多重反應監測質譜法驗證8個蛋白質。此研究證實癌症膜蛋白質體為探勘失蹤蛋白質之好策略，幫助完整人類蛋白質體。
單一注射蛋白質體分析法具快速及靈敏之優點，是大量分析臨床樣品的常用方法。於第二部分，我們以HeLa 細胞為模版，在傅立葉轉換高場離子阱(Orbitrap)質譜儀中，以數據依賴擷取模式(DDA)進行深度蛋白質體鑑定分析。利用6 µg HeLa細胞的水解胜肽，在約10小時的優化分析中，鑑定到約6000個蛋白質(<1%錯誤率)。為了進一步增加鑑定深度，我們利用HpRP分群技術，在20 µg的水解胜肽中鑑定到約8000個蛋白質。結合單一注射蛋白質體分析法與HpRP分群技術，我們建立約8000個具有高可信度的圖譜資料庫。
此研究的第三部分專注於非數據依賴擷取(DIA)方法學的開發。非數據依賴擷取法目標為在大量的蛋白質樣品中，於特定之母離子(precursor)質量範圍間，收集所有可以偵測到的胜肽並且得到其二次質譜（MS/MS）分析圖譜。非數據依賴擷取法可以無偏記錄子離子(fragment)圖譜，並且因為提供良好了定量準確性而被注意。儘管對於非數據依賴擷取法已經有一些新軟體分析的報導，但目前還沒有標準數據擷取與分析方法可運用於高質量精確度質譜儀的資料分析。與一般使用的25道爾頓(Da)母離子擷取範圍相比，我們發現當擷取範圍降低至10 Da，利用優化質量範圍與二次質譜解析度之方法，可提升15%蛋白質與22%胜肽鑑定率。 利用前述數據依賴擷取模式所建立的圖譜資料庫(約8000個蛋白質)，在單次約2小時梯度時間的非數據依賴擷取法分析中，定量約5000個蛋白質(變異係數(CV)小於20%)。相較已發表文獻，我們的單次非數據依賴擷取模式分析提供了最深的蛋白質體鑑定，此方法可以應用於任何細胞株與組織檢體的分析。
本研究的最後一部分，我們結合發展的StageTip蛋白質水解法與IMAC純化磷酸化胜肽技術，將非數據依賴擷取模式分析法進一步應用極優化於磷酸化蛋白質體的分析，於兩組非小細胞肺癌細胞株PC9(EGFR抑制劑敏感性)與CL68(EGFR抑制劑抗藥性)中，進行，我們建立了包含來自6905磷酸化蛋白質的44247條磷酸化胜肽的數據依賴擷取圖譜資料庫，由兩組細胞株中所純化出來的磷酸化胜肽，在單次約2小時梯度時間的非數據依賴擷取法分析下，我們可以定量來自4800磷酸化蛋白質的28000條磷酸化胜肽且變異係數(CV)小於20%。在抗藥性細胞株中大量表現的磷酸化蛋白質中，多個磷酸化位置已經被報導過與癌症有關，如: ERK/MAPK、HGF訊息傳遞路徑。EGFR上的20個磷酸化位置也在CL68細胞株中有顯著過度表現。此結果提供了解抗藥性的訊息傳遞路徑與探勘可能的藥物治療標的。另一方面，建立高品質的蛋白質體與磷酸化蛋白質體圖譜資料庫，將可應用於非數據依賴擷取方法學於各式生化樣品的分析。

Compared to the genome, the complexity of human proteome due to daunting number of proteins expressed at a given time, presence of multiple protein isoforms, and post-translational modifications, large scale proteomic analysis still needs improvement toward full identification coverage of human proteome. The depth of identification coverage and acquisition rate of mass spectrometer (MS)based proteomics for qualitative and quantitative profiling of cellular proteome has advanced in the past decade. In addition to the well-established discovery data dependent acquisition (DDA) mode, targeted multiple reaction monitoring (MRM) and recently invented data independent acquisition (DIA) methods were among important developments, providing alternative approaches to study the biological system. In this thesis, we aim to develop strategies towards comprehensive proteomic profiling.
