1
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Yang JC, Chen SP, Wang YF, Chang CH, Chang KH, Fuh JL, Chow LH, Han CL, Chen YJ, Wang SJ. Cerebrospinal Fluid Proteome Map Reveals Molecular Signatures of Reversible Cerebral Vasoconstriction Syndrome. Mol Cell Proteomics 2024; 23:100794. [PMID: 38839039 PMCID: PMC11263949 DOI: 10.1016/j.mcpro.2024.100794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 04/08/2024] [Accepted: 05/07/2024] [Indexed: 06/07/2024] Open
Abstract
Reversible cerebral vasoconstriction syndrome (RCVS) is a complex neurovascular disorder characterized by repetitive thunderclap headaches and reversible cerebral vasoconstriction. The pathophysiological mechanism of this mysterious syndrome remains underexplored and there is no clinically available molecular biomarker. To provide insight into the pathogenesis of RCVS, this study reported the first landscape of dysregulated proteome of cerebrospinal fluid (CSF) in patients with RCVS (n = 21) compared to the age- and sex-matched controls (n = 20) using data-independent acquisition mass spectrometry. Protein-protein interaction and functional enrichment analysis were employed to construct functional protein networks using the RCVS proteome. An RCVS-CSF proteome library resource of 1054 proteins was established, which illuminated large groups of upregulated proteins enriched in the brain and blood-brain barrier (BBB). Personalized RCVS-CSF proteomic profiles from 17 RCVS patients and 20 controls reveal proteomic changes involving the complement system, adhesion molecules, and extracellular matrix, which may contribute to the disruption of BBB and dysregulation of neurovascular units. Moreover, an additional validation cohort validated a panel of biomarker candidates and a two-protein signature predicted by machine learning model to discriminate RCVS patients from controls with an area under the curve of 0.997. This study reveals the first RCVS proteome and a potential pathogenetic mechanism of BBB and neurovascular unit dysfunction. It also nominates potential biomarker candidates that are mechanistically plausible for RCVS, which may offer potential diagnostic and therapeutic opportunities beyond the clinical manifestations.
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Affiliation(s)
- Jhih-Ci Yang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan; Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Shih-Pin Chen
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Division of Translational Research, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Yen-Feng Wang
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chan-Hua Chang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan; Department of Chemistry, National Central University, Taoyuan, Taiwan
| | - Kun-Hao Chang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan; Molecular Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan; Department of Chemistry, Institute of Chemistry, Academia Sinica, Naitonal Tsing Hua University, Hsinchu, Taiwan
| | - Jong-Ling Fuh
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Lok-Hi Chow
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Anesthesiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chia-Li Han
- Master Program in Clinical Genomics and Proteomics, College of Pharmacy, Taipei Medical University, Taipei, Taiwan.
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan; Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Chemistry, National Taiwan University, Taipei, Taiwan.
| | - Shuu-Jiun Wang
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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2
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Bai Y, Li Y, Tang Z, Hu L, Jiang X, Chen J, Huang S, Wu K, Xu W, Chen C. Urinary proteome analysis of acute kidney injury in post-cardiac surgery patients using enrichment materials with high-resolution mass spectrometry. Front Bioeng Biotechnol 2022; 10:1002853. [PMID: 36177176 PMCID: PMC9513377 DOI: 10.3389/fbioe.2022.1002853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 08/29/2022] [Indexed: 11/25/2022] Open
Abstract
Background: Cardiac surgery-associated acute kidney injury (CSA-AKI) may increase the mortality and incidence rates of chronic kidney disease in critically ill patients. This study aimed to investigate the underlying correlations between urinary proteomic changes and CSA-AKI. Methods: Nontargeted proteomics was performed using nano liquid chromatography coupled with Orbitrap Exploris mass spectrometry (MS) on urinary samples preoperatively and postoperatively collected from patients with CSA-AKI. Gemini C18 silica microspheres were used to separate and enrich trypsin-hydrolysed peptides under basic mobile phase conditions. Differential analysis was conducted to screen out urinary differential expressed proteins (DEPs) among patients with CSA-AKI for bioinformatics. Kyoto Encyclopedia of Genes and Genomes (KEGG) database analysis was adopted to identify the altered signal pathways associated with CSA-AKI. Results: Approximately 2000 urinary proteins were identified and quantified through data-independent acquisition MS, and 324 DEPs associated with AKI were screened by univariate statistics. According to KEGG enrichment analysis, the signal pathway of protein processing in the endoplasmic reticulum was enriched as the most up-regulated DEPs, and cell adhesion molecules were enriched as the most down-regulated DEPs. In protein–protein interaction analysis, the three hub targets in the up-regulated DEPs were α-1-antitrypsin, β-2-microglobulin and angiotensinogen, and the three key down-regulated DEPs were growth arrest-specific protein 6, matrix metalloproteinase-9 and urokinase-type plasminogen activator. Conclusion: Urinary protein disorder was observed in CSA-AKI due to ischaemia and reperfusion. The application of Gemini C18 silica microspheres can improve the protein identification rate to obtain highly valuable resources for the urinary DEPs of AKI. This work provides valuable knowledge about urinary proteome biomarkers and essential resources for further research on AKI.
