臺灣博碩士論文加值系統

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

1. Aebersold, Ruedi, and Matthias Mann. "Mass-spectrometric exploration of proteome structure and function." Nature 537.7620 (2016): 347-355.
2. Graves, Paul R., and Timothy AJ Haystead. "Molecular biologist's guide to proteomics." Microbiology and molecular biology reviews 66.1 (2002): 39-63.
3. Wilkins, M.R., Pasquali, C., Appel, R.D., Ou, K., Golaz, O., Sanchez, J.C., Yan, J.X., Gooley, A.A., Hughes, G., Humphery-Smith, I. and Williams, K.L. "From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and arnino acid analysis". Nat Biotechnol 14.1 (1996): 61-65.
4. Wilkins, M. R., Sanchez, J. C., Gooley, A. A., Appel, R. D., Humphery-Smith, I., Hochstrasser, D. F., & Williams, K. L. "Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it." Biotechnology and genetic engineering reviews 13.1 (1996): 19-50.
5. Abbott, Alison. "A post-genomic challenge: learning to read patterns of protein synthesis." Nature 402.6763 (1999): 715-720.
6. Aebersold, R., Mann, M. "Mass spectrometry-based proteomics". Nature 422.6928 (2003): 198-207.
7. Washburn, M., Wolters, D. and Yates, J. "Large-scale analysis of the yeast proteome by multidimensional protein identification technology". Nat Biotechnol 19.3 (2001): 242-247.
8. Wolters, Dirk A., Michael P. Washburn, and John R. Yates. "An automated multidimensional protein identification technology for shotgun proteomics." Analytical chemistry 73.23 (2001): 5683-5690.
9. Domon, Bruno, and Ruedi Aebersold. "Mass spectrometry and protein analysis." science 312.5771 (2006): 212-217.
10. Thakur, S. S., Geiger, T., Chatterjee, B., Bandilla, P., Fröhlich, F., Cox, J., and Mann, M. "Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation." Molecular & cellular proteomics 10.8 (2011).
11. Low, T. Y., van Heesch, S., van den Toorn, H., Giansanti, P., Cristobal, A., Toonen, P., Schafer, S., Hubner, N., Breukelen, B., Mohammed, S., Cuppen, E., Heck, A, and Guryev, V."Quantitative and qualitative proteome characteristics extracted from in-depth integrated genomics and proteomics analysis." Cell reports 5.5 (2013): 1469-1478.
12. Wilhelm, M., Schlegl, J., Hahne, H., Gholami, A. M., Lieberenz, M., Savitski, M. M., Ziegler, E., Butzmann, L., Gessulat, S., Marx, H., Mathieson, T., Lemeer, S., Schnatbaum, K., Reimer, U., Wenschuh, H., Mollenhauer, M., Slotta-Huspenina, J., Boese, J-K., Batnscheff, M., Gerstmair, A., Fareber, A, and Kuster, B. "Mass-spectrometry-based draft of the human proteome". Nature 509, (2014): 582–587.
13. Bantscheff, M., Schirle, M., Sweetman, G., Rick, J., & Kuster, B. "Quantitative mass spectrometry in proteomics: a critical review". Anal Bioanal Chem 389, (2007): 1017–1031.
14. Steen, H., Mann, M. "The abc's (and xyz's) of peptide sequencing". Nat Rev Mol Cell Biol 5, (2004): 699–711.
15. Boersema, Paul J., Abdullah Kahraman, and Paola Picotti. "Proteomics beyond large-scale protein expression analysis." Current opinion in biotechnology 34 (2015): 162-170.
16. Schubert, O. T., Röst, H. L., Collins, B. C., Rosenberger, G., and Aebersold, R. "Quantitative proteomics: challenges and opportunities in basic and applied research". Nat Protoc 12, (2017): 1289–1294.
17. Rieckmann, J.C., Geiger, R., Hornburg, D., Wolf, T., Kveler, K., Jarrossay, D., Sallusto, F., Shen-Orr, S.S., Lanzavecchia, A., Mann, M. and Meissner, F. "Social network architecture of human immune cells unveiled by quantitative proteomics". Nat Immunol 18, (2017): 583–593.
