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Fields L, Ma M, DeLaney K, Phetsanthad A, Li L. A crustacean neuropeptide spectral library for data-independent acquisition (DIA) mass spectrometry applications. Proteomics 2024; 24:e2300285. [PMID: 38171828 PMCID: PMC11219527 DOI: 10.1002/pmic.202300285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 11/06/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024]
Abstract
Neuropeptides have tremendous potential for application in modern medicine, including utility as biomarkers and therapeutics. To overcome the inherent challenges associated with neuropeptide identification and characterization, data-independent acquisition (DIA) is a fitting mass spectrometry (MS) method of choice to achieve sensitive and accurate analysis. It is advantageous for preliminary neuropeptidomic studies to occur in less complex organisms, with crustacean models serving as a popular choice due to their relatively simple nervous system. With spectral libraries serving as a means to interpret DIA-MS output spectra, and Cancer borealis as a model of choice for neuropeptide analysis, we performed the first spectral library mapping of crustacean neuropeptides. Leveraging pre-existing data-dependent acquisition (DDA) spectra, a spectral library was built using PEAKS Online. The library is comprised of 333 unique neuropeptides. The identification results obtained through the use of this spectral library were compared with those achieved through library-free analysis of crustacean brain, pericardial organs (PO), and thoracic ganglia (TG) tissues. A statistically significant increase (Student's t-test, P value < 0.05) in the number of identifications achieved from the TG data was observed in the spectral library results. Furthermore, in each of the tissues, a distinctly different set of identifications was found in the library search compared to the library-free search. This work highlights the necessity for the use of spectral libraries in neuropeptide analysis, illustrating the advantage of spectral libraries for interpreting DIA spectra in a reproducible manner with greater neuropeptidomic depth.
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Affiliation(s)
- Lauren Fields
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, United States
| | - Min Ma
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, United States
| | - Kellen DeLaney
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, United States
| | - Ashley Phetsanthad
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, United States
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, United States
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, United States
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2
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Li K, Teo GC, Yang KL, Yu F, Nesvizhskii AI. diaTracer enables spectrum-centric analysis of diaPASEF proteomics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.25.595875. [PMID: 38854051 PMCID: PMC11160675 DOI: 10.1101/2024.05.25.595875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from cerebrospinal fluid (CSF) and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass offset searches.
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Affiliation(s)
- Kai Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Guo Ci Teo
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Kevin L. Yang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fengchao Yu
- 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
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3
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Rosenberger G, Li W, Turunen M, He J, Subramaniam PS, Pampou S, Griffin AT, Karan C, Kerwin P, Murray D, Honig B, Liu Y, Califano A. Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis. Nat Commun 2024; 15:3909. [PMID: 38724493 PMCID: PMC11082183 DOI: 10.1038/s41467-024-47957-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.
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Affiliation(s)
- George Rosenberger
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Mikko Turunen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jing He
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Prem S Subramaniam
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sergey Pampou
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron T Griffin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Medical Scientist Training Program, Columbia University Irving Medical Center, New York, NY, USA
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Patrick Kerwin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA.
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA.
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA.
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
- Chan Zuckerberg Biohub New York, New York, NY, USA.
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4
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Gunter HM, Youlten SE, Reis ALM, McCubbin T, Madala BS, Wong T, Stevanovski I, Cipponi A, Deveson IW, Santini NS, Kummerfeld S, Croucher PI, Marcellin E, Mercer TR. A universal molecular control for DNA, mRNA and protein expression. Nat Commun 2024; 15:2480. [PMID: 38509097 PMCID: PMC10954659 DOI: 10.1038/s41467-024-46456-9] [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: 09/12/2022] [Accepted: 02/28/2024] [Indexed: 03/22/2024] Open
Abstract
The expression of genes encompasses their transcription into mRNA followed by translation into protein. In recent years, next-generation sequencing and mass spectrometry methods have profiled DNA, RNA and protein abundance in cells. However, there are currently no reference standards that are compatible across these genomic, transcriptomic and proteomic methods, and provide an integrated measure of gene expression. Here, we use synthetic biology principles to engineer a multi-omics control, termed pREF, that can act as a universal molecular standard for next-generation sequencing and mass spectrometry methods. The pREF sequence encodes 21 synthetic genes that can be in vitro transcribed into spike-in mRNA controls, and in vitro translated to generate matched protein controls. The synthetic genes provide qualitative controls that can measure sensitivity and quantitative accuracy of DNA, RNA and peptide detection. We demonstrate the use of pREF in metagenome DNA sequencing and RNA sequencing experiments and evaluate the quantification of proteins using mass spectrometry. Unlike previous spike-in controls, pREF can be independently propagated and the synthetic mRNA and protein controls can be sustainably prepared by recipient laboratories using common molecular biology techniques. Together, this provides a universal synthetic standard able to integrate genomic, transcriptomic and proteomic methods.
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Affiliation(s)
- Helen M Gunter
- Australian Institute of Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Queensland, Australia
- BASE mRNA Facility, The University of Queensland, Brisbane, Queensland, Australia
- ARC Centre of Excellence in Synthetic Biology, The University of Queensland, Brisbane, Queensland, Australia
| | - Scott E Youlten
- Department of Genetics, Yale University School of Medicine, New Haven, CT, 06510, USA
- Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Andre L M Reis
- Genomics and Inherited Disease Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Garvan Institute of Medical Research and Murdoch Children's Research Institute, Sydney, New South Wales, Australia
- School of Electrical and Information Engineering, University of Sydney, Sydney, New South Wales, Australia
| | - Tim McCubbin
- Australian Institute of Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Queensland, Australia
- ARC Centre of Excellence in Synthetic Biology, The University of Queensland, Brisbane, Queensland, Australia
| | - Bindu Swapna Madala
- Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Garvan Institute of Medical Research and Murdoch Children's Research Institute, Sydney, New South Wales, Australia
| | - Ted Wong
- Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | - Igor Stevanovski
- Genomics and Inherited Disease Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Garvan Institute of Medical Research and Murdoch Children's Research Institute, Sydney, New South Wales, Australia
| | - Arcadi Cipponi
- Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Ira W Deveson
- Genomics and Inherited Disease Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Garvan Institute of Medical Research and Murdoch Children's Research Institute, Sydney, New South Wales, Australia
- School of Electrical and Information Engineering, University of Sydney, Sydney, New South Wales, Australia
| | - Nadia S Santini
- Centro Nacional de Investigación Disciplinaria en Conservación y Mejoramiento de Ecosistemas Forestales, INIFAP, Ciudad de México, 04010, Mexico
| | - Sarah Kummerfeld
- Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Peter I Croucher
- Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Esteban Marcellin
- Australian Institute of Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Queensland, Australia
- ARC Centre of Excellence in Synthetic Biology, The University of Queensland, Brisbane, Queensland, Australia
| | - Tim R Mercer
- Australian Institute of Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Queensland, Australia.
- BASE mRNA Facility, The University of Queensland, Brisbane, Queensland, Australia.
- ARC Centre of Excellence in Synthetic Biology, The University of Queensland, Brisbane, Queensland, Australia.
- Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
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5
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Langan LM, Lovin LM, Taylor RB, Scarlett KR, Kevin Chambliss C, Chatterjee S, Scott JT, Brooks BW. Proteome changes in larval zebrafish (Danio rerio) and fathead minnow (Pimephales promelas) exposed to (±) anatoxin-a. ENVIRONMENT INTERNATIONAL 2024; 185:108514. [PMID: 38394915 DOI: 10.1016/j.envint.2024.108514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/16/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024]
Abstract
Anatoxin-a and its analogues are potent neurotoxins produced by several genera of cyanobacteria. Due in part to its high toxicity and potential presence in drinking water, these toxins pose threats to public health, companion animals and the environment. It primarily exerts toxicity as a cholinergic agonist, with high affinity at neuromuscular junctions, but molecular mechanisms by which it elicits toxicological responses are not fully understood. To advance understanding of this cyanobacteria, proteomic characterization (DIA shotgun proteomics) of two common fish models (zebrafish and fathead minnow) was performed following (±) anatoxin-a exposure. Specifically, proteome changes were identified and quantified in larval fish exposed for 96 h (0.01-3 mg/L (±) anatoxin-a and caffeine (a methodological positive control) with environmentally relevant treatment levels examined based on environmental exposure distributions of surface water data. Proteomic concentration - response relationships revealed 48 and 29 proteins with concentration - response relationships curves for zebrafish and fathead minnow, respectively. In contrast, the highest number of differentially expressed proteins (DEPs) varied between zebrafish (n = 145) and fathead minnow (n = 300), with only fatheads displaying DEPs at all treatment levels. For both species, genes associated with reproduction were significantly downregulated, with pathways analysis that broadly clustered genes into groups associated with DNA repair mechanisms. Importantly, significant differences in proteome response between the species was also observed, consistent with prior observations of differences in response using both behavioral assays and gene expression, adding further support to model specific differences in organismal sensitivity and/or response. When DEPs were read across from humans to zebrafish, disease ontology enrichment identified diseases associated with cognition and muscle weakness consistent with the prior literature. Our observations highlight limited knowledge of how (±) anatoxin-a, a commonly used synthetic racemate surrogate, elicits responses at a molecular level and advances its toxicological understanding.