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Affiliation(s)
- Yunpeng Bai
- Center of Scientific Research, Maoming People’s Hospital, Maoming, China
- Department of Critical Care Medicine, Maoming People’s Hospital, Maoming, China
| | - Ying Li
- Department of Intensive Care Unit of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhizhong Tang
- Department of Urology, Maoming People’s Hospital, Maoming, China
| | - Linhui Hu
- Department of Critical Care Medicine, Maoming People’s Hospital, Maoming, China
| | - Xinyi Jiang
- Department of Intensive Care Unit of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Jingchun Chen
- Department of Intensive Care Unit of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Sumei Huang
- Center of Scientific Research, Maoming People’s Hospital, Maoming, China
- Department of Emergency, Maoming People’s Hospital, Maoming, China
- Biological Resource Center of Maoming People’s Hospital, Maoming, China
| | - Kunyong Wu
- Center of Scientific Research, Maoming People’s Hospital, Maoming, China
- Biological Resource Center of Maoming People’s Hospital, Maoming, China
| | - Wang Xu
- Department of Intensive Care Unit of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Department of Critical Care Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Chunbo Chen
- Department of Emergency, Maoming People’s Hospital, Maoming, China
- Department of Critical Care Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Renal Failure Research, Southern Medical University, Guangzhou, China
- *Correspondence: Chunbo Chen, , orcid.org/0000-0001-5662-497X
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3
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Qian S, Li X, Liu C, Zhang C, Blecker C. Proteomic changes involved in water holding capacity of frozen bovine longissimus dorsi muscles based on DIA strategy. J Food Biochem 2022; 46:e14330. [PMID: 35848392 DOI: 10.1111/jfbc.14330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/24/2022] [Accepted: 06/15/2022] [Indexed: 11/29/2022]
Abstract
As freeze/thaw procedure leads to inevitable drip loss, elucidation of mechanism on dynamic changes in water holding capacity (WHC) of muscle is urgently needed. In this study, the proteomic profile by DIA-based strategy, muscle microstructure, water mobility, and WHC indices of bovine longissimus dorsi muscles were investigated under different freezing conditions as well as the correlations among them. Results indicated that slow freezing (SF) sample exhibited significantly higher water mobility, thaw loss, total loss, and shear force value than the samples subjected to fast freezing (FF) and non-frozen control (CON). According to the protein profile, we have identified 272 differential abundance proteins (DAPs), in which more significant proteome changes were found in SF/CON samples as compared with FF/CON. Among the 132 DAPs in FF/SF comparison, correlation analysis revealed that MYL3, DES, SYNE2, EXR, RPL35A, RPS6, and Hsp40 were closely correlated with T23 , thaw loss, and total loss. Accordingly, we considered those seven proteins as potential biomarkers related to WHC of frozen muscle. Our study should give a further understanding on mechanisms behind the various WHC of muscle when subjected to different freezing conditions. PRACTICAL APPLICATIONS: Freezing plays a key role in the preservation method for meat and meat products. However, the drip loss during freezing and subsequent thawing procedure causes considerable economic and nutritional losses. To minimize the losses, elucidation of mechanism on the mechanism of thaw loss formation is urgently needed. DIA-based proteomics is a novel, robust method that provides further understanding on the mechanisms behind the dynamic changes in water holding capacity of muscle. The screened protein biomarkers in frozen muscle would play key roles in the development of WHC, especially for the thaw loss formation. Through this perspective, we can explain the origin of thaw loss and the variation under different freezing conditions, which should provide the meat industries with theoretical basis for reducing losses.
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Affiliation(s)
- Shuyi Qian
- Chinese Academy of Agricultural Sciences, Institute of Food Science and Technology, Beijing, China.,Unit of Food Science and Formulation, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Xia Li
- Chinese Academy of Agricultural Sciences, Institute of Food Science and Technology, Beijing, China
| | - Chengjiang Liu
- Institute of Agro-Products Processing Science and Technology, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
| | - Chunhui Zhang
- Chinese Academy of Agricultural Sciences, Institute of Food Science and Technology, Beijing, China
| | - Christophe Blecker
- Unit of Food Science and Formulation, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
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4
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Ravuri HG, Noor Z, Mills PC, Satake N, Sadowski P. Data-Independent Acquisition Enables Robust Quantification of 400 Proteins in Non-Depleted Canine Plasma. Proteomes 2022; 10:9. [PMID: 35324581 PMCID: PMC8953371 DOI: 10.3390/proteomes10010009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/25/2022] [Accepted: 02/22/2022] [Indexed: 12/30/2022] Open
Abstract
Mass spectrometry-based plasma proteomics offers a major advance for biomarker discovery in the veterinary field, which has traditionally been limited to quantification of a small number of proteins using biochemical assays. The development of foundational data and tools related to sequential window acquisition of all theoretical mass spectra (SWATH)-mass spectrometry has allowed for quantitative profiling of a significant number of plasma proteins in humans and several animal species. Enabling SWATH in dogs enhances human biomedical research as a model species, and significantly improves diagnostic and disease monitoring capability. In this study, a comprehensive peptide spectral library specific to canine plasma proteome was developed and evaluated using SWATH for protein quantification in non-depleted dog plasma. Specifically, plasma samples were subjected to various orthogonal fractionation and digestion techniques, and peptide fragmentation data corresponding to over 420 proteins was collected. Subsequently, a SWATH-based assay was introduced that leveraged the developed resource and that enabled reproducible quantification of 400 proteins in non-depleted plasma samples corresponding to various disease conditions. The ability to profile the abundance of such a significant number of plasma proteins using a single method in dogs has the potential to accelerate biomarker discovery studies in this species.
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Affiliation(s)
- Halley Gora Ravuri
- School of Veterinary Science, The University of Queensland, Gatton, QLD 4343, Australia; (H.G.R.); (P.C.M.)
| | - Zainab Noor
- ProCan, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia;
| | - Paul C. Mills
- School of Veterinary Science, The University of Queensland, Gatton, QLD 4343, Australia; (H.G.R.); (P.C.M.)
| | - Nana Satake
- School of Veterinary Science, The University of Queensland, Gatton, QLD 4343, Australia; (H.G.R.); (P.C.M.)