18. Lapek, J. D., Greninger, P., Morris, R., Amzallag, A., Pruteanu-Malinici, I., Benes, C. H., and Haas, W. "Detection of dysregulated protein-association networks by high-throughput proteomics predicts cancer vulnerabilities". Nat Biotechnol 35, (2017): 983–989.
19. Zhang, H., Liu, T., Zhang, Z., Payne, S.H., Zhang, B., McDermott, J.E., Zhou, J.Y., Petyuk, V.A., Chen, L., Ray, D. and Sun, S."Integrated proteogenomic characterization of human high-grade serous ovarian cancer." Cell 166.3 (2016): 755-765.
20. Mertins P, Mani DR, Ruggles KV, Gillette MA, Clauser KR, Wang P, Wang X, Qiao JW, Cao S, Petralia F, Kawaler E. "Proteogenomics connects somatic mutations to signaling in breast cancer". Nature 534, (2016): 55–62.
21. Chen, Y. J., Roumeliotis, T. I., Chang, Y. H., Chen, C. T., Han, C. L., Lin, M. H., Chen, H.W., Chang, G.C., Chang, Y.L., Wu, C.T., Lin, M.W., Hsieh, M.S., Wang, Y.T., Chen, Y.T., Jonassen, I., Ghavidel, F.Z., Lin, Z.S., Lin, K.T., Chen, C.W., Sheu, P.Y., Hung, C.T., Huang, K.C., Yang, H.C., Lin, P.Y., Yen, T.C., Lin, Y.W., Wang, J.H., Raghav, L., Lin, C.Y., Chen, Y.S., Wu, P.S., Lai, C.T., Weng, S.H., Su, K.Y., Chang, W.H., Tsai, P.Y., Robles, A.I., Rodriguez, H., Hsiao, Y.J., Chang, W.H., Sung, T.Y., Chen, J.S., Yu, S.L., Choudhary, J.S., Chen, H.Y., Yang, P.C, and Chen, Y.J. "Proteogenomics of non-smoking lung cancer in East Asia delineates molecular signatures of pathogenesis and progression." Cell 182.1 (2020): 226-244.
22. Mertins, P., Tang, L.C., Krug, K., Clark, D.J., Gritsenko, M.A., Chen, L., Clauser, K.R., Clauss, T.R., Shah, P., Gillette, M.A., Petyuk, V.A., Thomas, S.N., Mani, D.R., Mundt, F., Moore, R.J., Hu, Y., Zhao, R., Schnaubelt, M., Keshishian, H., Monroe, M.E., Zhang, Z., Udeshi, N.D., Mani, D., Davies, S.R., Townsend, R.R., Chan, T.W., Smith, R.D., Zhang, H., Liu, T, and Carr, S.A. "Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography–mass spectrometry". Nat Protoc 13, (2018): 1632–1661.
23. Satpathy, S., Jaehnig, E.J., Krug, K., Kim, B.J., Saltzman, A.B., Chan, D.W., Holloway, K.R., Anurag, M., Huang, C., Singh, P., Gao, A., Namai, N., Dou, Y., Wen, Bo., Vasaikar, SV., Mutch, D., Watson, M.A., Ma, C., Ademuyiwa, F.O., Rimawi, M.F., Schiff, R., Hoog, J., Jacobs, S., Malovannaya, A., Hyslop, T., Clauser, K.R., Mani, D.R., Perou, C.M., Miles, G., Zhang, Bing., Gillete, M.A., Carr, S.A, and Ellis, M.J. . "Microscaled proteogenomic methods for precision oncology". Nat Commun 11, (2020): 532.
24. Branca, Rui MM, Lukas M. Orre, Henrik J. Johansson, Viktor Granholm, Mikael Huss, Åsa Pérez-Bercoff, Jenny Forshed, Lukas Käll, and Janne Lehtiö. "HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics". Nat Methods 11, (2014): 59–62.
25. Muntel, J., Gandhi, T., Verbeke, L., Bernhardt, O. M., Treiber, T., Bruderer, R., and Reiter, L. "Surpassing 10000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy." Molecular omics 15.5 (2019): 348-360.
26. Zubarev, Roman A. "The challenge of the proteome dynamic range and its implications for in‐depth proteomics." Proteomics 13.5 (2013): 723-726.
27. Budnik, B., Levy, E., Harmange, G. and Slavov, N."SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation". Genome Biol 19, (2018): 161.
