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Wang J, Tan H, Fu Y, Mishra A, Sun H, Wang Z, Wu Z, Wang X, Serrano GE, Beach TG, Peng J, High AA. Evaluation of Protein Identification and Quantification by the diaPASEF Method on timsTOF SCP. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:1253-1260. [PMID: 38754071 DOI: 10.1021/jasms.4c00067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
Accurate and precise quantification is crucial in modern proteomics, particularly in the context of exploring low-amount samples. While the innovative 4D-data-independent acquisition (DIA) quantitative proteomics facilitated by timsTOF mass spectrometers gives enhanced sensitivity and selectivity for protein identification, the diaPASEF (parallel accumulation-serial fragmentation combined with data-independent acquisition) parameters have not been systematically optimized, and a comprehensive evaluation of the quantification is currently lacking. In this study, we conducted a thorough optimization of key parameters on a timsTOF SCP instrument, including sample loading amount (50 ng), ramp/accumulation time (140 ms), isolation window width (20 m/z), and gradient time (60 min). To further improve the identification of proteins in low-amount samples, we utilized different column settings and introduced 0.02% n-dodecyl-β-d-maltoside (DDM) in the sample reconstitution solution, resulting in a remarkable 19-fold increase in protein identification at the single-cell-equivalent level. Moreover, a comprehensive comparison of protein quantification using a tandem mass tag reporter (TMT-reporter), complement TMT ions (TMTc), and diaPASEF revealed a strong correlation between these methods. Both diaPASEF and TMTc have effectively addressed the issue of ratio compression, highlighting the diaPASEF method's effectiveness in achieving accurate quantification data compared to TMT reporter quantification. Additionally, an in-depth analysis of in-group variation positioned diaPASEF between the TMT-reporter and TMTc methods. Therefore, diaPASEF quantification on the timsTOF SCP instrument emerges as a precise and accurate methodology for quantitative proteomics, especially for samples with small amounts.
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Affiliation(s)
- Ju Wang
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Haiyan Tan
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Yingxue Fu
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Ashutosh Mishra
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Huan Sun
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Zhen Wang
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Zhiping Wu
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Xusheng Wang
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Geidy E Serrano
- Banner Sun Health Research Institute, Sun City, Arizona 85351, United States
| | - Thomas G Beach
- Banner Sun Health Research Institute, Sun City, Arizona 85351, United States
| | - Junmin Peng
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Anthony A High
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
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Deep representation features from DreamDIA XMBD improve the analysis of data-independent acquisition proteomics. Commun Biol 2021; 4:1190. [PMID: 34650228 PMCID: PMC8517002 DOI: 10.1038/s42003-021-02726-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 09/27/2021] [Indexed: 12/24/2022] Open
Abstract
We developed DreamDIAXMBD (denoted as DreamDIA), a software suite based on a deep representation model for data-independent acquisition (DIA) data analysis. DreamDIA adopts a data-driven strategy to capture comprehensive information from elution patterns of peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. Specifically, in contrast to existing methods which use only 6 to 10 selected fragment ions from spectral libraries, DreamDIA extracts additional features from hundreds of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by nonlinear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA is publicly available at https://github.com/xmuyulab/DreamDIA-XMBD for high coverage and accuracy DIA data analysis.
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Noor Z, Mohamedali A, Ranganathan S. iSwathX 2.0 for Processing DDA Spectral Libraries for DIA Data Analysis. ACTA ACUST UNITED AC 2020; 70:e101. [PMID: 32478466 DOI: 10.1002/cpbi.101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The iSwathX web application processes and normalizes mass spectrometry-based proteomics spectral libraries generated in the data-dependent acquisition (DDA) approach. These libraries are stored in various proteomics repositories such as PeptideAtlas and NIST, or are user-generated and provide reference data for data-independent acquisition (DIA) targeted data extraction and analysis. iSwathX 2.0 can efficiently normalize DDA data from different instruments, gathered at different instances, and make it compatible with specific DIA experiments. Novel functions for parallel processing of DDA libraries and DIA report files, along with various data visualizations, are available in iSwathX 2.0. The step-by-step protocols provided here describe how the libraries are uploaded, processed, visualized, and downloaded using various modules of the application. They also provide detailed guidelines on the use of DIA report files for data analysis and visualization. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Processing, combining, and visualizing two DDA libraries Basic Protocol 2: Parallel processing and combination of multiple DDA libraries Basic Protocol 3: Downstream processing, comparison, and visualization of DIA report files.