| | - Pawel Sadowski
- Central Analytical Research Facility, Queensland University of Technology, Brisbane, QLD 4000, Australia
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5
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Modi T, Regufe da Mota S, Gervais D. l-Asparaginase and HCP quantification by SWATH LC-MS/MS for new and improved purification step in Erwinia chrysanthemil-asparaginase manufacture. J Pharm Biomed Anal 2021; 209:114537. [PMID: 34929569 DOI: 10.1016/j.jpba.2021.114537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/10/2021] [Accepted: 12/10/2021] [Indexed: 11/16/2022]
Abstract
Erwinase® or Erwinaze® are the proprietary names for the L-asparaginase enzyme derived from Erwinia chrysanthemi.L-asparaginase is an integral part of the treatment of Acute Lymphoblastic Leukaemia (ALL) in children and adolescents. E. chrysanthemiL-asparaginase was first developed in the early 1970s at Porton Down and is currently manufactured by Porton Biopharma Ltd. One of the early purification steps during E. chrysanthemiL-asparaginase manufacture, involves use of batch cation exchange carboxymethyl resin, and alternatives to this older technology are currently under investigation using mass spectrometry to understand the impact of resin changes on the impurity profile. In this study, a novel SWATH library was developed for E. chrysanthemi proteome and used to evaluate this potential process change on product yield and host cell protein (HCP) profile and clearance. An ELISA assay is currently used as a quality control release test for quantifying HCPs at the Drug Substance (DS) stage, but these early extract samples are too crude for interference-free analysis by ELISA. Given that ELISA assay could not be used in the assessment of new resin options, SWATH LC-MS/MS analysis proved to be pivotal in selecting a resin for further scale-up and implementation. The data quantified that L-asparaginase from the new process step was 2.28-fold higher in concentration than in legacy-process samples. The new step, using a modern ion exchanger, was at least equivalent and in some cases outperformed the legacy resin step in terms of HCP clearance for 78.2% of total HCPs (528 of 675 total proteins).
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Affiliation(s)
- Tapasvi Modi
- Porton Biopharma Limited, Porton Down, Salisbury, Wiltshire SP4 0JG, UK
| | | | - David Gervais
- Porton Biopharma Limited, Porton Down, Salisbury, Wiltshire SP4 0JG, UK
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6
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Ge W, Liang X, Zhang F, Hu Y, Xu L, Xiang N, Sun R, Liu W, Xue Z, Yi X, Sun Y, Wang B, Zhu J, Lu C, Zhan X, Chen L, Wu Y, Zheng Z, Gong W, Wu Q, Yu J, Ye Z, Teng X, Huang S, Zheng S, Liu T, Yuan C, Guo T. Computational Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage and Applications to 15 Tumors. J Proteome Res 2021; 20:5392-5401. [PMID: 34748352 DOI: 10.1021/acs.jproteome.1c00640] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Efficient peptide and protein identifications from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on a project-specific spectral library with a suitable size. Here, we describe subLib, a computational strategy for optimizing the spectral library for a specific DIA data set based on a comprehensive spectral library, requiring the preliminary analysis of the DIA data set. Compared with the pan-human library strategy, subLib achieved a 41.2% increase in peptide precursor identifications and a 35.6% increase in protein group identifications in a test data set of six colorectal tumor samples. We also applied this strategy to 389 carcinoma samples from 15 tumor data sets: up to a 39.2% increase in peptide precursor identifications and a 19.0% increase in protein group identifications were observed. Our strategy for spectral library size optimization thus successfully proved to deepen the proteome coverages of DIA-MS data.
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Affiliation(s)
- Weigang Ge
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1, Yunmeng Road, Cloud Town, Xihu District, Hangzhou 310024, Zhejiang Province, China
| | - Xiao Liang
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Fangfei Zhang
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Yifan Hu
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1, Yunmeng Road, Cloud Town, Xihu District, Hangzhou 310024, Zhejiang Province, China
| | - Luang Xu
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Nan Xiang
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1, Yunmeng Road, Cloud Town, Xihu District, Hangzhou 310024, Zhejiang Province, China
| | - Rui Sun
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Wei Liu
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1, Yunmeng Road, Cloud Town, Xihu District, Hangzhou 310024, Zhejiang Province, China
| | - Zhangzhi Xue
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Xiao Yi
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1, Yunmeng Road, Cloud Town, Xihu District, Hangzhou 310024, Zhejiang Province, China
| | - Yaoting Sun
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Bo Wang
- Department of Pathology, The First Affiliated Hospital of College of Medicine, Zhejiang University, Hangzhou 310024, Zhejiang Province, China
| | - Jiang Zhu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, Hubei Province, China
| | - Cong Lu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, Hubei Province, China
| | - Xiaolu Zhan
- Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Lirong Chen
- Department of Pathology, The Second Affiliated Hospital of College of Medicine, Zhejiang University, Hangzhou 310009, Zhejiang Province, China
| | - Yan Wu
- Department of Orthopaedics, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang Province, China.,Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province, Hangzhou 310020, Zhejiang Province, China
| | - Zhiguo Zheng
- The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou 310022, Zhejiang Province, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang Province, China
| | - Wangang Gong
- The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou 310022, Zhejiang Province, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang Province, China
| | - Qijun Wu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang 110000, Liaoning Province, China
| | - Jiekai Yu
- Cancer Institute, Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China
| | - Zhaoming Ye
- Department of Orthopaedics, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang Province, China.,Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province, Hangzhou 310020, Zhejiang Province, China
| | - Xiaodong Teng
- Department of Pathology, The First Affiliated Hospital of College of Medicine, Zhejiang University, Hangzhou 310024, Zhejiang Province, China
| | - Shiang Huang
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, Hubei Province, China
| | - Shu Zheng
- Cancer Institute, Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China
| | - Tong Liu
- Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Chunhui Yuan
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Tiannan Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
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7
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Quantification of Changes in Protein Expression Using SWATH Proteomics. Methods Mol Biol 2021. [PMID: 34236656 DOI: 10.1007/978-1-0716-1641-3_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Sequential Window Acquisition of all THeoretical fragment ion spectra (SWATH) is a data independent acquisition mode used to accurately quantify thousands of proteins in a biological sample in a single run. It exploits fast scanning hybrid mass spectrometers to combine accuracy, reproducibility and sensitivity. This method requires the use of ion libraries, a sort of databases of spectral and chromatographic information about the proteins to be quantified. In this chapter, a typical workflow of SWATH experiment is described, from the sample preparation to the analysis of proteomics data.