28. Bekker-Jensen, D.B., Martínez-Val, A., Steigerwald, S., Rüther, P., Fort, K.L., Arrey, T.N., Harder, A., Makarov, A. and Olsen, J.V. "A compact quadrupole-orbitrap mass spectrometer with FAIMS interface improves proteome coverage in short LC gradients." Molecular & Cellular Proteomics 19.4 (2020): 716-729.
29. Feist, Peter, and Amanda B. Hummon. "Proteomic challenges: sample preparation techniques for microgram-quantity protein analysis from biological samples." International journal of molecular sciences 16.2 (2015): 3537-3563.
30. Vitrinel, B., Iannitelli, D.E., Mazzoni, E.O., Christiaen, L. and Vogel, C."Simple method to quantify protein abundances from 1000 Cells." ACS omega 5.25 (2020): 15537-15546.
31. Alexovič, Michal, Ján Sabo, and Rémi Longuespée. "Microproteomic sample preparation." Proteomics 21.9 (2021): 2000318.
32. Couvillion, S.P., Zhu, Y., Nagy, G., Adkins, J.N., Ansong, C., Renslow, R.S., Piehowski, P.D., Ibrahim, Y.M., Kelly, R.T. and Metz, T.O. "New mass spectrometry technologies contributing towards comprehensive and high throughput omics analyses of single cells." Analyst 144.3 (2019): 794-807.
33. Gutstein, H.B., Morris, J.S., Annangudi, S.P. and Sweedler, J.V. "Microproteomics: analysis of protein diversity in small samples." Mass spectrometry reviews 27.4 (2008): 316-330.
34. Kasuga, K., Katoh, Y., Nagase, K. and Igarashi, K. "Microproteomics with microfluidic‐based cell sorting: Application to 1000 and 100 immune cells." Proteomics 17.13-14 (2017): 1600420.
35. Roulhac, P.L., Ward, J.M., Thompson, J.W., Soderblom, E.J., Silva, M., Moseley, M.A. and Jarvis, E.D. "Microproteomics: quantitative proteomic profiling of small numbers of laser-captured cells." Cold Spring Harbor Protocols 2011.2 (2011): pdb-prot5573.
36. Maes, E., Cools, N., Willems, H. and Baggerman, G. "FACS-Based Proteomics Enables Profiling of Proteins in Rare Cell Populations." International Journal of Molecular Sciences 21.18 (2020): 6557.
37. Nagrath, S., Sequist, L.V., Maheswaran, S., Bell, D.W., Irimia, D., Ulkus, L., Smith, M.R., Kwak, E.L., Digumarthy, S., Muzikansky, A. and Ryan, P. "Isolation of rare circulating tumour cells in cancer patients by microchip technology." Nature 450.7173 (2007): 1235-1239.
38. Cristofanilli, M., Budd, G.T., Ellis, M.J., Stopeck, A., Matera, J., Miller, M.C., Reuben, J.M., Doyle, G.V., Allard, W.J., Terstappen, L.W. and Hayes, D.F. "Circulating tumor cells, disease progression, and survival in metastatic breast cancer." New England Journal of Medicine 351.8 (2004): 781-791.
39. Gleghorn, J.P., Pratt, E.D., Denning, D., Liu, H., Bander, N.H., Tagawa, S.T., Nanus, D.M., Giannakakou, P.A. and Kirby, B.J. "Capture of circulating tumor cells from whole blood of prostate cancer patients using geometrically enhanced differential immunocapture (GEDI) and a prostate-specific antibody." Lab on a Chip 10.1 (2010): 27-29.
40. Riethdorf, S., Fritsche, H., Müller, V., Rau, T., Schindlbeck, C., Rack, B., Janni, W., Coith, C., Beck, K., Jänicke, F. and Jackson, S. "Detection of circulating tumor cells in peripheral blood of patients with metastatic breast cancer: a validation study of the Cell Search system." Clinical cancer research 13.3 (2007): 920-928.
41. Naito, T., Udagawa, H., Sato, J., Horinouchi, H., Murakami, S., Goto, Y., Kanda, S., Fujiwara, Y., Yamamoto, N., Zenke, Y. and Kirita, K. "A Minimum Of 100 Tumor Cells in a Single Biopsy Sample Is Required to Assess Programmed Cell Death Ligand 1 Expression in Predicting Patient Response to Nivolumab Treatment in Non-squamous Non–Small Cell Lung Carcinoma." Journal of Thoracic Oncology 14.10 (2019): 1818-1827.