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Affiliation(s)
- Zainab Noor
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia
| | - Abidali Mohamedali
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia
| | - Shoba Ranganathan
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia
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Xu M, Deng J, Xu K, Zhu T, Han L, Yan Y, Yao D, Deng H, Wang D, Sun Y, Chang C, Zhang X, Dai J, Yue L, Zhang Q, Cai X, Zhu Y, Duan H, Liu Y, Li D, Zhu Y, Radstake TRDJ, Balak DM, Xu D, Guo T, Lu C, Yu X. In-depth serum proteomics reveals biomarkers of psoriasis severity and response to traditional Chinese medicine. Theranostics 2019; 9:2475-2488. [PMID: 31131048 PMCID: PMC6526001 DOI: 10.7150/thno.31144] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 02/22/2019] [Indexed: 12/23/2022] Open
Abstract
Serum and plasma contain abundant biological information that reflect the body's physiological and pathological conditions and are therefore a valuable sample type for disease biomarkers. However, comprehensive profiling of the serological proteome is challenging due to the wide range of protein concentrations in serum. Methods: To address this challenge, we developed a novel in-depth serum proteomics platform capable of analyzing the serum proteome across ~10 orders or magnitude by combining data obtained from Data Independent Acquisition Mass Spectrometry (DIA-MS) and customizable antibody microarrays. Results: Using psoriasis as a proof-of-concept disease model, we screened 50 serum proteomes from healthy controls and psoriasis patients before and after treatment with traditional Chinese medicine (YinXieLing) on our in-depth serum proteomics platform. We identified 106 differentially-expressed proteins in psoriasis patients involved in psoriasis-relevant biological processes, such as blood coagulation, inflammation, apoptosis and angiogenesis signaling pathways. In addition, unbiased clustering and principle component analysis revealed 58 proteins discriminating healthy volunteers from psoriasis patients and 12 proteins distinguishing responders from non-responders to YinXieLing. To further demonstrate the clinical utility of our platform, we performed correlation analyses between serum proteomes and psoriasis activity and found a positive association between the psoriasis area and severity index (PASI) score with three serum proteins (PI3, CCL22, IL-12B). Conclusion: Taken together, these results demonstrate the clinical utility of our in-depth serum proteomics platform to identify specific diagnostic and predictive biomarkers of psoriasis and other immune-mediated diseases.
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Chen YT, Tsai CH, Chen CL, Yu JS, Chang YH. Development of biomarkers of genitourinary cancer using mass spectrometry-based clinical proteomics. J Food Drug Anal 2019; 27:387-403. [PMID: 30987711 PMCID: PMC9296213 DOI: 10.1016/j.jfda.2018.09.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Revised: 09/19/2018] [Accepted: 09/20/2018] [Indexed: 12/23/2022] Open
Abstract
Prostate, bladder and kidney cancer are the three most common types of genitourinary cancer in the world. Of these, prostate and bladder cancers are within the top 10 most common cancers in men. Notably, kidney cancer causes no obvious symptoms in the early stages. To satisfy clinical-management requirements, researchers have developed numerous biomarkers by applying proteomic approaches using clinical serum, urine and tissue specimens, as well as cell and animal models. Through application of biomarker pipeline protocols, including discovery, verification and validation phases, and mass-spectrometric based proteomic platforms coupled with multiplexed quantification assays, these studies have led to recent rapid progress in this area. With improvements in mass-spectrometric based proteomic techniques, numerous promising biomarker candidates and marker panels for various clinical purposes have been proposed. Verification of novel protein biomarker candidates is very resource demanding (e.g. on the clinical and laboratory sides). With the support of national consortia, it is now possible to investigate the future clinical use of such biomarker strategies and assess their cost-effectiveness in personalized medicine.