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8
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Jin D, Liu H, Bu L, Ke Q, Li Z, Han W, Zhu S, Liu C. Comparative Analysis of Whey Proteins in Human Milk Using a Data-Independent Acquisition Proteomics Approach during the Lactation Period. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:4319-4330. [PMID: 33788563 DOI: 10.1021/acs.jafc.1c00186] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Human milk (HM) is the primary source of nutrients and bioactive components that supports the growth and development of infants. However, the proteins present in human milk may change depending on the period of lactation. In this light, the objective of the present study was to evaluate the effect of lactation period on HM utilizing a data-independent acquisition (DIA) approach to identify the differences in HM whey protein proteomes. As part of the study, whey proteins of January, February, and June in human milk were studied. The results identified a total of 1563 proteins in HM whey proteins of which 114 groups were subunits of differentially expressed proteins as revealed by cluster analysis. Protein expression was observed to be affected by the period of lactation with expression levels of plasminogen, thrombospondin-1, and tenascin higher during January, keratin, type I cytoskeletal 9 highest in February, and transcobalamin-1 highest in June. The results of this study contribute to expand our understanding of the human whey proteome but also provide strong evidence for the nutritional difference of HM during different lactation periods.
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Affiliation(s)
- Dengpeng Jin
- The Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Huan Liu
- The Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Lingling Bu
- The Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Qianhua Ke
- The Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Zhongyi Li
- The Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Wenna Han
- The Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Siyu Zhu
- The Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Chunhong Liu
- The Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science, South China Agricultural University, Guangzhou 510642, China
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9
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Ahn SB, Kamath KS, Mohamedali A, Noor Z, Wu JX, Pascovici D, Adhikari S, Cheruku HR, Guillemin GJ, McKay MJ, Nice EC, Baker MS. Use of a Recombinant Biomarker Protein DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins. J Proteome Res 2021; 20:2374-2389. [PMID: 33752330 DOI: 10.1021/acs.jproteome.0c00898] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). In this proof-of-concept study, we employed a mixture of selected recombinant proteins in DDA libraries to subsequently identify (not quantify) cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 recombinant human proteins that had been previously implicated as possible cancer biomarkers from both our own and other studies. The rPSL was then used to identify proteins from nondepleted colorectal cancer (CRC) EDTA plasmas by SWATH-MS. Most (32/36) of the proteins used in the rPSL were reliably identified from CRC plasma samples, including 8 proteins (i.e., BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency protein inference MS according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and postdigestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 could be identified using DDA-MS. The 20 additional proteins exclusively identified using the rPSL SWATH approach were almost exclusively lower abundance (i.e., <10 ng/mL) proteins. To mitigate justified FDR concerns, and to replicate a more typical library creation approach, the DDA rPSL library was merged with a human plasma DDA library and SWATH identification repeated using such a merged library. The majority (33/36) of the low abundance plasma proteins added from the rPSL were still able to be identified using such a merged library when high-stringency HPP Guidelines v3.0 protein inference criteria were applied to our data set. The MS data set has been deposited to ProteomeXchange Consortium via the PRIDE partner repository (PXD022361).
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Affiliation(s)
- Seong Beom Ahn
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Karthik S Kamath
- Australian Proteome Analysis Facility (APAF), Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Abidali Mohamedali
- Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Zainab Noor
- ProCan, Children's Medical Research Institute, The University of Sydney, Westmead, Newtown, NSW 2042, Australia
| | - Jemma X Wu
- Australian Proteome Analysis Facility (APAF), Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Dana Pascovici
- Australian Proteome Analysis Facility (APAF), Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Subash Adhikari
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Harish R Cheruku
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Gilles J Guillemin
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Matthew J McKay
- Australian Proteome Analysis Facility (APAF), Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Edouard C Nice
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, VIC 3800, Australia
| | - Mark S Baker
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia
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10
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Chantada-Vázquez MDP, García Vence M, Serna A, Núñez C, Bravo SB. SWATH-MS Protocols in Human Diseases. Methods Mol Biol 2021; 2259:105-141. [PMID: 33687711 DOI: 10.1007/978-1-0716-1178-4_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Identification of molecular biomarkers for human diseases is one of the most important disciplines in translational science as it helps to elucidate their origin and early progression. Thus, it is a key factor in better diagnosis, prognosis, and treatment. Proteomics can help to solve the problem of sample complexity when the most common primary sample specimens were analyzed: organic fluids of easy access. The latest developments in high-throughput and label-free quantitative proteomics (SWATH-MS), together with more advanced liquid chromatography, have enabled the analysis of large sample sets with the sensitivity and depth needed to succeed in this task. In this chapter, we show different sample processing methods (major protein depletion, digestion, etc.) and a micro LC-SWATH-MS protocol to identify/quantify several proteins in different types of samples (serum/plasma, saliva, urine, tears).
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Affiliation(s)
| | - María García Vence
- Proteomic Unit, Instituto de Investigaciones Sanitarias-IDIS, Complejo Hospitalario Universitario de Santiago de Compostela (CHUS), Santiago de Compostela, Spain
| | | | - Cristina Núñez
- Research Unit, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de Saúde (SERGAS), Lugo, Spain.
| | - Susana B Bravo
- Proteomic Unit, Instituto de Investigaciones Sanitarias-IDIS, Complejo Hospitalario Universitario de Santiago de Compostela (CHUS), Santiago de Compostela, Spain.
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11
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Wen B, Zeng W, Liao Y, Shi Z, Savage SR, Jiang W, Zhang B. Deep Learning in Proteomics. Proteomics 2020; 20:e1900335. [PMID: 32939979 PMCID: PMC7757195 DOI: 10.1002/pmic.201900335] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 09/14/2020] [Indexed: 12/17/2022]
Abstract
Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post-translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich scientific research domains. Here, a comprehensive overview of deep learning applications in proteomics, including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding prediction, and protein structure prediction, is provided. Limitations and the future directions of deep learning in proteomics are also discussed. This review will provide readers an overview of deep learning and how it can be used to analyze proteomics data.