42. Hwang, S.I., Thumar, J., Lundgren, D.H., Rezaul, K., Mayya, V., Wu, L., Eng, J., Wright, M.E. and Han, D.K. "Direct cancer tissue proteomics: a method to identify candidate cancer biomarkers from formalin-fixed paraffin-embedded archival tissues." Oncogene 26.1 (2007): 65-76.
43. Specht, H., Emmott, E., Petelski, A.A., Huffman, R.G., Perlman, D.H., Serra, M., Kharchenko, P., Koller, A. and Slavov, N. "Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2." Genome biology 22.1 (2021): 1-27.
44. Zhu, Y., Scheibinger, M., Ellwanger, D.C., Krey, J.F., Choi, D., Kelly, R.T., Heller, S. and Barr-Gillespie, P.G. "Single-cell proteomics reveals changes in expression during hair-cell development." Elife 8 (2019): e50777.
45. Slavov, N. "Unpicking the proteome in single cells." Science 367.6477 (2020): 512-513.
46. Balasubramanian, V.K., Purvine, S.O., Liang, Y., Kelly, R.T., Pasa‐Tolic, L., Chrisler, W.B., Blumwald, E., Stewart Jr, C.N., Zhu, Y. and Ahkami, A.H. "Cell‐Type‐Specific Proteomics Analysis of a Small Number of Plant Cells by Integrating Laser Capture Microdissection with a Nanodroplet Sample Processing Platform." Current Protocols 1.5 (2021): e153.
47. Piehowski, P.D., Zhu, Y., Bramer, L.M., Stratton, K.G., Zhao, R., Orton, D.J., Moore, R.J., Yuan, J., Mitchell, H.D., Gao, Y. and Webb-Robertson, B.J.M. "Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution". Nat Commun 11, (2020): 8.
48. Weke, K., Singh, A., Uwugiaren, N., Alfaro, J.A., Wang, T., Hupp, T.R., O’Neill, J.R., Vojtesek, B., Goodlett, D.R., Williams, S.M., Zhou, M., Kelly, R.T., Zhu, Y., and Dapic, I. "MicroPOTS Analysis of Barrett’s Esophageal Cell Line Models Identifies Proteomic Changes after Physiologic and Radiation Stress." Journal of proteome research 20.5 (2021): 2195-2205.
49. Cong, Y., Motamedchaboki, K., Misal, S.A., Liang, Y., Guise, A.J., Truong, T., Huguet, R., Plowey, E.D., Zhu, Y., Lopez-Ferrer, D. and Kelly, R.T. "Ultrasensitive single-cell proteomics workflow identifies> 1000 protein groups per mammalian cell." Chemical Science 12.3 (2021): 1001-1006.
50. Levy, Ezra, and Nikolai Slavov. "Single cell protein analysis for systems biology." Essays in biochemistry 62.4 (2018): 595-605.
51. Mahdessian, D., Cesnik, A.J., Gnann, C., Danielsson, F., Stenström, L., Arif, M., Zhang, C., Le, T., Johansson, F., Shutten, R., Bäckström, A., Axelsson, U., Thul, P., Cho, N.H., Carja, O., Uhlen, M., Mardinoglu, A., Stadler, C., Lindskog, C., Ayoglu, B., Leonetti, M., Ponten, F., Sullivan, D.P., and Lundberg, E. "Spatiotemporal dissection of the cell cycle with single-cell proteogenomics." Nature 590.7847 (2021): 649-654.
52. Hughes, C.S., Foehr, S., Garfield, D.A., Furlong, E.E., Steinmetz, L.M. and Krijgsveld, J. "Ultrasensitive proteome analysis using paramagnetic bead technology." Molecular systems biology 10.10 (2014): 757.
53. Sielaff, M., Kuharev, J., Bohn, T., Hahlbrock, J., Bopp, T., Tenzer, S. and Distler, U. "Evaluation of FASP, SP3, and iST protocols for proteomic sample preparation in the low microgram range." Journal of proteome research 16.11 (2017): 4060-4072.
54. Kulak, N.A., Pichler, G., Paron, I., Nagaraj, N. and Mann, M. "Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells." Nature methods 11.3 (2014): 319-324.