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Affiliation(s)
- Laura M Langan
- Department of Environmental Science, Baylor University, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, TX 76798, USA; Department of Environmental Health Sciences, University of South Carolina, Columbia, SC 29208, USA.
| | - Lea M Lovin
- Department of Environmental Science, Baylor University, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, TX 76798, USA; Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden
| | - Raegyn B Taylor
- Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, TX 76798, USA; Department of Chemistry, Baylor University, Waco, TX 76798, USA
| | - Kendall R Scarlett
- Department of Environmental Science, Baylor University, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, TX 76798, USA
| | - C Kevin Chambliss
- Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, TX 76798, USA; Department of Chemistry, Baylor University, Waco, TX 76798, USA
| | - Saurabh Chatterjee
- Department of Medicine, Department of Environmental and Occupational Health, University of California Irvine, Irvine, CA 92617, USA
| | - J Thad Scott
- Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, TX 76798, USA; Department of Biology, Baylor University, Waco, TX 76798, USA
| | - Bryan W Brooks
- Department of Environmental Science, Baylor University, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, TX 76798, USA.
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6
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Lou R, Shui W. Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023. Mol Cell Proteomics 2024; 23:100712. [PMID: 38182042 PMCID: PMC10847697 DOI: 10.1016/j.mcpro.2024.100712] [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: 10/31/2023] [Revised: 12/27/2023] [Accepted: 01/02/2024] [Indexed: 01/07/2024] Open
Abstract
Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.
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Affiliation(s)
- Ronghui Lou
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
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7
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Searfoss RM, Karki R, Lin Z, Robison F, Garcia BA. An Optimized and High-Throughput Method for Histone Propionylation and Data-Independent Acquisition Analysis for the Identification and Quantification of Histone Post-translational Modifications. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:2508-2517. [PMID: 37853520 PMCID: PMC11017827 DOI: 10.1021/jasms.3c00223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
Histones are DNA binding proteins that allow for packaging of the DNA into the nucleus. They are abundantly present across the genome and thus serve as a major site of epigenetic regulation through the use of post-translational modifications (PTMs). Aberrations in histone expression and modifications have been implicated in a variety of human diseases and thus are a major focus of disease etiology studies. A well-established method for studying histones and PTMs is through the chemical derivatization of isolated histones followed by liquid chromatography and mass spectrometry analysis. Using such an approach has allowed for a swath of discoveries to be found, leading to novel therapeutics such as histone deacetylase (HDAC) inhibitors that have already been applied in the clinic. However, with the rapid improvement in instrumentation and data analysis pipelines, it remains important to temporally re-evaluate the established protocols to improve throughput and ensure data quality. Here, we optimized the histone derivatization procedure to increase sample throughput without compromising peptide quantification. An implemented spike-in standard peptide further serves as a quality control to evaluate the propionylation and digestion efficiencies as well as reproducibility in chromatographic retention and separation. Last, the application of various data-independent acquisition (DIA) strategies was explored to ensure low variation between runs. The output of this study is a newly optimized derivatization protocol and mass spectrometry method that maintains high identification and quantification of histone PTMs while increasing sample throughput.
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Affiliation(s)
- Richard M. Searfoss
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO 63110
- Contributed equally to this work
| | - Rashmi Karki
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO 63110
- Contributed equally to this work
| | - Zongtao Lin
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO 63110
| | - Faith Robison
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO 63110
| | - Benjamin A. Garcia
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO 63110
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8
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Mikacenic C, Fussner LA, Bell J, Burnham EL, Chlan LL, Cook SK, Dickson RP, Almonor F, Luo F, Madan K, Morales-Nebreda L, Mould KJ, Simpson AJ, Singer BD, Stapleton RD, Wendt CH, Files DC. Research Bronchoscopies in Critically Ill Research Participants: An Official American Thoracic Society Workshop Report. Ann Am Thorac Soc 2023; 20:621-631. [PMID: 37125997 PMCID: PMC10174130 DOI: 10.1513/annalsats.202302-106st] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023] Open
Abstract
Bronchoscopy for research purposes is a valuable tool to understand lung-specific biology in human participants. Despite published reports and active research protocols using this procedure in critically ill patients, no recent document encapsulates the important safety considerations and downstream applications of this procedure in this setting. The objectives were to identify safe practices for patient selection and protection of hospital staff, provide recommendations for sample procurement to standardize studies, and give guidance on sample preparation for novel research technologies. Seventeen international experts in the management of critically ill patients, bronchoscopy in clinical and research settings, and experience in patient-oriented clinical or translational research convened for a workshop. Review of relevant literature, expert presentations, and discussion generated the findings presented herein. The committee concludes that research bronchoscopy with bronchoalveolar lavage in critically ill patients on mechanical ventilation is valuable and safe in appropriately selected patients. This report includes recommendations on standardization of this procedure and prioritizes the reporting of sample management to produce more reproducible results between laboratories. This document serves as a resource to the community of researchers who endeavor to include bronchoscopy as part of their research protocols and highlights key considerations for the inclusion and safety of research participants.
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9
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Li X, Huang Y, Zheng K, Yu G, Wang Q, Gu L, Li J, Wang H, Zhang W, Sun Y, Li C. Integrated proteomic and phosphoproteomic data-independent acquisition data evaluate the personalized drug responses of primary and metastatic tumors in colorectal cancer. BIOPHYSICS REPORTS 2023; 9:67-81. [PMID: 37753059 PMCID: PMC10518519 DOI: 10.52601/bpr.2022.210048] [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: 12/31/2021] [Accepted: 11/18/2022] [Indexed: 02/19/2023] Open
Abstract
Mass spectrometry (MS)-based proteomics and phosphoproteomics are powerful methods to study the biological mechanisms, diagnostic biomarkers, prognostic analysis, and drug therapy of tumors. Data-independent acquisition (DIA) mode is considered to perform better than data-dependent acquisition (DDA) mode in terms of quantitative reproducibility, specificity, accuracy, and identification of low-abundance proteins. Mini patient derived xenograft (MiniPDX) model is an effective model to assess the response to antineoplastic drugs in vivo and is helpful for the precise treatment of cancer patients. Kinases are favorable spots for tumor-targeted drugs, and their functional completion relies on signaling pathways through phosphorylating downstream substrates. Kinase-phosphorylation networks or edge interactions are considered more credible and permanent for characterizing complex diseases. Here, we provide a workflow for personalized drug response assessment in primary and metastatic colorectal cancer (CRC) tumors using DIA proteomic data, DIA phosphoproteomic data, and MiniPDX models. Three kinase inhibitors, afatinib, gefitinib, and regorafenib, are tested pharmacologically. The process mainly includes the following steps: clinical tissue collection, sample preparation, hybrid spectral libraries establishment, MS data acquisition, kinase-substrate network construction, in vivo drug test, and elastic regression modeling. Our protocol gives a more direct data basis for individual drug responses, and will improve the selection of treatment strategies for patients without the druggable mutation.
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Affiliation(s)
- Xumiao Li
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yiming Huang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Kuo Zheng
- Colorectal Surgery Department, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Guanyu Yu
- Colorectal Surgery Department, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Qinqin Wang
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Lei Gu
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jingquan Li
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hui Wang
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Wei Zhang
- Colorectal Surgery Department, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Yidi Sun
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chen Li
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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10
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Phetsanthad A, Carr AV, Fields L, Li L. Definitive Screening Designs to Optimize Library-Free DIA-MS Identification and Quantification of Neuropeptides. J Proteome Res 2023; 22:1510-1519. [PMID: 36921255 DOI: 10.1021/acs.jproteome.3c00088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
Abstract
Method optimization is crucial for successful mass spectrometry (MS) analysis. However, extensive method assessments, altering various parameters individually, are rarely performed due to practical limitations regarding time and sample quantity. To maximize sample space for optimization while maintaining reasonable instrumentation requirements, a definitive screening design (DSD) is leveraged for systematic optimization of data-independent acquisition (DIA) parameters to maximize crustacean neuropeptide identifications. While DSDs require several injections, a library-free methodology enables surrogate sample usage for comprehensive optimization of MS parameters to assess biomolecules from limited samples. We identified several parameters contributing significant first- or second-order effects to method performance, and the DSD model predicted ideal values to implement. These increased reproducibility and detection capabilities enabled the identification of 461 peptides, compared to 375 and 262 peptides identified through data-dependent acquisition (DDA) and a published DIA method for crustacean neuropeptides, respectively. Herein, we demonstrate a DSD optimization workflow, using standard material, not reliant on spectral libraries for the analysis of any low abundance molecules from previous samples of limited availability. This extends the DIA method to low abundance isoforms dysregulated or only detectable in disease samples, thus improving characterization of previously inaccessible biomolecules, such as neuropeptides. Data are available via ProteomeXchange with identifier PXD038520.