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Affiliation(s)
- Bo Wen
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Wen‐Feng Zeng
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS)Chinese Academy of SciencesInstitute of Computing TechnologyBeijing100190China
| | - Yuxing Liao
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Zhiao Shi
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Sara R. Savage
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Wen Jiang
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Bing Zhang
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
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12
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Gao X, Li Q, Liu Y, Zeng R. Multi-in-One: Multiple-Proteases, One-Hour-Shot Strategy for Fast and High-Coverage Phosphoproteomic Investigation. Anal Chem 2020; 92:8943-8951. [DOI: 10.1021/acs.analchem.0c00906] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Xiaojing Gao
- CAS Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences; University of Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China
| | - Qingrun Li
- CAS Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences; University of Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
| | - Yansheng Liu
- Department of Pharmacology, Cancer Biology Institute, Yale University School of Medicine, West Haven, Connecticut 06516, United States
| | - Rong Zeng
- CAS Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences; University of Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
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13
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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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14
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Ludwig C, Gillet L, Rosenberger G, Amon S, Collins BC, Aebersold R. Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol Syst Biol 2018; 14:e8126. [PMID: 30104418 PMCID: PMC6088389 DOI: 10.15252/msb.20178126] [Citation(s) in RCA: 625] [Impact Index Per Article: 104.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 05/11/2018] [Accepted: 05/15/2018] [Indexed: 01/16/2023] Open
Abstract
Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH-MS is a specific variant of data-independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH-MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH-MS data, a strategy based on peptide-centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH-MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH-MS data using peptide-centric scoring. Furthermore, concepts on how to improve SWATH-MS data acquisition, potential trade-offs of parameter settings and alternative data analysis strategies are discussed.
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Affiliation(s)
- Christina Ludwig
- Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), Technical University of Munich (TUM), Freising, Germany
| | - Ludovic Gillet
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - George Rosenberger
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Sabine Amon
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Ben C Collins
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Faculty of Science, University of Zurich, Zurich, Switzerland
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15
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Heissel S, Bunkenborg J, Kristiansen MP, Holmbjerg AF, Grimstrup M, Mørtz E, Kofoed T, Højrup P. Evaluation of spectral libraries and sample preparation for DIA-LC-MS analysis of host cell proteins: A case study of a bacterially expressed recombinant biopharmaceutical protein. Protein Expr Purif 2018. [DOI: 10.1016/j.pep.2018.03.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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16
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Yan Z, Yan R. Exploring the Potential of Data-Independent Acquisition Proteomics Using Untargeted All-Ion Quantitation: Application to Tumor Subtype Diagnosis. Anal Chem 2018. [PMID: 29522333 DOI: 10.1021/acs.analchem.7b03920] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Maximizing the recovery of meaningful biological information can facilitate proteomics-guided early detection and precise treatment of diseases. However, the conventional protein and peptide level targeted quantification of untargeted data independent acquisition (DIA) such as sequential window acquisition of all theoretical spectra (SWATH) is not necessarily descriptive of all information. Untargeted all-ion quantification theoretically could retrieve more features in SWATH digital maps by circumventing the initial identification process but is intrinsically susceptible to errors because of the extreme complexity of proteome samples and the poor selectivity of a single ion. In this study, we optimized and applied the untargeted all-ion quantification of SWATH data to differentiate tumor subtypes. Large peptides and low abundant peptides benefited more from untargeted all-ion quantification. Top-ranked significant ions were linked to their corresponding ion envelops, where multiple correlated ions were used for measurement and only ion envelopes containing at least three ions with consistent intensity ratio were kept as refined differentiating features. Multivariate statistical analysis revealed that for the tested data set, the refined markers discovered by untargeted SWATH analysis showed comparable diagnostic power to protein and peptide markers. Limitations and benefits of the approach are further discussed.
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Affiliation(s)
- Zhixiang Yan
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences , University of Macau , Taipa, Macao , China.,Zhuhai UM Science & Technology Research Institute , Zhuhai 519080 , China
| | - Ru Yan
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences , University of Macau , Taipa, Macao , China.,Zhuhai UM Science & Technology Research Institute , Zhuhai 519080 , China
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17
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Wu JX, Pascovici D, Ignjatovic V, Song X, Krisp C, Molloy MP. Improving Protein Detection Confidence Using SWATH-Mass Spectrometry with Large Peptide Reference Libraries. Proteomics 2017; 17. [DOI: 10.1002/pmic.201700174] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Revised: 07/27/2017] [Indexed: 11/10/2022]
Affiliation(s)
- Jemma X. Wu
- Australian Proteome Analysis Facility (APAF); Department of Chemistry and Biomolecular Sciences; Macquarie University; Sydney Australia
| | - Dana Pascovici
- Australian Proteome Analysis Facility (APAF); Department of Chemistry and Biomolecular Sciences; Macquarie University; Sydney Australia
| | - Vera Ignjatovic
- Hematology Research Laboratory; Murdoch Children's Research Institute; Melbourne Australia
| | - Xiaomin Song
- Australian Proteome Analysis Facility (APAF); Department of Chemistry and Biomolecular Sciences; Macquarie University; Sydney Australia
| | - Christoph Krisp
- Australian Proteome Analysis Facility (APAF); Department of Chemistry and Biomolecular Sciences; Macquarie University; Sydney Australia
| | - Mark P. Molloy
- Australian Proteome Analysis Facility (APAF); Department of Chemistry and Biomolecular Sciences; Macquarie University; Sydney Australia
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18
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Huang R, Chen Z, He L, He N, Xi Z, Li Z, Deng Y, Zeng X. Mass spectrometry-assisted gel-based proteomics in cancer biomarker discovery: approaches and application. Theranostics 2017; 7:3559-3572. [PMID: 28912895 PMCID: PMC5596443 DOI: 10.7150/thno.20797] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2017] [Accepted: 07/12/2017] [Indexed: 12/13/2022] Open
Abstract
There is a critical need for the discovery of novel biomarkers for early detection and targeted therapy of cancer, a major cause of deaths worldwide. In this respect, proteomic technologies, such as mass spectrometry (MS), enable the identification of pathologically significant proteins in various types of samples. MS is capable of high-throughput profiling of complex biological samples including blood, tissues, urine, milk, and cells. MS-assisted proteomics has contributed to the development of cancer biomarkers that may form the foundation for new clinical tests. It can also aid in elucidating the molecular mechanisms underlying cancer. In this review, we discuss MS principles and instrumentation as well as approaches in MS-based proteomics, which have been employed in the development of potential biomarkers. Furthermore, the challenges in validation of MS biomarkers for their use in clinical practice are also reviewed.
