101
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Doellinger J, Blumenscheit C, Schneider A, Lasch P. Isolation Window Optimization of Data-Independent Acquisition Using Predicted Libraries for Deep and Accurate Proteome Profiling. Anal Chem 2020; 92:12185-12192. [DOI: 10.1021/acs.analchem.0c00994] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Joerg Doellinger
- Robert Koch-Institute, Centre for Biological Threats and Special Pathogens, Proteomics and Spectroscopy (ZBS6), 13353 Berlin, Germany
| | - Christian Blumenscheit
- Robert Koch-Institute, Centre for Biological Threats and Special Pathogens, Proteomics and Spectroscopy (ZBS6), 13353 Berlin, Germany
| | - Andy Schneider
- Robert Koch-Institute, Centre for Biological Threats and Special Pathogens, Proteomics and Spectroscopy (ZBS6), 13353 Berlin, Germany
| | - Peter Lasch
- Robert Koch-Institute, Centre for Biological Threats and Special Pathogens, Proteomics and Spectroscopy (ZBS6), 13353 Berlin, Germany
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102
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Nys G, Nix C, Cobraiville G, Servais AC, Fillet M. Enhancing protein discoverability by data independent acquisition assisted by ion mobility mass spectrometry. Talanta 2020; 213:120812. [DOI: 10.1016/j.talanta.2020.120812] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 02/01/2020] [Accepted: 02/07/2020] [Indexed: 11/28/2022]
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103
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Zhang F, Ge W, Ruan G, Cai X, Guo T. Data‐Independent Acquisition Mass Spectrometry‐Based Proteomics and Software Tools: A Glimpse in 2020. Proteomics 2020; 20:e1900276. [DOI: 10.1002/pmic.201900276] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 03/27/2020] [Indexed: 01/02/2023]
Affiliation(s)
- Fangfei Zhang
- Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
- Institute of Basic Medical SciencesWestlake Institute for Advanced Study 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
| | - Weigang Ge
- Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
- Institute of Basic Medical SciencesWestlake Institute for Advanced Study 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
| | - Guan Ruan
- Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
- Institute of Basic Medical SciencesWestlake Institute for Advanced Study 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
| | - Xue Cai
- Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
- Institute of Basic Medical SciencesWestlake Institute for Advanced Study 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
| | - Tiannan Guo
- Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
- Institute of Basic Medical SciencesWestlake Institute for Advanced Study 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
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104
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Zhong CQ, Wu J, Qiu X, Chen X, Xie C, Han J. Generation of a murine SWATH-MS spectral library to quantify more than 11,000 proteins. Sci Data 2020; 7:104. [PMID: 32218446 PMCID: PMC7099061 DOI: 10.1038/s41597-020-0449-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 03/06/2020] [Indexed: 12/16/2022] Open
Abstract
Targeted SWATH-MS data analysis is critically dependent on the spectral library. Comprehensive spectral libraries of human or several other organisms have been published, but the extensive spectral library for mouse, a widely used model organism is not available. Here, we present a large murine spectral library covering more than 11,000 proteins and 240,000 proteotypic peptides, which included proteins derived from 9 murine tissue samples and one murine L929 cell line. This resource supports the quantification of 67% of all murine proteins annotated by UniProtKB/Swiss-Prot. Furthermore, we applied the spectral library to SWATH-MS data from murine tissue samples. Data are available via SWATHAtlas (PASS01441). Measurement(s) | Mouse Protein • mass spectrum • spectral library | Technology Type(s) | mass spectrometry • combined ms-ms + spectral library search | Sample Characteristic - Organism | Mus musculus |
Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11968230
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Affiliation(s)
- Chuan-Qi Zhong
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen, China.
| | - Jianfeng Wu
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen, China
| | - Xingfeng Qiu
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, Xiamen, China
| | - Xi Chen
- Medical Research Institute, Wuhan University, Wuhan, China.,SpecAlly Life Technology Co., Ltd, Wuhan, China
| | - Changchuan Xie
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen, China
| | - Jiahuai Han
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen, China.
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105
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Huang T, Bruderer R, Muntel J, Xuan Y, Vitek O, Reiter L. Combining Precursor and Fragment Information for Improved Detection of Differential Abundance in Data Independent Acquisition. Mol Cell Proteomics 2020; 19:421-430. [PMID: 31888964 PMCID: PMC7000113 DOI: 10.1074/mcp.ra119.001705] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 12/16/2019] [Indexed: 11/16/2022] Open
Abstract
In bottom-up, label-free discovery proteomics, biological samples are acquired in a data-dependent (DDA) or data-independent (DIA) manner, with peptide signals recorded in an intact (MS1) and fragmented (MS2) form. While DDA has only the MS1 space for quantification, DIA contains both MS1 and MS2 at high quantitative quality. DIA profiles of complex biological matrices such as tissues or cells can contain quantitative interferences, and the interferences at the MS1 and the MS2 signals are often independent. When comparing biological conditions, the interferences can compromise the detection of differential peptide or protein abundance and lead to false positive or false negative conclusions.We hypothesized that the combined use of MS1 and MS2 quantitative signals could improve our ability to detect differentially abundant proteins. Therefore, we developed a statistical procedure incorporating both MS1 and MS2 quantitative information of DIA. We benchmarked the performance of the MS1-MS2-combined method to the individual use of MS1 or MS2 in DIA using four previously published controlled mixtures, as well as in two previously unpublished controlled mixtures. In the majority of the comparisons, the combined method outperformed the individual use of MS1 or MS2. This was particularly true for comparisons with low fold changes, few replicates, and situations where MS1 and MS2 were of similar quality. When applied to a previously unpublished investigation of lung cancer, the MS1-MS2-combined method increased the coverage of known activated pathways.Since recent technological developments continue to increase the quality of MS1 signals (e.g. using the BoxCar scan mode for Orbitrap instruments), the combination of the MS1 and MS2 information has a high potential for future statistical analysis of DIA data.