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Affiliation(s)
- Ashley Phetsanthad
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Austin V Carr
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Lauren Fields
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.,School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin 53705, United States.,Lachman Institute for Pharmaceutical Development, School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin 53705, United States
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11
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Vreeke GJC, Meijers MGJ, Vincken JP, Wierenga PA. Towards absolute quantification of protein genetic variants in Pisum sativum extracts. Anal Biochem 2023; 665:115048. [PMID: 36657509 DOI: 10.1016/j.ab.2023.115048] [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: 10/04/2022] [Revised: 12/14/2022] [Accepted: 01/09/2023] [Indexed: 01/19/2023]
Abstract
In recent years, several studies have used proteomics approaches to characterize genetic variant profiles of agricultural raw materials. In such studies, the challenge is the quantification of the individual protein variants. In this study a novel UPLC-PDA-MS method with absolute and label-free UV-based peptide quantification was applied to quantify the genetic variants of legumin, vicilin and albumins in pea extracts. The aim was to investigate the applicability of this method and to identify challenges in determining protein concentration from the measured peptide concentrations. Analysis of the protein mass balance showed significant losses of proteins in extraction (37%) and of peptides in further sample preparation (69%). The challenge in calculating the extractable individual protein concentrations was how to deal with these insoluble peptides. The quantification approach using average amino acid concentrations in each position of the sequence showed most reproducible results and allowed comparison of the genetic protein composition of 8 different cultivars. The extractable protein composition (μM/μM) was remarkably similar for all cultivar extracts and consisted of legumins A1 (12.8 ± 1.2%), A2 (1.1 ± 0.4%), B (9.9 ± 1.6%), J (7.5 ± 1.0%) and K (10.3 ± 2.1%), vicilin (15.2 ± 1.7%), provicilin (15.7 ± 2.5%), convicilin (9.8 ± 0.8%), albumin A1 (7.4 ± 2.0%), albumin 2 (10.0 ± 1.5%) and protease inhibitor (0.4 ± 0.4%).
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Affiliation(s)
- Gijs J C Vreeke
- Laboratory of Food Chemistry, Wageningen University and Research, P.O. Box 17, 6700 AA, Wageningen, the Netherlands
| | - Maud G J Meijers
- Laboratory of Food Chemistry, Wageningen University and Research, P.O. Box 17, 6700 AA, Wageningen, the Netherlands; TiFN, P.O. Box 557, 6700 AN, Wageningen, the Netherlands
| | - Jean-Paul Vincken
- Laboratory of Food Chemistry, Wageningen University and Research, P.O. Box 17, 6700 AA, Wageningen, the Netherlands
| | - Peter A Wierenga
- Laboratory of Food Chemistry, Wageningen University and Research, P.O. Box 17, 6700 AA, Wageningen, the Netherlands.
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12
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Rosenberger G, Li W, Turunen M, He J, Subramaniam PS, Pampou S, Griffin AT, Karan C, Kerwin P, Murray D, Honig B, Liu Y, Califano A. Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.15.528736. [PMID: 36824919 PMCID: PMC9949144 DOI: 10.1101/2023.02.15.528736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.
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Affiliation(s)
- George Rosenberger
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Mikko Turunen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jing He
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Present address: Regeneron Genetics Center, Tarrytown, NY, USA
| | - Prem S Subramaniam
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sergey Pampou
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron T Griffin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Medical Scientist Training Program, Columbia University Irving Medical Center, New York, NY, USA
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Patrick Kerwin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
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13
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Wang JH, Choong WK, Chen CT, Sung TY. Calibr improves spectral library search for spectrum-centric analysis of data independent acquisition proteomics. Sci Rep 2022; 12:2045. [PMID: 35132134 PMCID: PMC8821666 DOI: 10.1038/s41598-022-06026-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 01/21/2022] [Indexed: 12/20/2022] Open
Abstract
Identifying peptides and proteins from mass spectrometry (MS) data, spectral library searching has emerged as a complementary approach to the conventional database searching. However, for the spectrum-centric analysis of data-independent acquisition (DIA) data, spectral library searching has not been widely exploited because existing spectral library search tools are mainly designed and optimized for the analysis of data-dependent acquisition (DDA) data. We present Calibr, a spectral library search tool for spectrum-centric DIA data analysis. Calibr optimizes spectrum preprocessing for pseudo MS2 spectra, generating an 8.11% increase in spectrum–spectrum match (SSM) number and a 7.49% increase in peptide number over the traditional preprocessing approach. When searching against the DDA-based spectral library, Calibr improves SSM number by 17.6–26.65% and peptide number by 18.45–37.31% over two state-of-the-art tools on three different data sets. Searching against the public spectral library from MassIVE, Calibr improves state-of-the-art tools in SSM and peptide numbers by more than 31.49% and 25.24%, respectively, for two data sets. Our analyses indicate higher sensitivity of Calibr results from the use of various spectral similarity measures and statistical scores, coupled with machine learning-based statistical validation for FDR control. Calibr executable files including a graphical user-interface application are available at https://ms.iis.sinica.edu.tw/COmics/Software_CalibrWizard.html and https://sourceforge.net/projects/comics-calibr.
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14
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Ma Q, Chai Y, Yang Z, Huang A. Deciphering the mechanisms of Limosilactobacillus fermentum L1 involved in conjugated linoleic acid regulated by luxS/AI-2 quorum sensing. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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15
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Data-Independent Acquisition Mass Spectrometry-Based Deep Proteome Analysis for Hydrophobic Proteins from Dried Blood Spots Enriched by Sodium Carbonate Precipitation. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2420:39-52. [PMID: 34905164 DOI: 10.1007/978-1-0716-1936-0_4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Dried blood spots (DBS) are widely used for screening molecular profiles, including enzymatic activity. However, hydrophilic proteins present in large amounts in blood inhibit detection of other proteins in DBS by liquid chromatography-mass spectrometry (LC-MS/MS) without preenrichment. Sodium carbonate precipitation (SCP) can concentrate hydrophobic proteins from DBS and effectively remove soluble hydrophilic proteins. Furthermore, SCP combination with data-independent acquisition (DIA) for quantitative LC-MS/MS enhanced the proteome analysis sensitivity and quantification limits. In this protocol, we have described in detail a simple preenrichment method using SCP and a deep proteome analysis method for LC-MS/MS data using DIA.
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16
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Sauer CS, Phetsanthad A, Riusech OL, Li L. Developing mass spectrometry for the quantitative analysis of neuropeptides. Expert Rev Proteomics 2021; 18:607-621. [PMID: 34375152 PMCID: PMC8522511 DOI: 10.1080/14789450.2021.1967146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 08/09/2021] [Indexed: 12/22/2022]
Abstract
INTRODUCTION Neuropeptides are signaling molecules originating in the neuroendocrine system that can act as neurotransmitters and hormones in many biochemical processes. Their exact function is difficult to characterize, however, due to dependence on concentration, post-translational modifications, and the presence of other comodulating neuropeptides. Mass spectrometry enables sensitive, accurate, and global peptidomic analyses that can profile neuropeptide expression changes to understand their roles in many biological problems, such as neurodegenerative disorders and metabolic function. AREAS COVERED We provide a brief overview of the fundamentals of neuropeptidomic research, limitations of existing methods, and recent progress in the field. This review is focused on developments in mass spectrometry and encompasses labeling strategies, post-translational modification analysis, mass spectrometry imaging, and integrated multi-omic workflows, with discussion emphasizing quantitative advancements. EXPERT OPINION Neuropeptidomics is critical for future clinical research with impacts in biomarker discovery, receptor identification, and drug design. While advancements are being made to improve sensitivity and accuracy, there is still room for improvement. Better quantitative strategies are required for clinical analyses, and these methods also need to be amenable to mass spectrometry imaging, post-translational modification analysis, and multi-omics to facilitate understanding and future treatment of many diseases.
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Affiliation(s)
- Christopher S. Sauer
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, USA
| | - Ashley Phetsanthad
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, USA
| | - Olga L. Riusech
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, USA
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, USA
- School of Pharmacy, University of Wisconsin-Madison, 777 Highland Avenue, Madison, WI 53075, USA
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17
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Bichmann L, Gupta S, Rosenberger G, Kuchenbecker L, Sachsenberg T, Ewels P, Alka O, Pfeuffer J, Kohlbacher O, Röst H. DIAproteomics: A Multifunctional Data Analysis Pipeline for Data-Independent Acquisition Proteomics and Peptidomics. J Proteome Res 2021; 20:3758-3766. [PMID: 34153189 DOI: 10.1021/acs.jproteome.1c00123] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. The main advantages include greater reproducibility and sensitivity and a greater dynamic range compared with data-dependent acquisition (DDA). However, the data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics, a multifunctional, automated, high-throughput pipeline implemented in the Nextflow workflow management system that allows one to easily process proteomics and peptidomics DIA data sets on diverse compute infrastructures. The central components are well-established tools such as the OpenSwathWorkflow for the DIA spectral library search and PyProphet for the false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and to carry out the retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from the statistical postprocessing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is well documented open-source software and is available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.