55. Li, Z.Y., Huang, M., Wang, X.K., Zhu, Y., Li, J.S., Wong, C.C. and Fang, Q. "Nanoliter-scale oil-air-droplet chip-based single cell proteomic analysis." Analytical chemistry 90.8 (2018): 5430-5438.
56. Zhu, Y., Piehowski, P.D., Zhao, R., Chen, J., Shen, Y., Moore, R.J., Shukla, A.K., Petyuk, V.A., Campbell-Thompson, M., Mathews, C.E., Smith, R.D., Qian, W.J., and Kelly, R.T. "Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells". Nat Commun 9, (2018): 882.
57. Leipert, Jan, and Andreas Tholey. "Miniaturized sample preparation on a digital microfluidics device for sensitive bottom-up microproteomics of mammalian cells using magnetic beads and mass spectrometry-compatible surfactants." Lab on a chip 19.20 (2019): 3490-3498.
58. Shao, X., Wang, X., Guan, S., Lin, H., Yan, G., Gao, M., Deng, C. and Zhang, X. "Integrated proteome analysis device for fast single-cell protein profiling." Analytical chemistry 90.23 (2018): 14003-14010.
59. Lamanna, J., Scott, E.Y., Edwards, H.S., Chamberlain, M.D., Dryden, M.D., Peng, J., Mair, B., Lee, A., Chan, C., Sklavounos, A.A., Heffernan, A., Abbas, F., Lam, C., Olson, M., Moffat, J., and Wheeler, A.R. "Digital microfluidic isolation of single cells for -Omics". Nat Commun 11, (2020): 5632.
60. Zhu, Y., Clair, G., Chrisler, W.B., Shen, Y., Zhao, R., Shukla, A.K., Moore, R.J., Misra, R.S., Pryhuber, G.S., Smith, R.D., Ansong, C., and Kelly, R.T. "Proteomic analysis of single mammalian cells enabled by microfluidic nanodroplet sample preparation and ultrasensitive NanoLC‐MS." Angewandte Chemie 130.38 (2018): 12550-12554.
61. Schoof, E.M., Furtwängler, B., Üresin, N., Rapin, N., Savickas, S., Gentil, C., Lechman, E., auf dem Keller, U., Dick, J.E. and Porse, B.T. "Quantitative single-cell proteomics as a tool to characterize cellular hierarchies". Nat Commun 12, (2021): 3341.
62. Domon, Bruno, and Ruedi Aebersold. "Options and considerations when selecting a quantitative proteomics strategy." Nature biotechnology 28.7 (2010): 710-721.
63. Liu, Hongbin, Rovshan G. Sadygov, and John R. Yates. "A model for random sampling and estimation of relative protein abundance in shotgun proteomics." Analytical chemistry 76.14 (2004): 4193-4201.
64. Egertson, J.D., Kuehn, A., Merrihew, G.E., Bateman, N.W., MacLean, B.X., Ting, Y.S., Canterbury, J.D., Marsh, D.M., Kellmann, M., Zabrouskov, V., Wu, C.C., and MacCoss, M.J. "Multiplexed MS/MS for improved data-independent acquisition." Nature methods 10.8 (2013): 744-746.
65. Michalski, Annette, Juergen Cox, and Matthias Mann. "More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC− MS/MS." Journal of proteome research 10.4 (2011): 1785-1793.
66. Venable, J.D., Dong, M.Q., Wohlschlegel, J., Dillin, A. and Yates, J.R. "Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra." Nature methods 1.1 (2004): 39-45.
67. Gillet, L.C., Navarro, P., Tate, S., Röst, H., Selevsek, N., Reiter, L., Bonner, R. and Aebersold, R. "Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis." Molecular & Cellular Proteomics (2012): 11.6.
68. Ludwig, C., Gillet, L., Rosenberger, G., Amon, S., Collins, B.C. and Aebersold, R. "Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial." Molecular systems biology 14.8 (2018): e8126.
69. Distler, U., Kuharev, J., Navarro, P., Levin, Y., Schild, H. and Tenzer, S. "Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics." Nature methods 11.2 (2014): 167-170.