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Affiliation(s)
- Rongrong Huang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Zhongsi Chen
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Lei He
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Nongyue He
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
- Economical Forest Cultivation and Utilization of 2011 Collaborative Innovation Center in Hunan Province, Hunan Key Laboratory of Green Chemistry and Application of Biological Nanotechnology; Hunan University of Technology, Zhuzhou 412007, China
| | - Zhijiang Xi
- School of Medicine, Yangtze University, Jingzhou 434023, China
| | - Zhiyang Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
- Department of Clinical Laboratory, the Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Yan Deng
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
- Economical Forest Cultivation and Utilization of 2011 Collaborative Innovation Center in Hunan Province, Hunan Key Laboratory of Green Chemistry and Application of Biological Nanotechnology; Hunan University of Technology, Zhuzhou 412007, China
| | - Xin Zeng
- Nanjing Maternity and Child Health Medical Institute, Obstetrics and Gynecology Hospital Affiliated to Nanjing Medical University, Nanjing 210004, China
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19
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Govaert E, Van Steendam K, Willems S, Vossaert L, Dhaenens M, Deforce D. Comparison of fractionation proteomics for local SWATH library building. Proteomics 2017; 17:1700052. [PMID: 28664598 PMCID: PMC5601298 DOI: 10.1002/pmic.201700052] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 05/23/2017] [Accepted: 06/28/2017] [Indexed: 01/08/2023]
Abstract
For data-independent acquisition by means of sequential window acquisition of all theoretical fragment ion spectra (SWATH), a reference library of data-dependent acquisition (DDA) runs is typically used to correlate the quantitative data from the fragment ion spectra with peptide identifications. The quality and coverage of such a reference library is therefore essential when processing SWATH data. In general, library sizes can be increased by reducing the impact of DDA precursor selection with replicate runs or fractionation. However, these strategies can affect the match between the library and SWATH measurement, and thus larger library sizes do not necessarily correspond to improved SWATH quantification. Here, three fractionation strategies to increase local library size were compared to standard library building using replicate DDA injection: protein SDS-PAGE fractionation, peptide high-pH RP-HPLC fractionation and MS-acquisition gas phase fractionation. The impact of these libraries on SWATH performance was evaluated in terms of the number of extracted peptides and proteins, the match quality of the peptides and the extraction reproducibility of the transitions. These analyses were conducted using the hydrophilic proteome of differentiating human embryonic stem cells. Our results show that SWATH quantitative results and interpretations are affected by choice of fractionation technique. Data are available via ProteomeXchange with identifier PXD006190.
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Affiliation(s)
- Elisabeth Govaert
- Laboratory of Pharmaceutical BiotechnologyGhent UniversityGhentBelgium
| | | | - Sander Willems
- Laboratory of Pharmaceutical BiotechnologyGhent UniversityGhentBelgium
| | - Liesbeth Vossaert
- Laboratory of Pharmaceutical BiotechnologyGhent UniversityGhentBelgium
| | - Maarten Dhaenens
- Laboratory of Pharmaceutical BiotechnologyGhent UniversityGhentBelgium
| | - Dieter Deforce
- Laboratory of Pharmaceutical BiotechnologyGhent UniversityGhentBelgium
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20
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Kang Y, Burton L, Lau A, Tate S. SWATH-ID: An instrument method which combines identification and quantification in a single analysis. Proteomics 2017; 17:e1500522. [PMID: 28387034 DOI: 10.1002/pmic.201500522] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Revised: 02/28/2017] [Accepted: 03/24/2017] [Indexed: 11/09/2022]
Abstract
Data-independent acquisition (DIA) approaches, such as SWATH® -MS, are showing great potential to reliably quantify significant numbers of peptides and proteins in an unbiased manner. These developments have enhanced interest in developing a single DIA method that integrates qualitative and quantitative analysis, eliminating the need of a prebuilt library of peptide spectra, which are created through data-dependent acquisition methods or from public repositories. Here, we introduce a new DIA approach, referred to as "SWATH-ID," which was developed to allow peptide identification as well as quantitation. The SWATH-ID method is composed of small Q1 windows, achieving better selectivity and thus significantly improving high-confidence peptide extractions from data files. Furthermore, the SWATH-ID approach transmits precursor ions without fragmentation as well as their fragments within the same SWATH acquisition period. This provides a single scan that includes all precursor ions within the isolation window as well as a record of all of their fragment ions, substantially negating the need for a survey scan. In this way all precursors present in a small Q1 window are associated with their fragment ions, improving the identification specificity and providing a more comprehensive and in-depth view of protein and peptide species in complex samples.