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Affiliation(s)
- Leon Bichmann
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany.,Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen 72076, Germany
| | - Shubham Gupta
- Donnelly Center for Biomolecular Research, University of Toronto, Toronto, Ontario ON M5S 3E1, Canada
| | - George Rosenberger
- Department of Systems Biology, Columbia University, New York, New York 10032, United States
| | - Leon Kuchenbecker
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany
| | - Timo Sachsenberg
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany
| | - Phil Ewels
- Science for Life Laboratory (SciLifeLab), Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Oliver Alka
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany
| | - Julianus Pfeuffer
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany.,Institute for Informatics, Freie Universität Berlin, Berlin 14195, Germany.,Zuse Institute Berlin, Berlin 14195, Germany
| | - Oliver Kohlbacher
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany.,Institute for Biological and Medical Informatics, University of Tübingen, Tübingen 72076, Germany.,Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen 72076, Germany
| | - Hannes Röst
- Donnelly Center for Biomolecular Research, University of Toronto, Toronto, Ontario ON M5S 3E1, Canada
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18
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Bludau I. Discovery-Versus Hypothesis-Driven Detection of Protein-Protein Interactions and Complexes. Int J Mol Sci 2021; 22:4450. [PMID: 33923221 PMCID: PMC8123138 DOI: 10.3390/ijms22094450] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 04/13/2021] [Accepted: 04/21/2021] [Indexed: 12/13/2022] Open
Abstract
Protein complexes are the main functional modules in the cell that coordinate and perform the vast majority of molecular functions. The main approaches to identify and quantify the interactome to date are based on mass spectrometry (MS). Here I summarize the benefits and limitations of different MS-based interactome screens, with a focus on untargeted interactome acquisition, such as co-fractionation MS. Specific emphasis is given to the discussion of discovery- versus hypothesis-driven data analysis concepts and their applicability to large, proteome-wide interactome screens. Hypothesis-driven analysis approaches, i.e., complex- or network-centric, are highlighted as promising strategies for comparative studies. While these approaches require prior information from public databases, also reviewed herein, the available wealth of interactomic data continuously increases, thereby providing more exhaustive information for future studies. Finally, guidance on the selection of interactome acquisition and analysis methods is provided to aid the reader in the design of protein-protein interaction studies.
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Affiliation(s)
- Isabell Bludau
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
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19
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Pak H, Michaux J, Huber F, Chong C, Stevenson BJ, Müller M, Coukos G, Bassani-Sternberg M. Sensitive Immunopeptidomics by Leveraging Available Large-Scale Multi-HLA Spectral Libraries, Data-Independent Acquisition, and MS/MS Prediction. Mol Cell Proteomics 2021; 20:100080. [PMID: 33845167 PMCID: PMC8724634 DOI: 10.1016/j.mcpro.2021.100080] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 03/18/2021] [Accepted: 04/05/2021] [Indexed: 12/15/2022] Open
Abstract
Mass spectrometry (MS) is the state-of-the-art methodology for capturing the breadth and depth of the immunopeptidome across human leukocyte antigen (HLA) allotypes and cell types. The majority of studies in the immunopeptidomics field are discovery driven. Hence, data-dependent tandem MS (MS/MS) acquisition (DDA) is widely used, as it generates high-quality references of peptide fingerprints. However, DDA suffers from the stochastic selection of abundant ions that impairs sensitivity and reproducibility. In contrast, in data-independent acquisition (DIA), the systematic fragmentation and acquisition of all fragment ions within given isolation m/z windows yield a comprehensive map for a given sample. However, many DIA approaches commonly require generating comprehensive DDA-based spectrum libraries, which can become impractical for studying noncanonical and personalized neoantigens. Because the amount of HLA peptides eluted from biological samples such as small tissue biopsies is typically not sufficient for acquiring both meaningful DDA data necessary for generating comprehensive spectral libraries and DIA MS measurements, the implementation of DIA in the immunopeptidomics translational research domain has remained limited. We implemented a DIA immunopeptidomics workflow and assessed its sensitivity and accuracy by matching DIA data against libraries with growing complexity-from sample-specific libraries to libraries combining 2 to 40 different immunopeptidomics samples. Analyzing DIA immunopeptidomics data against a complex multi-HLA spectral library resulted in a two-fold increase in peptide identification compared with sample-specific library and in a three-fold increase compared with DDA measurements, yet with no detrimental effect on the specificity. Furthermore, we demonstrated the implementation of DIA for sensitive personalized neoantigen discovery through the analysis of DIA data with predicted MS/MS spectra of clinically relevant HLA ligands. We conclude that a comprehensive multi-HLA library for DIA approach in combination with MS/MS prediction is highly advantageous for clinical immunopeptidomics, especially when low amounts of biological samples are available.
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Affiliation(s)
- HuiSong Pak
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland
| | - Justine Michaux
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland
| | - Florian Huber
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland
| | - Chloe Chong
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland
| | | | - Markus Müller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - George Coukos
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland.
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20
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Yu F, Haynes SE, Nesvizhskii AI. IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs. Mol Cell Proteomics 2021; 20:100077. [PMID: 33813065 PMCID: PMC8131922 DOI: 10.1016/j.mcpro.2021.100077] [Citation(s) in RCA: 146] [Impact Index Per Article: 48.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 03/23/2021] [Indexed: 02/06/2023] Open
Abstract
Missing values weaken the power of label-free quantitative proteomic experiments to uncover true quantitative differences between biological samples or experimental conditions. Match-between-runs (MBR) has become a common approach to mitigate the missing value problem, where peptides identified by tandem mass spectra in one run are transferred to another by inference based on m/z, charge state, retention time, and ion mobility when applicable. Though tolerances are used to ensure such transferred identifications are reasonably located and meet certain quality thresholds, little work has been done to evaluate the statistical confidence of MBR. Here, we present a mixture model-based approach to estimate the false discovery rate (FDR) of peptide and protein identification transfer, which we implement in the label-free quantification tool IonQuant. Using several benchmarking datasets generated on both Orbitrap and timsTOF mass spectrometers, we demonstrate superior performance of IonQuant with FDR-controlled MBR compared with MaxQuant (19-38 times faster; 6-18% more proteins quantified and with comparable or better accuracy). We further illustrate the performance of IonQuant and highlight the need for FDR-controlled MBR, in two single-cell proteomics experiments, including one acquired with the help of high-field asymmetric ion mobility spectrometry separation. Fully integrated in the FragPipe computational environment, IonQuant with FDR-controlled MBR enables fast and accurate peptide and protein quantification in label-free proteomics experiments.
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Affiliation(s)
- Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Sarah E Haynes
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
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21
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Weng S, Wang M, Zhao Y, Ying W, Qian X. Optimised data-independent acquisition strategy recaptures the classification of early-stage hepatocellular carcinoma based on data-dependent acquisition. J Proteomics 2021; 238:104152. [PMID: 33609755 DOI: 10.1016/j.jprot.2021.104152] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/21/2021] [Accepted: 02/08/2021] [Indexed: 12/27/2022]
Abstract
Proteomics is increasingly used for exploring disease biomarkers and therapeutic targets. The data-independent acquisition (DIA) method collects all peptide signals in a sample, and provides a convenient way to archive disease-related molecular features for further exploration. In this study, we first established a high-coverage human hepatocellular carcinoma (HCC) spectral library containing 9393 protein groups, 119,903 peptides. Furthermore, we optimised the DIA method with respect to four key parameters: settings for mass spectrometry acquisition, gradient length, amount of sample loading, and length of analytical column. More than 6000 proteins from HepG2 cells could be stably quantified using the optimised one-shot DIA approach with a 2 h gradient time. One-shot DIA identified a similar number of proteins as did multi-fraction data-dependent acquisition (DDA) from the same group of HCC samples, but at a quarter of the total acquisition time. DIA data could recapture the classification results obtained from DDA data, thus paving the way for large-scale, multi-centre proteomics analysis of clinical samples. SIGNIFICANCE: The organ-specific spectral library for HCC and the optimised 2 h DIA approach met the urgent demands for large-scale quantitative proteomics analysis of HCC clinical samples. Compared with multi-fraction-DDA, the optimised one-shot DIA could reach a similar identification while consuming shorter acquisition time, thus making it possible to analyse thousands of clinical samples.
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Affiliation(s)
- Shuang Weng
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Mingchao Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Yingyi Zhao
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Wantao Ying
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.
| | - Xiaohong Qian
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.
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22
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Xu Y, Song X, Wang D, Wang Y, Li P, Li J. Proteomic insights into synaptic signaling in the brain: the past, present and future. Mol Brain 2021; 14:37. [PMID: 33596935 PMCID: PMC7888154 DOI: 10.1186/s13041-021-00750-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 02/09/2021] [Indexed: 12/29/2022] Open
Abstract
Chemical synapses in the brain connect neurons to form neural circuits, providing the structural and functional bases for neural communication. Disrupted synaptic signaling is closely related to a variety of neurological and psychiatric disorders. In the past two decades, proteomics has blossomed as a versatile tool in biological and biomedical research, rendering a wealth of information toward decoding the molecular machinery of life. There is enormous interest in employing proteomic approaches for the study of synapses, and substantial progress has been made. Here, we review the findings of proteomic studies of chemical synapses in the brain, with special attention paid to the key players in synaptic signaling, i.e., the synaptic protein complexes and their post-translational modifications. Looking toward the future, we discuss the technological advances in proteomics such as data-independent acquisition mass spectrometry (DIA-MS), cross-linking in combination with mass spectrometry (CXMS), and proximity proteomics, along with their potential to untangle the mystery of how the brain functions at the molecular level. Last but not least, we introduce the newly developed synaptomic methods. These methods and their successful applications marked the beginnings of the synaptomics era.
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Affiliation(s)
- Yalan Xu
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, Medical College, Qingdao University, Qingdao, 266021, China
| | - Xiuyue Song
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, Medical College, Qingdao University, Qingdao, 266021, China
| | - Dong Wang
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, Medical College, Qingdao University, Qingdao, 266021, China
| | - Yin Wang
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, Medical College, Qingdao University, Qingdao, 266021, China
| | - Peifeng Li
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, Medical College, Qingdao University, Qingdao, 266021, China
| | - Jing Li
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, Medical College, Qingdao University, Qingdao, 266021, China.