Among the over 3000 protein coding genes lacking protein expression evidence, which are called “missing protein”, we found that over one-third are predicted to be membrane proteins, presumably due to analytical challenges for membrane proteins, including difficulty of solubilization by detergents, low abundance of membrane proteins and accessibility to protease digestion. In the first part of this thesis, for comprehensive membrane protein coverage, we employed a rapid and sensitive high pH reversed phase stop-and-go extraction tip (StageTip) for efficient peptide fractionation of membrane proteins from 11 non-small cell lung cancer cell (NSCLC) lines. The dataset of 20 pairs of lung cancer tissues membrane sample analysis without fractionation was also included. Among the identified 7702 proteins (66% membrane proteins), we found 178 “missing proteins” (74 membrane proteins) with peptide spectral matches (PSMs)-, peptide-, and protein-level FDR of 1% using Q-TOF MS in DDA mode. Among these 178 missing protein, 78% possessed mRNA expression data. By further validation using targeted acquisition MRM, 8 proteins were verified comparing the fragmentation spectra and retention time to corresponding synthetic peptide. This study demonstrates that mining missing proteins from cancer membrane subproteome can complementarily contribute to mapping the whole human proteome.
Single shot proteome analysis are of interest due to minimum sample consumption and fast high throughput nature particularly applicable to analyze large number of clinical samples. In the second part of the thesis, using HeLa cell line as a model, we further evaluated the depth of proteome identification using DDA by optimizing chromatographic separations on Orbitrap instruments. Using 6 µg HeLa peptide digest, best performance of single shot loading was achieved with identification of ~6000 proteins at 1% FDR over extended acquisition time of ~10 hr. To enhance the identification depth, we also employed our previously developed HpRP for peptide fractionation and obtained ~8000 proteins from 20 µg peptide digest. Combining single shot and HpRP fractions, we were able to build spectral library from high confidence identification of nearly 8000 proteins.
The third part of the thesis focus on the development of data independent acquisition (DIA) method. DIA acquisition aims at obtaining MS/MS spectra of all detectable peptides in specified precursor mass range to retain large number of proteins. The unbiased recording of fragment spectra from DIA has been found to be attractive approach for reproducible quantitation. Despite the several reports on new software and acquisition approaches, standardized method of data acquisition and analysis were not developed particularly with application of the method on high mass accuracy LTQ Orbitrap MS instruments. We found that narrow 10 Da DIA precursor isolation window over optimized mass range and MS/MS resolution identified ~15% more proteins and ~22% more peptides compared to the wider 25 Da window commonly used. Using spectral library (~8000 proteins) built from DDA, we were able to quantify ~5000 proteins within 20% CV in single shot DIA acquisition using ~2 hr gradient time from HeLa cell. Our study provided so far the deepest proteome profiling by a single shot-DIA analysis using DIA library which can be used for other cell lines or tissue samples.
In the last part of the thesis, the DIA approach was extended to phosphoproteomic profiling of two NSCLC cell lines PC9 (EGFRDel1，TKI sensitive) and CL68 (EGFRT790M/Del19，TKI resistant). Using our newly developed StageTip based protein digestion and peptide fractionation followed by IMAC enrichment, we built a comprehensive DDA spectral library of 44,247 phosphopeptides from 6905 phosphoproteins. Using single shot DIA acquisition (~2 hr gradient time) to analyze phosphopeptides enriched from the two cell lines, we were able to quantify at the depth of ~28,625 and 27,937 phosphopeptides from 4768 and 4827 phodphoproteins for PC9 and CL68, respectively, within 20% coefficient of variation. Quantitative comparison of these NSCLC lines identified several phosphosites on proteins up-regulated in cancer signaling pathways such as ERK/MAPK, HGF and others in resistant CL68 cell lines. For example, 20 phosphosites of EGFR were found to be significantly upregulated in CL 68 cell lines. The result may provide insight for resistance-related pathway and mining druggable target to overcome drug resistance.
In summary, the high quality spectral library generated for proteome and phosphoproteome in this study can be a valuable resource for proteomic community to apply DIA data analysis in various samples.