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21
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Anjo SI, Santa C, Manadas B. SWATH-MS as a tool for biomarker discovery: From basic research to clinical applications. Proteomics 2017; 17. [DOI: 10.1002/pmic.201600278] [Citation(s) in RCA: 102] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 01/05/2017] [Accepted: 01/23/2017] [Indexed: 12/16/2022]
Affiliation(s)
- Sandra Isabel Anjo
- CNC - Center for Neuroscience and Cell Biology; University of Coimbra; Coimbra Portugal
- Faculty of Sciences and Technology; University of Coimbra; Coimbra Portugal
| | - Cátia Santa
- CNC - Center for Neuroscience and Cell Biology; University of Coimbra; Coimbra Portugal
- Institute for Interdisciplinary Research (III); University of Coimbra; Coimbra Portugal
| | - Bruno Manadas
- CNC - Center for Neuroscience and Cell Biology; University of Coimbra; Coimbra Portugal
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22
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Li S, Cao Q, Xiao W, Guo Y, Yang Y, Duan X, Shui W. Optimization of Acquisition and Data-Processing Parameters for Improved Proteomic Quantification by Sequential Window Acquisition of All Theoretical Fragment Ion Mass Spectrometry. J Proteome Res 2017; 16:738-747. [PMID: 27995803 DOI: 10.1021/acs.jproteome.6b00767] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Proteomic analysis with data-independent acquisition (DIA) approaches represented by the sequential window acquisition of all theoretical fragment ion spectra (SWATH) technique has gained intense interest in recent years because DIA is able to overcome the intrinsic weakness of conventional data-dependent acquisition (DDA) methods and afford higher throughout and reproducibility for proteome-wide quantification. Although the raw mass spectrometry (MS) data quality and the data-mining workflow conceivably influence the throughput, accuracy and consistency of SWATH-based proteomic quantification, there lacks a systematic evaluation and optimization of the acquisition and data-processing parameters for SWATH MS analysis. Herein, we evaluated the impact of major acquisition parameters such as the precursor mass range, isolation window width and accumulation time as well as the data-processing variables including peak extraction criteria and spectra library selection on SWATH performance. Fine tuning these interdependent parameters can further improve the throughput and accuracy of SWATH quantification compared to the original setting adopted in most SWATH proteomic studies. Furthermore, we compared the effectiveness of two widely used peak extraction software PeakView and Spectronaut in discovery of differentially expressed proteins in a biological context. Our work is believed to contribute to a deeper understanding of the critical factors in SWATH MS experiments and help researchers optimize their SWATH parameters and workflows depending on the sample type, available instrument and software.
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Affiliation(s)
- Shanshan Li
- iHuman Institute, ShanghaiTech University , Shanghai 201210, China
| | - Qichen Cao
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences , Tianjin 300308, China
| | - Weidi Xiao
- College of Life Sciences, Nankai University , Tianjin 300071, China
| | - Yufeng Guo
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences , Tianjin 300308, China
| | - Yunfei Yang
- College of Life Sciences, Nankai University , Tianjin 300071, China
| | - Xiaoxiao Duan
- College of Life Sciences, Nankai University , Tianjin 300071, China
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University , Shanghai 201210, China
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23
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Schmidlin T, Garrigues L, Lane CS, Mulder TC, van Doorn S, Post H, de Graaf EL, Lemeer S, Heck AJR, Altelaar AFM. Assessment of SRM, MRM3, and DIA for the targeted analysis of phosphorylation dynamics in non-small cell lung cancer. Proteomics 2016; 16:2193-205. [DOI: 10.1002/pmic.201500453] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 04/12/2016] [Accepted: 05/20/2016] [Indexed: 12/21/2022]
Affiliation(s)
- Thierry Schmidlin
- Biomolecular Mass Spectrometry and Proteomics; Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences; Utrecht University and Netherlands Proteomics Centre; Utrecht The Netherlands
| | - Luc Garrigues
- Biomolecular Mass Spectrometry and Proteomics; Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences; Utrecht University and Netherlands Proteomics Centre; Utrecht The Netherlands
| | | | - T. Celine Mulder
- Biomolecular Mass Spectrometry and Proteomics; Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences; Utrecht University and Netherlands Proteomics Centre; Utrecht The Netherlands
| | - Sander van Doorn
- Biomolecular Mass Spectrometry and Proteomics; Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences; Utrecht University and Netherlands Proteomics Centre; Utrecht The Netherlands
| | - Harm Post
- Biomolecular Mass Spectrometry and Proteomics; Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences; Utrecht University and Netherlands Proteomics Centre; Utrecht The Netherlands
| | - Erik L. de Graaf
- Biomolecular Mass Spectrometry and Proteomics; Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences; Utrecht University and Netherlands Proteomics Centre; Utrecht The Netherlands
- Current address: Erik L. de Graaf, Fondazione Pisana per la Scienza ONLUS; Via Panfilo Castaldi 2; 56121 Pisa Italy
| | - Simone Lemeer
- Biomolecular Mass Spectrometry and Proteomics; Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences; Utrecht University and Netherlands Proteomics Centre; Utrecht The Netherlands
| | - Albert J. R. Heck
- Biomolecular Mass Spectrometry and Proteomics; Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences; Utrecht University and Netherlands Proteomics Centre; Utrecht The Netherlands
| | - A. F. Maarten Altelaar
- Biomolecular Mass Spectrometry and Proteomics; Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences; Utrecht University and Netherlands Proteomics Centre; Utrecht The Netherlands
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24
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Wu JX, Song X, Pascovici D, Zaw T, Care N, Krisp C, Molloy MP. SWATH Mass Spectrometry Performance Using Extended Peptide MS/MS Assay Libraries. Mol Cell Proteomics 2016; 15:2501-14. [PMID: 27161445 DOI: 10.1074/mcp.m115.055558] [Citation(s) in RCA: 82] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Indexed: 12/26/2022] Open
Abstract
The use of data-independent acquisition methods such as SWATH for mass spectrometry based proteomics is usually performed with peptide MS/MS assay libraries which enable identification and quantitation of peptide peak areas. Reference assay libraries can be generated locally through information dependent acquisition, or obtained from community data repositories for commonly studied organisms. However, there have been no studies performed to systematically evaluate how locally generated or repository-based assay libraries affect SWATH performance for proteomic studies. To undertake this analysis, we developed a software workflow, SwathXtend, which generates extended peptide assay libraries by integration with a local seed library and delivers statistical analysis of SWATH-quantitative comparisons. We designed test samples using peptides from a yeast extract spiked into peptides from human K562 cell lysates at three different ratios to simulate protein abundance change comparisons. SWATH-MS performance was assessed using local and external assay libraries of varying complexities and proteome compositions. These experiments demonstrated that local seed libraries integrated with external assay libraries achieve better performance than local assay libraries alone, in terms of the number of identified peptides and proteins and the specificity to detect differentially abundant proteins. Our findings show that the performance of extended assay libraries is influenced by the MS/MS feature similarity of the seed and external libraries, while statistical analysis using multiple testing corrections increases the statistical rigor needed when searching against large extended assay libraries.