70. Selevsek, N., Chang, C.Y., Gillet, L.C., Navarro, P., Bernhardt, O.M., Reiter, L., Cheng, L.Y., Vitek, O. and Aebersold, R. "Reproducible and consistent quantification of the Saccharomyces cerevisiae proteome by SWATH-mass spectrometry." Molecular & Cellular Proteomics 14.3 (2015): 739-749.
71. Bruderer, R., Bernhardt, O.M., Gandhi, T., Miladinović, S.M., Cheng, L.Y., Messner, S., Ehrenberger, T., Zanotelli, V., Butscheid, Y., Escher, C., Vitek, O., Rinner, O., and Reiter, L. "Extending the Limits of Quantitative Proteome Profiling with Data-Independent Acquisition and Application to Acetaminophen-Treated Three-Dimensional Liver Microtissues*[S]." Molecular & Cellular Proteomics 14.5 (2015): 1400-1410.
72. Escher, C., Reiter, L., MacLean, B., Ossola, R., Herzog, F., Chilton, J., MacCoss, M.J. and Rinner, O. "Using i RT, a normalized retention time for more targeted measurement of peptides." Proteomics 12.8 (2012): 1111-1121.
73. MacLean, B., Tomazela, D.M., Shulman, N., Chambers, M., Finney, G.L., Frewen, B., Kern, R., Tabb, D.L., Liebler, D.C. and MacCoss, M.J. "Skyline: an open source document editor for creating and analyzing targeted proteomics experiments." Bioinformatics 26.7 (2010): 966-968.
74. Egertson, J.D., MacLean, B., Johnson, R., Xuan, Y. and MacCoss, M.J. "Multiplexed peptide analysis using data-independent acquisition and Skyline." Nature protocols 10.6 (2015): 887-903.
75. Demichev, V., Messner, C.B., Vernardis, S.I., Lilley, K.S. and Ralser, M. "DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput." Nature methods 17.1 (2020): 41-44.
76. Röst, H.L., Rosenberger, G., Navarro, P., Gillet, L., Miladinović, S.M., Schubert, O.T., Wolski, W., Collins, B.C., Malmström, J., Malmström, L. and Aebersold, R. "OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data." Nature biotechnology 32.3 (2014): 219-223.
77. Sinitcyn, P., Hamzeiy, H., Salinas Soto, F., Itzhak, D., McCarthy, F., Wichmann, C., Steger, M., Ohmayer, U., Distler, U., Kaspar-Schoenefeld, S., Prianichnikov, N., Yilmaz, S., Rudolph, J.D., Tenzer, S., Perez-Riverol, Y., Nagaraj, N., Humphrey, S.J., and Cox, J. "MaxDIA enables library-based and library-free data-independent acquisition proteomics". Nat Biotechnol 39, (2021): 1563–1573.
78. Ting, Y.S., Egertson, J.D., Payne, S.H., Kim, S., MacLean, B., Käll, L., Aebersold, R., Smith, R.D., Noble, W.S. and MacCoss, M.J. "Peptide-centric proteome analysis: an alternative strategy for the analysis of tandem mass spectrometry data." Molecular & Cellular Proteomics 14.9 (2015): 2301-2307.
79. Tsou, C.C., Avtonomov, D., Larsen, B., Tucholska, M., Choi, H., Gingras, A.C. and Nesvizhskii, A.I. "DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics." Nature methods 12.3 (2015): 258-264.
80. Ting, Y.S., Egertson, J.D., Bollinger, J.G., Searle, B.C., Payne, S.H., Noble, W.S. and MacCoss, M.J. "PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data." Nature methods 14.9 (2017): 903-908.
81. Li, Y., Zhong, C.Q., Xu, X., Cai, S., Wu, X., Zhang, Y., Chen, J., Shi, J., Lin, S. and Han, J. "Group-DIA: analyzing multiple data-independent acquisition mass spectrometry data files." Nature methods 12.12 (2015): 1105-1106.
82. Rappsilber, J., Mann, M. and Ishihama, Y. "Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips". Nat Protoc 2, (2007): 1896–1906.
83. Kitata, R.B., Choong, W.K., Tsai, C.F., Lin, P.Y., Chen, B.S., Chang, Y.C., Nesvizhskii, A.I., Sung, T.Y. and Chen, Y.J. "A data-independent acquisition-based global phosphoproteomics system enables deep profiling". Nature Communications 12, (2021): 2539.