Acknowledgement....................................................i
摘要........................................................... ii
Abstract....................................................... v
Table of Contents.............................................. ix
List of Figures................................................ xi
List of Tables................................................. xiii
Chapter 1 Introduction......................................... 1
1.1 Mass Spectrometry Based Proteomics......................... 1
1.2 Deep Proteome Profiling of Human Proteome.................. 4
1.3 Current Status of Human Proteome........................... 8
1.4 Objectives of the Thesis................................... 9
Chapter 2 Materials and Methods................................ 12
2.1 Materials and Reagents..................................... 12
2.2 Cell Culture, Lysis and Tissue Collection.................. 13
2.3 Extraction and Digestion of Membrane Protein from NSCLC Cell Lines.......................................................... 13
2.4 Membrane Protein Extraction and Digestion from Tissue...... 14
2.5 Hp-RP StageTip Fractionation............................... 15
2.6 Cell culture and lysis for Phosphoproteome analysis........ 16
2.7 StageTip-based Digestion for Phosphoproteme................ 16
2.8 Phosphopeptide Enrichment by IMAC.......................... 17
2.9 LC-MS/MS Analysis of Membrane Proteome..................... 17
2.10 MRM Method Development, Optimization and Acquisition...... 18
2.11 LC-MS/MS Analysis of Phosphoproteome...................... 19
2.12 LC-MS/MS Analysis for HeLa Proteome....................... 20
2.13 Database Search for Missing Membrane Proteome from Q-TOF Files.......................................................... 21
2.14 Database Search for Orbitrap Fusion and Fusion Lumos Files.......................................................... 23
2.15 Annotations of Cellular Localization, Family and Missing Protein........................................................ 23
2.16 DIA Data Processing by DIA-Umpire......................... 23
2.17 DIA Data Processing by Spectronaut........................ 24
Chapter 3 Mining Missing Membrane Proteins..................... 25
3.1 Strategies for Mining Missing Proteins..................... 25
3.2 Membrane Proteome Profiling Strategies of NSCLC Cell and Tissue......................................................... 26
3.3 Proteome and Membrane Proteome Identification.............. 28
3.4 Missing Protein Annotation................................. 30
3.5 Membrane Protein and Family Annotation of Missing Proteins. 32
3.6 Validation of Missing Membrane Proteins by Multiple Reaction Monitoring..................................................... 32
Chapter 4. Optimization of Data Dependent Acquisition.......... 36
4.1 Single-Shot Data Dependent Acquisition Evaluation.......... 36
4.2 Improved Chromatographic Separation for Single Shot Analysis....................................................... 37
4.3 Peptide Fractionation by HpRP to enhance depth of Proteome Profiling...................................................... 38
Chapter 5. Deep Proteomic Profiling using Data Independent Acquisition.................................................... 40
5.1 Developments in Data Independent Acquisition............... 40
5.2 Strategies of Data Independent Acquisition Analysis........ 43
5.3 Optimization of Scanning Window, Precursor Mass Range and Resolution..................................................... 45
5.3.1 DIA Isolation Window..................................... 47
5.3.2 Precursor Mass Scanning Range............................ 48
5.3.3 MS/MS Resolution......................................... 49
5.4 Proteomic Identification from Data Independent Acquisition. 50
5.5 Construction of Spectral Library for Targeted Data Independent Acquisition.................................................... 51
5.6 Targeted Data Extraction Strategy.......................... 53
5.7 Effect of Spectral Library Size and Composition on Quantitation Depth.......................................................... 54
5.8 Comparison of DDA and DIA Quantitation..................... 56
Chapter 6. Phosphoproteome Analysis using Data Independent Acquisition.................................................... 58
6.1 Strategies for Protein Phosphorylation Analysis............ 58
6.2 Phosphoproteome Analysis by SWATH.......................... 60
6.3 Construction of Phosphoproteome Spectral Library........... 61
6.4 Phosphoproteome analysis of Non-Small Cell Lung Cancer Cell Lines.......................................................... 63
Chapter 7 Conclusion........................................... 66
Chapter 8 References........................................... 69

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