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Affiliation(s)
- Ting Huang
- Northeastern University, Boston MA 02115
| | | | - Jan Muntel
- Biognosys, Wagistrasse 21, 8952 Schlieren, Switzerland
| | - Yue Xuan
- Thermo Fisher Scientific, 28199 Bremen, Germany
| | - Olga Vitek
- Northeastern University, Boston MA 02115.
| | - Lukas Reiter
- Biognosys, Wagistrasse 21, 8952 Schlieren, Switzerland.
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106
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Kawashima Y, Watanabe E, Umeyama T, Nakajima D, Hattori M, Honda K, Ohara O. Optimization of Data-Independent Acquisition Mass Spectrometry for Deep and Highly Sensitive Proteomic Analysis. Int J Mol Sci 2019; 20:ijms20235932. [PMID: 31779068 PMCID: PMC6928715 DOI: 10.3390/ijms20235932] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 11/20/2019] [Accepted: 11/23/2019] [Indexed: 02/07/2023] Open
Abstract
Data-independent acquisition (DIA)-mass spectrometry (MS)-based proteomic analysis overtop the existing data-dependent acquisition (DDA)-MS-based proteomic analysis to enable deep proteome coverage and precise relative quantitative analysis in single-shot liquid chromatography (LC)-MS/MS. However, DIA-MS-based proteomic analysis has not yet been optimized in terms of system robustness and throughput, particularly for its practical applications. We established a single-shot LC-MS/MS system with an MS measurement time of 90 min for a highly sensitive and deep proteomic analysis by optimizing the conditions of DIA and nanoLC. We identified 7020 and 4068 proteins from 200 ng and 10 ng, respectively, of tryptic floating human embryonic kidney cells 293 (HEK293F) cell digest by performing the constructed LC-MS method with a protein sequence database search. The numbers of identified proteins from 200 ng and 10 ng of tryptic HEK293F increased to 8509 and 5706, respectively, by searching the chromatogram library created by gas-phase fractionated DIA. Moreover, DIA protein quantification was highly reproducible, with median coefficients of variation of 4.3% in eight replicate analyses. We could demonstrate the power of this system by applying the proteomic analysis to detect subtle changes in protein profiles between cerebrums in germ-free and specific pathogen-free mice, which successfully showed that >40 proteins were differentially produced between the cerebrums in the presence or absence of bacteria.
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Affiliation(s)
- Yusuke Kawashima
- Department of Applied Genomics, Kazusa DNA Research Institute, Chiba 292-0818, Japan; (Y.K.); (D.N.)
| | - Eiichiro Watanabe
- Laboratory for Gut Homeostasis, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan; (E.W.); (K.H.)
- Department of Microbiology and Immunology, Keio University School of Medicine, Tokyo 160-8582, Japan
- Department of Pediatric Surgery, Faculty of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
| | - Taichi Umeyama
- Laboratory for Microbiome Sciences, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan; (T.U.); (M.H.)
| | - Daisuke Nakajima
- Department of Applied Genomics, Kazusa DNA Research Institute, Chiba 292-0818, Japan; (Y.K.); (D.N.)
| | - Masahira Hattori
- Laboratory for Microbiome Sciences, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan; (T.U.); (M.H.)
- Graduate School of Advanced Science and Engineering, Waseda University, Tokyo 169-8555, Japan
| | - Kenya Honda
- Laboratory for Gut Homeostasis, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan; (E.W.); (K.H.)
- Department of Microbiology and Immunology, Keio University School of Medicine, Tokyo 160-8582, Japan
| | - Osamu Ohara
- Department of Applied Genomics, Kazusa DNA Research Institute, Chiba 292-0818, Japan; (Y.K.); (D.N.)
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan
- Correspondence: ; Tel.: +81-438-52-391; Fax: +81-438-52-3914
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107
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Zhong CQ, Wu R, Chen X, Wu S, Shuai J, Han J. Systematic Assessment of the Effect of Internal Library in Targeted Analysis of SWATH-MS. J Proteome Res 2019; 19:477-492. [DOI: 10.1021/acs.jproteome.9b00669] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Chuan-Qi Zhong
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen 361102, China
| | - Rui Wu
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen 361102, China
| | - Xi Chen
- Medical Research Institute, Wuhan University, Wuhan 430072, China
- SpecAlly Life Technology Co., Ltd., Wuhan 430072, China
| | - Suqin Wu
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen 361102, China
| | - Jianwei Shuai
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen 361102, China
- Department of Physics, Xiamen University, Xiamen 361005, China
| | - Jiahuai Han
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen 361102, China
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