84. Stuart, T. and Satija, R. "Integrative single-cell analysis". Nat Rev Genet 20, 257-272 (2019).
85. Chappell, L., Russell, A.J.C. and Voet, T. "Single-Cell (Multi)omics Technologies". Annu Rev Genomics Hum Genet 19, (2018): 15-41.
86. Labib, M. and Kelley, S.O. "Single-cell analysis targeting the proteome". Nature Reviews Chemistry 4, (2020): 143-158.
87. Angel, T.E., Aryal, U.K., Hengel, S.M., Baker, E.S., Kelly, R.T., Robinson, E.W. and Smith, R.D. "Mass spectrometry-based proteomics: existing capabilities and future directions". Chem Soc Rev 41, (2012): 3912-3928.
88. Slavov, N. "Single-cell protein analysis by mass spectrometry". Curr Opin Chem Biol 60, (2020): 1-9.
89. Whitesides, G.M. "The origins and the future of microfluidics". Nature 442, (2006): 368-373.
90. Unger, M.A., Chou, H.P., Thorsen, T., Scherer, A. and Quake, S.R. "Monolithic microfabricated valves and pumps by multilayer soft lithography". Science 288, (2000): 113-116.
91. Sackmann, E.K., Fulton, A.L. and Beebe, D.J. "The present and future role of microfluidics in biomedical research". Nature 507, (2014): 181-189.
92. Tyanova, S., Temu, T. and Cox, J. "The MaxQuant computational platform for mass spectrometry-based shotgun proteomics". Nat Protoc 11, (2016): 2301-2319.
93. Lazar, C., Gatto, L., Ferro, M., Bruley, C. and Burger, T. "Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies". J Proteome Res 15, (2016): 1116-1125.
94. Kanehisa, M. and Goto, S. "KEGG: kyoto encyclopedia of genes and genomes". Nucleic Acids Res 28, (2000): 27-30.
95. "How Can Systems Biology Test Principles and Tools Using Immune Cells as a Model?" Cell Syst 6, (2018): 146-148.
96. Eid, S., Turk, S., Volkamer, A., Rippmann, F. and Fulle, S. "KinMap: a web-based tool for interactive navigation through human kinome data". BMC Bioinformatics 18, (2017): 16.
97. Burger, J.A. and Wiestner, A. "Targeting B cell receptor signalling in cancer: preclinical and clinical advances". Nat Rev Cancer 18, (2018): 148-167.
98. Myers, D.R., Zikherman, J. and Roose, J.P. "Tonic Signals: Why Do Lymphocytes Bother? " Trends in Immunology 38, (2017): 844-857.
99. Johnston, H.E., Carter, M.J., Larrayoz, M., Clarke, J., Garbis, S.D., Oscier, D., Strefford, J.C., Steele, A.J., Walewska, R. and Cragg, M.S. "Proteomics Profiling of CLL Versus Healthy B-cells Identifies Putative Therapeutic Targets and a Subtype-independent Signature of Spliceosome Dysregulation". Mol Cell Proteomics 17, (2018): 776-791.
100. Liang, Y., Acor, H., McCown, M.A., Nwosu, A.J., Boekweg, H., Axtell, N.B., Truong, T., Cong, Y., Payne, S.H. and Kelly, R.T. "Fully Automated Sample Processing and Analysis Workflow for Low-Input Proteome Profiling". Analytical Chemistry 93, (2021): 1658-1666.
101. Williams, S.M., Liyu, A.V., Tsai, C.F., Moore, R.J., Orton, D.J., Chrisler, W.B., Gaffrey, M.J., Liu, T., Smith, R.D., Kelly, R.T., Pasa-Tolic, L., and Zhu, Y. L. "Automated Coupling of Nanodroplet Sample Preparation with Liquid Chromatography–Mass Spectrometry for High-Throughput Single-Cell Proteomics". Analytical Chemistry 92, (2020): 10588-10596.
102. Williams, E. G., Wu, Y., Jha, P., Dubuis, S., Blattmann, P., Argmann, C. A., Houten, S. M., Amariuta, T., Wolski, W., Zamboni, N., Aebersold, R. and Auwerx, J. "Systems proteomics of liver mitochondria function". Science (2016): 352 (6291), aad0189.
