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研究生:史昂德
研究生(外文):Siyal, Asad Ali
論文名稱:高靈敏度數據非依賴性採集質譜技術: 從微量蛋白體學至單細胞蛋白體學
論文名稱(外文):Highly Sensitive Data-Independent Acquisition Mass Spectrometry: From Microproteomics to Single-cell Proteomics
指導教授:陳玉如陳玉如引用關係林俊成林俊成引用關係
指導教授(外文):Chen, Yu-JuLin, Chun-Cheng
口試委員:涂熊林邱繼輝俞松良林國儀
口試委員(外文):Tu, Hsiung-LinKhoo, Kay-HooiYu, Sung-LiangLin, Kuo-I
口試日期:2021-12-27
學位類別:博士
校院名稱:國立清華大學
系所名稱:化學系
學門:自然科學學門
學類:化學學類
論文種類:學術論文
論文出版年:2021
畢業學年度:110
語文別:英文
論文頁數:154
中文關鍵詞:蛋白質體學、微量蛋白質體學、單細胞蛋白質體學、數據非依賴性採集、質譜技術蛋白質體學微量蛋白質體學單細胞蛋白質體學數據非依賴性採集質譜技術
外文關鍵詞:ProteomicsMicroproteomicsSingle-cell proteomicsData-independent acquisitionmass spectrometryProteomicsMicroproteomicsSingle-cell proteomicsData-independent acquisitionmass spectrometry
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  • 被引用被引用:0
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蛋白質質譜法是鑑定複雜生物系統中蛋白質體的一種工具。然而,深度蛋白質體分析需要大量的細胞、組織或臨床樣品,並且涉及許多處理步驟,實驗設計經常需要在可獲取的樣本大小和蛋白質體覆蓋率之間進行權衡。因此,高效率且靈敏的方法將促進蛋白質體學在微量樣品中的應用。
為了解決以上問題,我們在本論文開發了高靈敏度的蛋白質體學方法,並且應用於微量至單細胞的蛋白質體分析。首先,我們應用了Stage Tip方法來評估微克級樣品的靈敏度,從約1微克的細胞裂解液中鑑定到超過2000種蛋白質,並且具有良好再現性。接著,為了串聯微流體裝置以進行更微量蛋白質體樣品實驗,我們建立一套一鍋化微量蛋白質體樣品製備法,可對少量細胞進行深度定量蛋白質體分析(約5000顆細胞可鑑定 >4000種蛋白質)。最後,我們將該方法應用於微流體晶片(稱作iProChip),並且結合數據非依賴性採集質譜法,建立了一個精簡的微量蛋白質體實驗流程。利用晶片進行樣品製備,以及客製化的圖譜資料庫進行數據非依賴性採集圖譜比對。我們在20顆哺乳動物細胞中,平均每顆細胞可鑑定到約1,500種蛋白質,為迄今最靈敏的單細胞蛋白質體分析之一。將此方法應用至貼附和非貼附癌症細胞的分析,定量動態範圍可以穩定地到達5個數量級,並可測到重要的藥物靶點和訊號傳遞物質,每次質譜分析之間的缺失值(missing values) 小於16%。此外,我們發展了樣品大小相容圖譜資料庫(sample size-comparable spectral library)的策略,可增加少量樣品的蛋白質鑑定數。例如,在0.75奈克(約5顆細胞)和1.5奈克(約10顆細胞)可以分別鑑定2380和3586種蛋白質,擁有很高的蛋白質體覆蓋率,且具有良好再現性(三重覆分析為 86%-99%)。在圖譜相似性分析中,我們發現實驗圖譜和圖譜資料庫的碎片離子模式之相似度對於比對低豐度蛋白質扮演非常重要的作用。
總結來說,我們開發新穎的方法將蛋白質體樣品製備整合到一個小型化平台上,從而為微量蛋白質體學和單細胞蛋白質體學的應用提供進階方法。
關鍵字:蛋白質體學、微量蛋白質體學、單細胞蛋白質體學、數據非依賴性採集、質譜技術 
Mass spectrometry (MS)-based proteomics provides a tool for proteome characterization of complex biological systems. However, in-depth proteome profiling requires a large number of cells, tissues or clinical samples and involves multistep processing, linking trade-offs between high proteome coverage and accessible sample size, especially for clinical specimens. Therefore, an efficient sample preparation protocol with high analytical sensitivity will greatly facilitate the application of proteomics for low-input cells.
To address the unmet needs, we developed highly sensitive proteomic methods for microscale and single cell proteomics. In the first part, we applied a Stage-Tip based workflow to evaluate the sensitivities of microgram-level cell samples, achieving a reproducible proteome profiling of >2000 proteins from as low as ~1 µg lysate. Besides, toward implementation of a microfluidic device, we developed an optimized one-pot microproteomics protocol for deep quantitative proteome profiling of low-cell numbers (>4000 proteins from ~5000 cells). Lastly, we adopted this protocol to an integrated proteomic chip (termed iProChip) with data-independent acquisition (DIA) MS as a streamlined microproteomics pipeline. Using project-specific spectral libraries, an average ~1,500 protein groups were obtained per cell across 20 single mammalian cells, achieving one of the most sensitive single-cell profiling to date. Applying the chip-DIA workflow to adherent and non-adherent cancer cells, the method covered a dynamic range of 5 orders of magnitude with good reproducibility (<16% missing values between runs) and coverage of important druggable targets and key signaling proteins. Furthermore, we report a sample size-comparable spectral library-based strategy to enhance the microproteomic profiling of low-input samples. i.e. 2380 and 3586 protein groups from as low as 0.75 (∼5 cells) and 1.5 ng (∼10 cells), respectively, highlighting one of the highest proteome coverage with good reproducibility (86%−99% in triplicate results). Spectral similarity analysis revealed that the fragmentation ions pattern in the DIA-MS/MS spectra of the dataset and spectra library play crucial roles for mapping low abundant proteins. Taken together, the developed platform opens new avenues to bringing proteomic sample processing into a single miniaturized platform, thus providing a basis for advanced microproteomic and single-cell proteomic applications.
Keywords: Proteomics, Microproteomics, Single-cell proteomics, Data-independent acquisition, mass spectrometry
Contents