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Affiliation(s)
- Yi-Ting Chen
- Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan,
Taiwan
- Molecular Medicine Research Center, College of Medicine, Chang Gung University, Taoyuan,
Taiwan
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan,
Taiwan
- Department of Nephrology, Chang Gung Memorial Hospital, Linkou Medical Center, Taiwan University, Taoyuan,
Taiwan
- Corresponding author. Department of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Cheng-Han Tsai
- Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan,
Taiwan
| | - Chien-Lun Chen
- Department of Urology, Chang Gung Memorial Hospital, Taoyuan,
Taiwan
- College of Medicine, Chang Gung University, Taoyuan,
Taiwan
| | - Jau-Song Yu
- Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan,
Taiwan
- Molecular Medicine Research Center, College of Medicine, Chang Gung University, Taoyuan,
Taiwan
- Liver Research Center, Chang Gung Memorial Hospital, Linkou,
Taiwan
| | - Ying-Hsu Chang
- Division of Urology, Department of Surgery, LinKou Chang Gung Memorial Hospital, Taoyuan,
Taiwan
- Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan,
Taiwan
- Corresponding author. Division of Urology, Department of Surgery, LinKou Chang Gung Memorial Hospital, Taoyuan, Taiwan. E-mail addresses: (Y.-T. Chen), (Y.-H. Chang)
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Muntel J, Kirkpatrick J, Bruderer R, Huang T, Vitek O, Ori A, Reiter L. Comparison of Protein Quantification in a Complex Background by DIA and TMT Workflows with Fixed Instrument Time. J Proteome Res 2019; 18:1340-1351. [DOI: 10.1021/acs.jproteome.8b00898] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jan Muntel
- Biognosys AG, Wagistrasse 21, 8952 Schlieren, Switzerland
| | - Joanna Kirkpatrick
- Leibniz Institute on Aging, Fritz Lipmann Institute, Beutenbergstrasse 11, 07745 Jena, Germany
| | | | - Ting Huang
- Northeastern University, 360 Huntington Avenue, Boston, Massachusetts 02115, United States
| | - Olga Vitek
- Northeastern University, 360 Huntington Avenue, Boston, Massachusetts 02115, United States
| | - Alessandro Ori
- Leibniz Institute on Aging, Fritz Lipmann Institute, Beutenbergstrasse 11, 07745 Jena, Germany
| | - Lukas Reiter
- Biognosys AG, Wagistrasse 21, 8952 Schlieren, Switzerland
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Heringer AS, Santa-Catarina C, Silveira V. Insights from Proteomic Studies into Plant Somatic Embryogenesis. Proteomics 2018; 18:e1700265. [DOI: 10.1002/pmic.201700265] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Revised: 01/08/2018] [Indexed: 12/24/2022]
Affiliation(s)
- Angelo Schuabb Heringer
- Laboratório de Biotecnologia; Centro de Biociências e Biotecnologia; Universidade Estadual do Norte Fluminense Darcy Ribeiro; Rio de Janeiro Brazil
- Unidade de Biologia Integrativa; Setor de Genômica e Proteômica; Universidade Estadual do Norte Fluminense Darcy Ribeiro; Rio de Janeiro Brazil
| | - Claudete Santa-Catarina
- Laboratório de Biologia Celular e Tecidual; Centro de Biociências e Biotecnologia; Universidade Estadual do Norte Fluminense Darcy Ribeiro; Rio de Janeiro Brazil
| | - Vanildo Silveira
- Laboratório de Biotecnologia; Centro de Biociências e Biotecnologia; Universidade Estadual do Norte Fluminense Darcy Ribeiro; Rio de Janeiro Brazil
- Unidade de Biologia Integrativa; Setor de Genômica e Proteômica; Universidade Estadual do Norte Fluminense Darcy Ribeiro; Rio de Janeiro Brazil
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Bruderer R, Sondermann J, Tsou CC, Barrantes-Freer A, Stadelmann C, Nesvizhskii AI, Schmidt M, Reiter L, Gomez-Varela D. New targeted approaches for the quantification of data-independent acquisition mass spectrometry. Proteomics 2017; 17:10.1002/pmic.201700021. [PMID: 28319648 PMCID: PMC5870755 DOI: 10.1002/pmic.201700021] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Revised: 03/13/2017] [Accepted: 03/14/2017] [Indexed: 11/10/2022]
Abstract
The use of data-independent acquisition (DIA) approaches for the reproducible and precise quantification of complex protein samples has increased in the last years. The protein information arising from DIA analysis is stored in digital protein maps (DIA maps) that can be interrogated in a targeted way by using ad hoc or publically available peptide spectral libraries generated on the same sample species as for the generation of the DIA maps. The restricted availability of certain difficult-to-obtain human tissues (i.e., brain) together with the caveats of using spectral libraries generated under variable experimental conditions limits the potential of DIA. Therefore, DIA workflows would benefit from high-quality and extended spectral libraries that could be generated without the need of using valuable samples for library production. We describe here two new targeted approaches, using either classical data-dependent acquisition repositories (not specifically built for DIA) or ad hoc mouse spectral libraries, which enable the profiling of human brain DIA data set. The comparison of our results to both the most extended publically available human spectral library and to a state-of-the-art untargeted method supports the use of these new strategies to improve future DIA profiling efforts.
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Affiliation(s)
| | - Julia Sondermann
- Somatosensory Signaling and Systems Biology Research Group, Max Planck Institute of Experimental Medicine, Goettingen, Germany
| | - Chih-Chiang Tsou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Manuela Schmidt
- Somatosensory Signaling and Systems Biology Research Group, Max Planck Institute of Experimental Medicine, Goettingen, Germany
| | | | - David Gomez-Varela
- Somatosensory Signaling and Systems Biology Research Group, Max Planck Institute of Experimental Medicine, Goettingen, Germany
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