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23
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Fu J, Luo Y, Mou M, Zhang H, Tang J, Wang Y, Zhu F. Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection. Curr Drug Targets 2021; 21:34-54. [PMID: 31433754 DOI: 10.2174/1389450120666190821160207] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 07/17/2019] [Accepted: 07/24/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets. OBJECTIVE The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics. METHODS Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics. RESULTS In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed. CONCLUSION In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.
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Affiliation(s)
- Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
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24
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Mun DG, Renuse S, Saraswat M, Madugundu A, Udainiya S, Kim H, Park SKR, Zhao H, Nirujogi RS, Na CH, Kannan N, Yates JR, Lee SW, Pandey A. PASS-DIA: A Data-Independent Acquisition Approach for Discovery Studies. Anal Chem 2020; 92:14466-14475. [PMID: 33079518 DOI: 10.1021/acs.analchem.0c02513] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
A data-independent acquisition (DIA) approach is being increasingly adopted as a promising strategy for identification and quantitation of proteomes. As most DIA data sets are acquired with wide isolation windows, highly complex MS/MS spectra are generated, which negatively impacts obtaining peptide information through classical protein database searches. Therefore, the analysis of DIA data mainly relies on the evidence of the existence of peptides from prebuilt spectral libraries. Consequently, one major weakness of this method is that it does not account for peptides that are not included in the spectral library, precluding the use of DIA for discovery studies. Here, we present a strategy termed Precursor ion And Small Slice-DIA (PASS-DIA) in which MS/MS spectra are acquired with small isolation windows (slices) and MS/MS spectra are interpreted with accurately determined precursor ion masses. This method enables the direct application of conventional spectrum-centric analysis pipelines for peptide identification and precursor ion-based quantitation. The performance of PASS-DIA was observed to be superior to both data-dependent acquisition (DDA) and conventional DIA experiments with 69 and 48% additional protein identifications, respectively. Application of PASS-DIA for the analysis of post-translationally modified peptides again highlighted its superior performance in characterizing phosphopeptides (77% more), N-terminal acetylated peptides (56% more), and N-glycopeptides (83% more) as compared to DDA alone. Finally, the use of PASS-DIA to characterize a rare proteome of human fallopian tube organoids enabled 34% additional protein identifications than DDA alone and revealed biologically relevant pathways including low abundance proteins. Overall, PASS-DIA is a novel DIA approach for use as a discovery tool that outperforms both conventional DDA and DIA experiments to provide additional protein information. We believe that the PASS-DIA method is an important strategy for discovery-type studies when deeper proteome characterization is required.
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Affiliation(s)
- Dong-Gi Mun
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Santosh Renuse
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Mayank Saraswat
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Molecular Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India.,Institute of Bioinformatics, International Technology Park, Bangalore 560066, Karnataka, India.,Manipal Academy of Higher Education (MAHE), Manipal 576104 Karnataka, India
| | - Anil Madugundu
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Molecular Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India.,Institute of Bioinformatics, International Technology Park, Bangalore 560066, Karnataka, India.,Manipal Academy of Higher Education (MAHE), Manipal 576104 Karnataka, India
| | - Savita Udainiya
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Molecular Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India
| | - Hokeun Kim
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Sung-Kyu Robin Park
- Department of Molecular Medicine and Neurobiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Hui Zhao
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Raja Sekhar Nirujogi
- Medical Research Council Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, Dundee DD1 5EH, United Kingdom
| | - Chan Hyun Na
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States.,Neurology, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Nagarajan Kannan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - John R Yates
- Department of Molecular Medicine and Neurobiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Sang-Won Lee
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Molecular Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India.,Institute of Bioinformatics, International Technology Park, Bangalore 560066, Karnataka, India.,Manipal Academy of Higher Education (MAHE), Manipal 576104 Karnataka, India
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25
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Fernández-Costa C, Martínez-Bartolomé S, McClatchy DB, Saviola AJ, Yu NK, Yates JR. Impact of the Identification Strategy on the Reproducibility of the DDA and DIA Results. J Proteome Res 2020; 19:3153-3161. [PMID: 32510229 PMCID: PMC7898222 DOI: 10.1021/acs.jproteome.0c00153] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Data-independent acquisition (DIA) is a promising technique for the proteomic analysis of complex protein samples. A number of studies have claimed that DIA experiments are more reproducible than data-dependent acquisition (DDA), but these claims are unsubstantiated since different data analysis methods are used in the two methods. Data analysis in most DIA workflows depends on spectral library searches, whereas DDA typically employs sequence database searches. In this study, we examined the reproducibility of the DIA and DDA results using both sequence database and spectral library search. The comparison was first performed using a cell lysate and then extended to an interactome study. Protein overlap among the technical replicates in both DDA and DIA experiments was 30% higher with library-based identifications than with sequence database identifications. The reproducibility of quantification was also improved with library search compared to database search, with the mean of the coefficient of variation decreasing more than 30% and a reduction in the number of missing values of more than 35%. Our results show that regardless of the acquisition method, higher identification and quantification reproducibility is observed when library search was used.
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Affiliation(s)
- Carolina Fernández-Costa
- Departments of Molecular Medicine & Neurobiology, The Scripps Research Institute, La Jolla, CA, USA
| | | | - Daniel B. McClatchy
- Departments of Molecular Medicine & Neurobiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Anthony J. Saviola
- Departments of Molecular Medicine & Neurobiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nam-Kyung Yu
- Departments of Molecular Medicine & Neurobiology, The Scripps Research Institute, La Jolla, CA, USA
| | - John R. Yates
- Departments of Molecular Medicine & Neurobiology, The Scripps Research Institute, La Jolla, CA, USA
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26
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Nakajima D, Kawashima Y, Shibata H, Yasumi T, Isa M, Izawa K, Nishikomori R, Heike T, Ohara O. Simple and Sensitive Analysis for Dried Blood Spot Proteins by Sodium Carbonate Precipitation for Clinical Proteomics. J Proteome Res 2020; 19:2821-2827. [PMID: 32343581 DOI: 10.1021/acs.jproteome.0c00271] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Dried blood spots (DBS) are widely used for screening biomolecular profiles, including enzymatic activities. However, detection of minor proteins in DBS by liquid chromatography-mass spectrometry (LC-MS/MS) without pre-enrichment remains challenging because of the coexistence of large quantities of hydrophilic proteins. In this study, we address this problem by developing a simple method using sodium carbonate precipitation (SCP). SCP enriches hydrophobic proteins from DBS, allowing substantial removal of soluble proteins. In combination with SCP, we used quantitative LC-MS/MS proteome analysis in a data-independent acquisition mode (DIA) to enhance the sensitivity and quantification limits of proteome analysis. As a result, identification of 1977 proteins in DBS is possible, including 585 disease-related proteins listed in the Online Mendelian Inheritance in Man.
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Affiliation(s)
| | | | - Hirofumi Shibata
- Department of Pediatrics, Kyoto University Graduate School of Medicine, Kyoto 606-8501, Japan
| | - Takahiro Yasumi
- Department of Pediatrics, Kyoto University Graduate School of Medicine, Kyoto 606-8501, Japan
| | - Masahiko Isa
- Department of Pediatrics, Kyoto University Graduate School of Medicine, Kyoto 606-8501, Japan
| | - Kazushi Izawa
- Department of Pediatrics, Kyoto University Graduate School of Medicine, Kyoto 606-8501, Japan
| | - Ryuta Nishikomori
- Department of Pediatrics and Child Health, Kurume University School of Medicine, Kurume, Fukuoka 830-0111, Japan
| | - Toshio Heike
- Department of Pediatrics, Kyoto University Graduate School of Medicine, Kyoto 606-8501, Japan.,Hyogo Prefectural Amagasaki General Medical Center, Hyogo 660-8550, Japan
| | - Osamu Ohara
- Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan
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27
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Butera G, Brandi J, Cavallini C, Scarpa A, Lawlor RT, Scupoli MT, Marengo E, Cecconi D, Manfredi M, Donadelli M. The Mutant p53-Driven Secretome Has Oncogenic Functions in Pancreatic Ductal Adenocarcinoma Cells. Biomolecules 2020; 10:biom10060884. [PMID: 32526853 PMCID: PMC7356389 DOI: 10.3390/biom10060884] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 05/30/2020] [Accepted: 06/05/2020] [Indexed: 02/06/2023] Open
Abstract
The cancer secretome is a rich repository of useful information for both cancer biology and clinical oncology. A better understanding of cancer secretome is particularly relevant for pancreatic ductal adenocarcinoma (PDAC), whose extremely high mortality rate is mainly due to early metastasis, resistance to conventional treatments, lack of recognizable symptoms, and assays for early detection. TP53 gene is a master transcriptional regulator controlling several key cellular pathways and it is mutated in ~75% of PDACs. We report the functional effect of the hot-spot p53 mutant isoforms R175H and R273H on cancer cell secretome, showing their influence on proliferation, chemoresistance, apoptosis, and autophagy, as well as cell migration and epithelial-mesenchymal transition. We compared the secretome of p53-null AsPC-1 PDAC cells after ectopic over-expression of R175H-mutp53 or R273H-mutp53 to identify the differentially secreted proteins by mutant p53. By using high-resolution SWATH-MS technology, we found a great number of differentially secreted proteins by the two p53 mutants, 15 of which are common to both mutants. Most of these secreted proteins are reported to promote cancer progression and epithelial-mesenchymal transition and might constitute a biomarker secreted signature that is driven by the hot-spot p53 mutants in PDAC.