中文摘要 1
Abstract 3
Acknowledgements 5
List of Figures 11
List of Tables 16
List of Abbreviations 17
List of Publications 19
Chapter 1. Introduction 20
1.1 Introduction to Proteomics 20
1.2 Mass Spectrometry (MS)-based Proteomics 21
1.2.1 Shotgun-proteomics workflow 21
1.3 Proteomics for Biomedical Research 22
1.4 Limitations in conventional bulk-level proteomics 23
1.5 Microproteomics (<100-microgram or ~1000-105 cells) 25
1.5.1 Recent developments in Microproteomics 27
1.5.2 Single-cell proteomics 29
1.6 Data Acquisition Methods 30
1.7 Thesis objectives 35

Chapter 2. Sensitive microproteomic profiling of samples in microgram range 37
2.1 Introduction 37
2.2 Experimental Section 38
2.2.1 Materials and reagents 38
2.2.2 Cell culture 39
2.2.3 Proteomics sample preparation workflow 39
2.2.4 LC-MS/MS analysis 40
2.3 Results and Discussion 43
2.3.1 Cell Number Counting and Protein Quantitation 44
2.3.2 Evaluation of sensitivities for proteome profiling of microgram samples using iST method 46
2.3.3 One-pot microproteomics approach for proteome profiling of low-cell numbers 49

Chapter 3. Streamlined single-cell proteomics by an integrated microfluidic chip and data-independent acquisition mass spectrometry 53
3.1 Introduction 53
3.2 Experimental Section 55
3.2.1 Materials and reagents 55
3.2.2 Cell culture 56
3.2.3 Proteomics workflow using the iProChip and SciProChip 56
3.2.4 Bulk proteomics workflow 57
3.2.5 LC-MS/MS analysis 59
3.2.6 Spectral library construction 61
3.2.7 Data analysis 62
3.2.8 Data availability 63
3.3 Results and Discussion 63
3.3.1 Development of the iProChip for streamlined microproteomics workflow 63
3.3.2 Integration of iProChip with DIA MS 65
3.3.4 Evaluating the performance of iProChip DIA-MS vs DDA-MS 68
3.3.5 Quantitative proteome profiling of mass-limited samples by iProChip and DIA-MS 71
3.3.5 Application of iProChip-DIA for MEC-1 immune cell proteome profiling 81
3.3.6 Enhanced sensitivity by single-cell integrated proteomic chip (SciProChip) and DIA MS 86
3.4 Conclusions 89

Chapter 4. Sample Size-comparable Spectral Library Enhances Data-Independent Acquisition-based Proteome Coverage of Low-input Cells 93
4.1 Introduction 93
4.2 Experimental Section 96
4.2.1 Materials and reagents 96
4.2.2 Cell culture 97
4.2.3 Cell lysis and proteomic sample preparation 97
4.2.4 LC-MS/MS analysis 98
4.2.5 Spectral library construction 99
4.2.6 Data analysis (Spectronaut) 100
4.2.7 Data analysis (DIA-NN) 101
4.2.8 Data analysis (PRM-MS) 101
4.2.9 Data availability 102
4.3 Results and Discussion 102
4.3.1 Optimization of DIA-MS for Low-input Cells 102
4.3.2 Enhanced Proteome Coverage by Sample Size-comparable Spectral Library 106
4.3.3 Small-Size Library Enhances the Detection of Low-Abundant Proteins 114
4.4 Conclusions 123

Chapter 5. Conclusion and Future Perspectives 125
References 128
Appendices 146
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