103. Searle, B.C., Swearingen, K.E., Barnes, C.A., Schmidt, T., Gessulat, S., Küster, B. and Wilhelm, M."Generating high quality libraries for DIA MS with empirically corrected peptide predictions". Nat Commun (2020): 11 (1).
104. Searle, B.C., Pino, L.K., Egertson, J.D., Ting, Y.S., Lawrence, R.T., MacLean, B.X., Villén, J. and MacCoss, M.J. "Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry". Nat Commun (2018):, 9.
105. Meier, F., Brunner, A.D., Koch, S., Koch, H., Lubeck, M., Krause, M., Goedecke, N., Decker, J., Kosinski, T., Park, M.A., Bache, N., Hoerning, O., Cox, J., Rather, O., and Mann, M. "Online Parallel Accumulation-Serial Fragmentation (PASEF) with a Novel Trapped Ion Mobility Mass Spectrometer". Mol Cell Proteomics (2018): 17 (12), 2534-2545.
106. Kawashima, Y., Watanabe, E., Umeyama, T., Nakajima, D., Hattori, M., Honda, K., and Ohara, O. "Optimization of Data-Independent Acquisition Mass Spectrometry for Deep and Highly Sensitive Proteomic Analysis". Int J Mol Sci (2019): 20 (23).
107. Brunner, A.D., Thielert, M., Vasilopoulou, C.G., Ammar, C., Coscia, F., Mund, A., Hoerning, O.B., Bache, N., Apalategui, A., Lubeck, M., Richter, S., Fischer, D.S., Raether, O., Park, M.A., Meier, F., Theis, F.J., and Mann, M. "Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation". bioRxiv, (2020): DOI:10.1101/2020.12.22.423933.
108. Gebreyesus, S.T., Siyal, A.A., Kitata, R.B., Chen, E.S.W., Enkhbayar, B., Angata, T., Lin, K.I., Chen, Y.J. and Tu, H.L. "Streamlined single-cell proteomics by an integrated microfluidic chip and data-independent acquisition mass spectrometry." Nature Communications 13.1 (2022): 1-13. DOI: https://doi.org/10.1038/s41467-021-27778-4.
109. Parker, S.J., Venkatraman, V. and Van Eyk, J.E. "Effect of pep-tide assay library size and composition in targeted data-independent acquisition-MS analyses". Proteomics 16 (15-16), (2016): 2221-37.
110. Rosenberger, G., Bludau, I., Schmitt, U., Heusel, M., Hunter, C. L., Liu, Y., MacCoss, M. J., MacLean, B. X., Nesvizhskii, A. I., Pedrioli, P. G. A., Reiter, L., Rost, H. L., Tate, S., Ting, Y. S., Collins, B. C., and Aebersold, R., "Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses". Nat Methods (2017): 14 (9), 921-927.
111. Cong, Y., Liang, Y., Motamedchaboki, K., Huguet, R., Truong, T., Zhao, R., Shen, Y., Lopez-Ferrer, D., Zhu, Y., and Kelly, R. T. "Improved Single-Cell Proteome Coverage Us-ing Narrow-Bore Packed NanoLC Columns and Ultrasensitive Mass Spectrometry''. Anal Chem (2020): 92 (3), 2665-2671.
112. Gregus, M., Kostas, J. C., Ray, S., Abbatiello, S. E., and Ivanov, A. R. "Improved Sensitivity of Ultralow Flow LC-MS-Based Proteomic Profiling of Limited Samples Using Monolithic Capillary Columns and FAIMS Technology". Anal Chem 92 (21), (2020): 14702-14712.
113. Okuda, S., Watanabe, Y., Moriya, Y., Kawano, S., Yamamoto, T., Matsumoto, M., Takami, T., Kobayashi, D., Araki, N., Yoshizawa, A. C., Tabata, T., Sugiyama, N., Goto, S., and Ishihama, Y. "jPOSTrepo: an international standard data repository for proteomes". Nucleic Acids Res. 45, (2017): D1107−D1111.
114. Siyal, A. A., Chen, E. S.-W., Chan, H.-J., Kitata, R. B., Yang, J.-C., Tu, H.-L., Chen, Y.-J. "Sample Size-Comparable Spectral Library Enhances Data-Independent Acquisition-Based Proteome Coverage of Low-Input Cells". Analytical Chemistry 93 (51), (2021): 17003-17011.