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Affiliation(s)
- Giovanna Butera
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Strada Le Grazie 8, 37134 Verona, Italy; (G.B.); (M.T.S.)
| | - Jessica Brandi
- Department of Biotechnology, University of Verona, 37134 Verona, Italy; (J.B.); (D.C.)
| | - Chiara Cavallini
- Research Center LURM (Interdepartmental Laboratory of Medical Research), University of Verona, 37134 Verona, Italy;
| | - Aldo Scarpa
- Department of Diagnostics and Public health, Section of Pathology, University of Verona, 37134 Verona, Italy;
- ARC-Net Centre for Applied Research on Cancer, University and Hospital Trust of Verona, 37134 Verona, Italy;
| | - Rita T. Lawlor
- ARC-Net Centre for Applied Research on Cancer, University and Hospital Trust of Verona, 37134 Verona, Italy;
| | - Maria Teresa Scupoli
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Strada Le Grazie 8, 37134 Verona, Italy; (G.B.); (M.T.S.)
- Research Center LURM (Interdepartmental Laboratory of Medical Research), University of Verona, 37134 Verona, Italy;
| | - Emílio Marengo
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, 28100 Novara, Italy;
- Center for Translational Research on Autoimmune and Allergic Diseases, University of Piemonte Orientale, Italy, ISALIT, Spin-off at the University of Piemonte Orientale, 28100 Novara, Italy
| | - Daniela Cecconi
- Department of Biotechnology, University of Verona, 37134 Verona, Italy; (J.B.); (D.C.)
| | - Marcello Manfredi
- Center for Translational Research on Autoimmune and Allergic Diseases, University of Piemonte Orientale, Italy, ISALIT, Spin-off at the University of Piemonte Orientale, 28100 Novara, Italy
- Department of Translational Medicine, University of Piemonte Orientale, Italy, CAAD, corso Trieste 15/A, 28100 Novara, Italy
- Correspondence: (M.M.); (M.D.); Tel.: +39-032-1660810 (M.M.); +39-045-8027281 (M.D.); Fax: +39-045-8027170 (M.D.)
| | - Massimo Donadelli
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Strada Le Grazie 8, 37134 Verona, Italy; (G.B.); (M.T.S.)
- Correspondence: (M.M.); (M.D.); Tel.: +39-032-1660810 (M.M.); +39-045-8027281 (M.D.); Fax: +39-045-8027170 (M.D.)
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28
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Cho KC, Clark DJ, Schnaubelt M, Teo GC, Leprevost FDV, Bocik W, Boja ES, Hiltke T, Nesvizhskii AI, Zhang H. Deep Proteomics Using Two Dimensional Data Independent Acquisition Mass Spectrometry. Anal Chem 2020; 92:4217-4225. [PMID: 32058701 PMCID: PMC7255061 DOI: 10.1021/acs.analchem.9b04418] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Methodologies that facilitate high-throughput proteomic analysis are a key step toward moving proteome investigations into clinical translation. Data independent acquisition (DIA) has potential as a high-throughput analytical method due to the reduced time needed for sample analysis, as well as its highly quantitative accuracy. However, a limiting feature of DIA methods is the sensitivity of detection of low abundant proteins and depth of coverage, which other mass spectrometry approaches address by two-dimensional fractionation (2D) to reduce sample complexity during data acquisition. In this study, we developed a 2D-DIA method intended for rapid- and deeper-proteome analysis compared to conventional 1D-DIA analysis. First, we characterized 96 individual fractions obtained from the protein standard, NCI-7, using a data-dependent approach (DDA), identifying a total of 151,366 unique peptides from 11,273 protein groups. We observed that the majority of the proteins can be identified from just a few selected fractions. By performing an optimization analysis, we identified six fractions with high peptide number and uniqueness that can account for 80% of the proteins identified in the entire experiment. These selected fractions were combined into a single sample which was then subjected to DIA (referred to as 2D-DIA) quantitative analysis. Furthermore, improved DIA quantification was achieved using a hybrid spectral library, obtained by combining peptides identified from DDA data with peptides identified directly from the DIA runs with the help of DIA-Umpire. The optimized 2D-DIA method allowed for improved identification and quantification of low abundant proteins compared to conventional unfractionated DIA analysis (1D-DIA). We then applied the 2D-DIA method to profile the proteomes of two breast cancer patient-derived xenograft (PDX) models, quantifying 6,217 and 6,167 unique proteins in basal- and luminal- tumors, respectively. Overall, this study demonstrates the potential of high-throughput quantitative proteomics using a novel 2D-DIA method.
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Affiliation(s)
- Kyung-Cho Cho
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, United States
| | - David J Clark
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, United States
| | - Michael Schnaubelt
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, United States
| | - Guo Ci Teo
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States
| | | | - William Bocik
- Antibody Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Emily S Boja
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, Maryland 20892, United States
| | - Tara Hiltke
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, Maryland 20892, United States
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, United States
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29
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Fernández-Costa C, Martínez-Bartolomé S, McClatchy D, Yates JR. Improving Proteomics Data Reproducibility with a Dual-Search Strategy. Anal Chem 2020; 92:1697-1701. [PMID: 31880919 DOI: 10.1021/acs.analchem.9b04955] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Mass spectrometry-based proteomics is an invaluable tool for addressing important biological questions. Data-dependent acquisition methods effectuate stochastic acquisition of data in complex mixtures, which results in missing identifications across replicates. We developed a search approach that improves the reproducibility of data acquired from any mass spectrometer. In our approach, a spectral library is built from the identification results from a database search, and then, the library is used to research the same data files to obtain the final result. We showed that higher identification and quantification reproducibility is achieved with the dual-search approach than with a typical database search. Four datasets with different complexity were compared: (1) data from a cell lysate study performed in our lab, (2) data from an interactome study performed in our lab, (3) a publicly available extracellular vesicles dataset, and (4) a publicly available phosphoproteomics dataset. Our results show that the dual-search approach can be widely and easily used to improve data quality in proteomics data.
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Affiliation(s)
- Carolina Fernández-Costa
- Department of Molecular Medicine , The Scripps Research Institute , La Jolla , California 92037 , United States
| | - Salvador Martínez-Bartolomé
- Department of Molecular Medicine , The Scripps Research Institute , La Jolla , California 92037 , United States
| | - Daniel McClatchy
- Department of Molecular Medicine , The Scripps Research Institute , La Jolla , California 92037 , United States
| | - John R Yates
- Department of Molecular Medicine , The Scripps Research Institute , La Jolla , California 92037 , United States
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30
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Lin YM, Chen CT, Chang JM. MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks. BMC Genomics 2019; 20:906. [PMID: 31874640 PMCID: PMC6929458 DOI: 10.1186/s12864-019-6297-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 11/15/2019] [Indexed: 01/22/2023] Open
Abstract
Background Tandem mass spectrometry allows biologists to identify and quantify protein samples in the form of digested peptide sequences. When performing peptide identification, spectral library search is more sensitive than traditional database search but is limited to peptides that have been previously identified. An accurate tandem mass spectrum prediction tool is thus crucial in expanding the peptide space and increasing the coverage of spectral library search. Results We propose MS2CNN, a non-linear regression model based on deep convolutional neural networks, a deep learning algorithm. The features for our model are amino acid composition, predicted secondary structure, and physical-chemical features such as isoelectric point, aromaticity, helicity, hydrophobicity, and basicity. MS2CNN was trained with five-fold cross validation on a three-way data split on the large-scale human HCD MS2 dataset of Orbitrap LC-MS/MS downloaded from the National Institute of Standards and Technology. It was then evaluated on a publicly available independent test dataset of human HeLa cell lysate from LC-MS experiments. On average, our model shows better cosine similarity and Pearson correlation coefficient (0.690 and 0.632) than MS2PIP (0.647 and 0.601) and is comparable with pDeep (0.692 and 0.642). Notably, for the more complex MS2 spectra of 3+ peptides, MS2PIP is significantly better than both MS2PIP and pDeep. Conclusions We showed that MS2CNN outperforms MS2PIP for 2+ and 3+ peptides and pDeep for 3+ peptides. This implies that MS2CNN, the proposed convolutional neural network model, generates highly accurate MS2 spectra for LC-MS/MS experiments using Orbitrap machines, which can be of great help in protein and peptide identifications. The results suggest that incorporating more data for deep learning model may improve performance.
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Affiliation(s)
- Yang-Ming Lin
- Department of Computer Science, National Chengchi University, 11605, Taipei City, Taiwan
| | - Ching-Tai Chen
- Institute of Information Science, Academia Sinica, 115, Taipei City, Taiwan
| | - Jia-Ming Chang
- Department of Computer Science, National Chengchi University, 11605, Taipei City, Taiwan.