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Affiliation(s)
- Jemma X Wu
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Xiaomin Song
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Dana Pascovici
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Thiri Zaw
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Natasha Care
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Christoph Krisp
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Mark P Molloy
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
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25
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Mayne J, Ning Z, Zhang X, Starr AE, Chen R, Deeke S, Chiang CK, Xu B, Wen M, Cheng K, Seebun D, Star A, Moore JI, Figeys D. Bottom-Up Proteomics (2013-2015): Keeping up in the Era of Systems Biology. Anal Chem 2015; 88:95-121. [PMID: 26558748 DOI: 10.1021/acs.analchem.5b04230] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Janice Mayne
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Zhibin Ning
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Xu Zhang
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Amanda E Starr
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Rui Chen
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Shelley Deeke
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Cheng-Kang Chiang
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Bo Xu
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Ming Wen
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Kai Cheng
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Deeptee Seebun
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Alexandra Star
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Jasmine I Moore
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Daniel Figeys
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
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26
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Krisp C, Yang H, van Soest R, Molloy MP. Online Peptide fractionation using a multiphasic microfluidic liquid chromatography chip improves reproducibility and detection limits for quantitation in discovery and targeted proteomics. Mol Cell Proteomics 2015; 14:1708-19. [PMID: 25850434 DOI: 10.1074/mcp.m114.046425] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Indexed: 12/19/2022] Open
Abstract
Comprehensive proteomic profiling of biological specimens usually requires multidimensional chromatographic peptide fractionation prior to mass spectrometry. However, this approach can suffer from poor reproducibility because of the lack of standardization and automation of the entire workflow, thus compromising performance of quantitative proteomic investigations. To address these variables we developed an online peptide fractionation system comprising a multiphasic liquid chromatography (LC) chip that integrates reversed phase and strong cation exchange chromatography upstream of the mass spectrometer (MS). We showed superiority of this system for standardizing discovery and targeted proteomic workflows using cancer cell lysates and nondepleted human plasma. Five-step multiphase chip LC MS/MS acquisition showed clear advantages over analyses of unfractionated samples by identifying more peptides, consuming less sample and often improving the lower limits of quantitation, all in highly reproducible, automated, online configuration. We further showed that multiphase chip LC fractionation provided a facile means to detect many N- and C-terminal peptides (including acetylated N terminus) that are challenging to identify in complex tryptic peptide matrices because of less favorable ionization characteristics. Given as much as 95% of peptides were detected in only a single salt fraction from cell lysates we exploited this high reproducibility and coupled it with multiple reaction monitoring on a high-resolution MS instrument (MRM-HR). This approach increased target analyte peak area and improved lower limits of quantitation without negatively influencing variance or bias. Further, we showed a strategy to use multiphase LC chip fractionation LC-MS/MS for ion library generation to integrate with SWATH(TM) data-independent acquisition quantitative workflows. All MS data are available via ProteomeXchange with identifier PXD001464.
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Affiliation(s)
- Christoph Krisp
- From the ‡Australian Proteome Analysis Facility (APAF), Department of Chemistry and Biomolecular Sciences, Macquarie University, 2109, Sydney, Australia
| | - Hao Yang
- §Eksigent, part of AB SCIEX, 94065, Redwood City, California
| | - Remco van Soest
- §Eksigent, part of AB SCIEX, 94065, Redwood City, California
| | - Mark P Molloy
- From the ‡Australian Proteome Analysis Facility (APAF), Department of Chemistry and Biomolecular Sciences, Macquarie University, 2109, Sydney, Australia;
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27
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Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat Protoc 2015; 10:426-41. [PMID: 25675208 DOI: 10.1038/nprot.2015.015] [Citation(s) in RCA: 220] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Targeted proteomics by selected/multiple reaction monitoring (S/MRM) or, on a larger scale, by SWATH (sequential window acquisition of all theoretical spectra) MS (mass spectrometry) typically relies on spectral reference libraries for peptide identification. Quality and coverage of these libraries are therefore of crucial importance for the performance of the methods. Here we present a detailed protocol that has been successfully used to build high-quality, extensive reference libraries supporting targeted proteomics by SWATH MS. We describe each step of the process, including data acquisition by discovery proteomics, assertion of peptide-spectrum matches (PSMs), generation of consensus spectra and compilation of MS coordinates that uniquely define each targeted peptide. Crucial steps such as false discovery rate (FDR) control, retention time normalization and handling of post-translationally modified peptides are detailed. Finally, we show how to use the library to extract SWATH data with the open-source software Skyline. The protocol takes 2-3 d to complete, depending on the extent of the library and the computational resources available.
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28
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Basak T, Bhat A, Malakar D, Pillai M, Sengupta S. In-depth comparative proteomic analysis of yeast proteome using iTRAQ and SWATH based MS. MOLECULAR BIOSYSTEMS 2015; 11:2135-43. [DOI: 10.1039/c5mb00234f] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
SWATH is capable of quantifying proteins of lower abundance as compared to iTRAQ.
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Affiliation(s)
- Trayambak Basak
- Genomics and Molecular Medicine
- CSIR-IGIB
- New Delhi-110020
- India
- Academy of Scientific & Innovative Research (AcSIR)
| | - Ajay Bhat
- Genomics and Molecular Medicine
- CSIR-IGIB
- New Delhi-110020
- India
- Academy of Scientific & Innovative Research (AcSIR)
| | | | | | - Shantanu Sengupta
- Genomics and Molecular Medicine
- CSIR-IGIB
- New Delhi-110020
- India
- Academy of Scientific & Innovative Research (AcSIR)
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