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31
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Kawashima Y, Watanabe E, Umeyama T, Nakajima D, Hattori M, Honda K, Ohara O. Optimization of Data-Independent Acquisition Mass Spectrometry for Deep and Highly Sensitive Proteomic Analysis. Int J Mol Sci 2019; 20:ijms20235932. [PMID: 31779068 PMCID: PMC6928715 DOI: 10.3390/ijms20235932] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 11/20/2019] [Accepted: 11/23/2019] [Indexed: 02/07/2023] Open
Abstract
Data-independent acquisition (DIA)-mass spectrometry (MS)-based proteomic analysis overtop the existing data-dependent acquisition (DDA)-MS-based proteomic analysis to enable deep proteome coverage and precise relative quantitative analysis in single-shot liquid chromatography (LC)-MS/MS. However, DIA-MS-based proteomic analysis has not yet been optimized in terms of system robustness and throughput, particularly for its practical applications. We established a single-shot LC-MS/MS system with an MS measurement time of 90 min for a highly sensitive and deep proteomic analysis by optimizing the conditions of DIA and nanoLC. We identified 7020 and 4068 proteins from 200 ng and 10 ng, respectively, of tryptic floating human embryonic kidney cells 293 (HEK293F) cell digest by performing the constructed LC-MS method with a protein sequence database search. The numbers of identified proteins from 200 ng and 10 ng of tryptic HEK293F increased to 8509 and 5706, respectively, by searching the chromatogram library created by gas-phase fractionated DIA. Moreover, DIA protein quantification was highly reproducible, with median coefficients of variation of 4.3% in eight replicate analyses. We could demonstrate the power of this system by applying the proteomic analysis to detect subtle changes in protein profiles between cerebrums in germ-free and specific pathogen-free mice, which successfully showed that >40 proteins were differentially produced between the cerebrums in the presence or absence of bacteria.
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Affiliation(s)
- Yusuke Kawashima
- Department of Applied Genomics, Kazusa DNA Research Institute, Chiba 292-0818, Japan; (Y.K.); (D.N.)
| | - Eiichiro Watanabe
- Laboratory for Gut Homeostasis, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan; (E.W.); (K.H.)
- Department of Microbiology and Immunology, Keio University School of Medicine, Tokyo 160-8582, Japan
- Department of Pediatric Surgery, Faculty of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
| | - Taichi Umeyama
- Laboratory for Microbiome Sciences, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan; (T.U.); (M.H.)
| | - Daisuke Nakajima
- Department of Applied Genomics, Kazusa DNA Research Institute, Chiba 292-0818, Japan; (Y.K.); (D.N.)
| | - Masahira Hattori
- Laboratory for Microbiome Sciences, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan; (T.U.); (M.H.)
- Graduate School of Advanced Science and Engineering, Waseda University, Tokyo 169-8555, Japan
| | - Kenya Honda
- Laboratory for Gut Homeostasis, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan; (E.W.); (K.H.)
- Department of Microbiology and Immunology, Keio University School of Medicine, Tokyo 160-8582, Japan
| | - Osamu Ohara
- Department of Applied Genomics, Kazusa DNA Research Institute, Chiba 292-0818, Japan; (Y.K.); (D.N.)
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan
- Correspondence: ; Tel.: +81-438-52-391; Fax: +81-438-52-3914
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32
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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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Chen CT, Ko CL, Choong WK, Wang JH, Hsu WL, Sung TY. WinProphet: A User-Friendly Pipeline Management System for Proteomics Data Analysis Based on Trans-Proteomic Pipeline. Anal Chem 2019; 91:9403-9406. [DOI: 10.1021/acs.analchem.9b01556] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Ching-Tai Chen
- Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan
| | - Chu-Ling Ko
- Department of Computer Science, National Chiao Tung University, Hsinchu 300, Taiwan
| | - Wai-Kok Choong
- Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan
| | - Jen-Hung Wang
- Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan
| | - Ting-Yi Sung
- Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan
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34
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Saidi M, Kamali S, Beaudry F. Neuropeptidomics: Comparison of parallel reaction monitoring and data‐independent acquisition for the analysis of neuropeptides using high‐resolution mass spectrometry. Biomed Chromatogr 2019; 33:e4523. [DOI: 10.1002/bmc.4523] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 02/18/2019] [Accepted: 02/25/2019] [Indexed: 01/02/2023]
Affiliation(s)
- Mouna Saidi
- Groupe de Recherche en Pharmacologie Animal du Québec (GREPAQ), Département de Biomédecine Vétérinaire, Faculté de Médecine VétérinaireUniversité de Montréal Saint‐Hyacinthe Québec Canada
| | - Soufiane Kamali
- Groupe de Recherche en Pharmacologie Animal du Québec (GREPAQ), Département de Biomédecine Vétérinaire, Faculté de Médecine VétérinaireUniversité de Montréal Saint‐Hyacinthe Québec Canada
| | - Francis Beaudry
- Groupe de Recherche en Pharmacologie Animal du Québec (GREPAQ), Département de Biomédecine Vétérinaire, Faculté de Médecine VétérinaireUniversité de Montréal Saint‐Hyacinthe Québec Canada
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35
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Hu A, Lu YY, Bilmes J, Noble WS. Joint Precursor Elution Profile Inference via Regression for Peptide Detection in Data-Independent Acquisition Mass Spectra. J Proteome Res 2019; 18:86-94. [PMID: 30362768 PMCID: PMC6465123 DOI: 10.1021/acs.jproteome.8b00365] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
In data independent acquisition (DIA) mass spectrometry, precursor scans are interleaved with wide-window fragmentation scans, resulting in complex fragmentation spectra containing multiple coeluting peptide species. In this setting, detecting the isotope distribution profiles of intact peptides in the precursor scans can be a critical initial step in accurate peptide detection and quantification. This peak detection step is particularly challenging when the isotope peaks associated with two different peptide species overlap-or interfere-with one another. We propose a regression model, called Siren, to detect isotopic peaks in precursor DIA data that can explicitly account for interference. We validate Siren's peak-calling performance on a variety of data sets by counting how many of the peaks Siren identifies are associated with confidently detected peptides. In particular, we demonstrate that substituting the Siren regression model in place of the existing peak-calling step in DIA-Umpire leads to improved overall rates of peptide detection.
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36
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Deutsch EW, Perez-Riverol Y, Chalkley RJ, Wilhelm M, Tate S, Sachsenberg T, Walzer M, Käll L, Delanghe B, Böcker S, Schymanski EL, Wilmes P, Dorfer V, Kuster B, Volders PJ, Jehmlich N, Vissers JP, Wolan DW, Wang AY, Mendoza L, Shofstahl J, Dowsey AW, Griss J, Salek RM, Neumann S, Binz PA, Lam H, Vizcaíno JA, Bandeira N, Röst H. Expanding the Use of Spectral Libraries in Proteomics. J Proteome Res 2018; 17:4051-4060. [PMID: 30270626 PMCID: PMC6443480 DOI: 10.1021/acs.jproteome.8b00485] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.
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Affiliation(s)
- Eric W. Deutsch
- Institute for Systems Biology, Seattle, Washington, 98109, United States
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Robert J. Chalkley
- University of California San Francisco, San Francisco, 94158, California, United States
| | - Mathias Wilhelm
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, 85354, Germany
| | | | - Timo Sachsenberg
- Department of Computer Science, Center for Bioinformatics, University of Tübingen, Sand 14, Tübingen, 72076, Germany
| | - Mathias Walzer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Lukas Käll
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH − Royal Institute of Technology, Stockholm 114 28, Sweden
| | - Bernard Delanghe
- Thermo Fisher Scientific Bremen, Hanna-Kunath Str. 11, 28199 Bremen, Germany
| | - Sebastian Böcker
- Chair for Bioinformatics, Friedrich-Schiller-University Jena, 07743 Jena, Germany
| | - Emma L. Schymanski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Paul Wilmes
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Viktoria Dorfer
- University of Applied Sciences Upper Austria, Bioinformatics Research Group, Hagenberg, 4232, Austria
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, 85354, Germany
- Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), Technical University of Munich, Freising, 85354, Germany
| | | | - Nico Jehmlich
- Helmholtz-Centre for Environmental Research - UFZ, Leipzig, Germany
| | | | - Dennis W. Wolan
- Department of Molecular Medicine, The Scripps Research Institute, 92037, La Jolla, California, United States
| | - Ana Y. Wang
- Department of Molecular Medicine, The Scripps Research Institute, 92037, La Jolla, California, United States
| | - Luis Mendoza
- Institute for Systems Biology, Seattle, Washington, 98109, United States
| | - Jim Shofstahl
- Thermo Fisher Scientific, 355 River Oaks Parkway San Jose, CA 95134
| | - Andrew W. Dowsey
- Department of Population Health Sciences and Bristol Veterinary School, Faculty of Health Sciences, University of Bristol, Bristol BS9 1BN, UK
| | - Johannes Griss
- Division of Immunology, Allergy and Infectious Diseases, Department of Dermatology, Medical University of Vienna, Währinger Gürtel 18-20, Vienna 1090, Austria
| | - Reza M. Salek
- The International Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69372 Lyon CEDEX 08, France
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, 06120 Halle, Germany
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, 04103 Leipzig, Germany
| | - Pierre-Alain Binz
- Clinical Chemistry Service, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland
| | - Henry Lam
- Department of Chemical and Biological Engineering, the Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Nuno Bandeira
- Center for Computational Mass Spectrometry, Department of Computer Science and Engineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, 92093-0404, USA
| | - Hannes Röst
- The Donnelly Centre, University of Toronto, 160 College St., Toronto, ON, M5S 3E1, Canada
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37
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Borràs E, Sabidó E. DIA+: A Data-Independent Acquisition Method Combining Multiple Precursor Charges to Improve Peptide Signal. Anal Chem 2018; 90:12339-12341. [PMID: 30295461 DOI: 10.1021/acs.analchem.8b03418] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Data-independent acquisition methods that acquire fragment ions from virtually any peptide in a sample have expanded the benefits of low-throughput targeted proteomics to proteome-wide analyses. While these methods have increased the reproducibility of peptide quantification across multiple samples, their sensitivity is still limited, and the quantification of complete proteomes remains a challenge. Here we present DIA+, a DIA method that combines signals from identical peptides with different charge states, resulting in improved signal-to-noise, additional number of fragments, and therefore in a higher number of identified and quantified peptides in complex samples.
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Affiliation(s)
- Eva Borràs
- Proteomics Unit, Center for Genomic Regulation , Barcelona Institute of Science and Technology (BIST) , 08003 Barcelona , Spain.,Proteomics Unit , Universitat Pompeu Fabra , 08002 Barcelona , Spain
| | - Eduard Sabidó
- Proteomics Unit, Center for Genomic Regulation , Barcelona Institute of Science and Technology (BIST) , 08003 Barcelona , Spain.,Proteomics Unit , Universitat Pompeu Fabra , 08002 Barcelona , Spain
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38
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Barkovits K, Linden A, Galozzi S, Schilde L, Pacharra S, Mollenhauer B, Stoepel N, Steinbach S, May C, Uszkoreit J, Eisenacher M, Marcus K. Characterization of Cerebrospinal Fluid via Data-Independent Acquisition Mass Spectrometry. J Proteome Res 2018; 17:3418-3430. [PMID: 30207155 DOI: 10.1021/acs.jproteome.8b00308] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Cerebrospinal fluid (CSF) is in direct contact with the brain and serves as a valuable specimen to examine diseases of the central nervous system through analyzing its components. These include the analysis of metabolites, cells as well as proteins. For identifying new suitable diagnostic protein biomarkers bottom-up data-dependent acquisition (DDA) mass spectrometry-based approaches are most popular. Drawbacks of this method are stochastic and irreproducible precursor ion selection. Recently, data-independent acquisition (DIA) emerged as an alternative method. It overcomes several limitations of DDA, since it combines the benefits of DDA and targeted methods like selected reaction monitoring (SRM). We established a DIA method for in-depth proteome analysis of CSF. For this, four spectral libraries were generated with samples from native CSF ( n = 5), CSF fractionation (15 in total) and substantia nigra fractionation (54 in total) and applied to three CSF DIA replicates. The DDA and DIA methods for CSF were conducted with the same nanoLC parameters using a 180 min gradient. Compared to a conventional DDA method, our DIA approach increased the number of identified protein groups from 648 identifications in DDA to 1574 in DIA using a comprehensive spectral library generated with DDA measurements from five native CSF and 54 substantia nigra fractions. We also could show that a sample specific spectral library generated from native CSF only increased the identification reproducibility from three DIA replicates to 90% (77% with a DDA method). Moreover, by utilizing a substantia nigra specific spectral library for CSF DIA, over 60 brain-originated proteins could be identified compared to only 11 with DDA. In conclusion, the here presented optimized DIA method substantially outperforms DDA and could develop into a powerful tool for biomarker discovery in CSF. Data are available via ProteomeXchange with the identifiers PXD010698, PXD010708, PXD010690, PXD010705, and PXD009624.
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Affiliation(s)
- Katalin Barkovits
- Ruhr University Bochum, Medical Faculty , Medizinisches Proteom-Center , Universitaetsstrasse 150 , D-44801 Bochum , Germany
| | - Andreas Linden
- Ruhr University Bochum, Medical Faculty , Medizinisches Proteom-Center , Universitaetsstrasse 150 , D-44801 Bochum , Germany
| | - Sara Galozzi
- Ruhr University Bochum, Medical Faculty , Medizinisches Proteom-Center , Universitaetsstrasse 150 , D-44801 Bochum , Germany
| | - Lukas Schilde
- Ruhr University Bochum, Medical Faculty , Medizinisches Proteom-Center , Universitaetsstrasse 150 , D-44801 Bochum , Germany
| | - Sandra Pacharra
- Ruhr University Bochum, Medical Faculty , Medizinisches Proteom-Center , Universitaetsstrasse 150 , D-44801 Bochum , Germany
| | - Brit Mollenhauer
- Paracelsus-Elena-Klinik , Klinikstraße 16 , D-34128 Kassel , Germany
| | - Nadine Stoepel
- Ruhr University Bochum, Medical Faculty , Medizinisches Proteom-Center , Universitaetsstrasse 150 , D-44801 Bochum , Germany
| | - Simone Steinbach
- Ruhr University Bochum, Medical Faculty , Medizinisches Proteom-Center , Universitaetsstrasse 150 , D-44801 Bochum , Germany
| | - Caroline May
- Ruhr University Bochum, Medical Faculty , Medizinisches Proteom-Center , Universitaetsstrasse 150 , D-44801 Bochum , Germany
| | - Julian Uszkoreit
- Ruhr University Bochum, Medical Faculty , Medizinisches Proteom-Center , Universitaetsstrasse 150 , D-44801 Bochum , Germany
| | - Martin Eisenacher
- Ruhr University Bochum, Medical Faculty , Medizinisches Proteom-Center , Universitaetsstrasse 150 , D-44801 Bochum , Germany
| | - Katrin Marcus
- Ruhr University Bochum, Medical Faculty , Medizinisches Proteom-Center , Universitaetsstrasse 150 , D-44801 Bochum , Germany
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39
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Manfredi M, Chiariello C, Conte E, Castagna A, Robotti E, Gosetti F, Patrone M, Martinelli N, Bassi A, Cecconi D, Marengo E, Olivieri O. Plasma Proteome Profiles of Stable CAD Patients Stratified According to Total Apo C‐III Levels. Proteomics Clin Appl 2018; 13:e1800023. [DOI: 10.1002/prca.201800023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 06/30/2018] [Indexed: 12/31/2022]
Affiliation(s)
- Marcello Manfredi
- Department of Sciences and Technological InnovationUniversity of Piemonte Orientale Viale T. Michel 11 15121 Alessandria Italy
- ISALIT S.r.l. Via Canobia 4/6 28100 Novara Italy
| | - Carmela Chiariello
- Department of MedicineUnit of Internal MedicineUniversity of Verona P.le L.A. Scuro 10 37134 Verona Italy
| | | | - Annalisa Castagna
- Department of MedicineUnit of Internal MedicineUniversity of Verona P.le L.A. Scuro 10 37134 Verona Italy
| | - Elisa Robotti
- Department of Sciences and Technological InnovationUniversity of Piemonte Orientale Viale T. Michel 11 15121 Alessandria Italy
- ISALIT S.r.l. Via Canobia 4/6 28100 Novara Italy
| | - Fabio Gosetti
- Department of Sciences and Technological InnovationUniversity of Piemonte Orientale Viale T. Michel 11 15121 Alessandria Italy
| | - Mauro Patrone
- Department of Sciences and Technological InnovationUniversity of Piemonte Orientale Viale T. Michel 11 15121 Alessandria Italy
| | - Nicola Martinelli
- Department of MedicineUnit of Internal MedicineUniversity of Verona P.le L.A. Scuro 10 37134 Verona Italy
| | - Antonella Bassi
- Laboratory of Clinical Chemistry and HematologyUniversity Hospital of Verona P.le L.A. Scuro 10 37134 Verona Italy
| | - Daniela Cecconi
- Department of BiotechnologyProteomics and Mass Spectrometry LaboratoryUniversity of Verona Strada le grazie 15 37134 Verona Italy
| | - Emilio Marengo
- Department of Sciences and Technological InnovationUniversity of Piemonte Orientale Viale T. Michel 11 15121 Alessandria Italy
| | - Oliviero Olivieri
- Department of MedicineUnit of Internal MedicineUniversity of Verona P.le L.A. Scuro 10 37134 Verona Italy
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40
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Bilbao A, Gibbons BC, Slysz GW, Crowell KL, Monroe ME, Ibrahim YM, Smith RD, Payne SH, Baker ES. An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies. INTERNATIONAL JOURNAL OF MASS SPECTROMETRY 2018; 427:91-99. [PMID: 29706793 PMCID: PMC5920534 DOI: 10.1016/j.ijms.2017.11.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The mass accuracy and peak intensity of ions detected by mass spectrometry (MS) measurements are essential to facilitate compound identification and quantitation. However, high concentration species can yield erroneous results if their ion intensities reach beyond the limits of the detection system, leading to distorted and non-ideal detector response (e.g. saturation), and largely precluding the calculation of accurate m/z and intensity values. Here we present an open source computational method to correct peaks above a defined intensity (saturated) threshold determined by the MS instrumentation such as the analog-to-digital converters or time-to-digital converters used in conjunction with time-of-flight MS. In this method, the isotopic envelope for each observed ion above the saturation threshold is compared to its expected theoretical isotopic distribution. The most intense isotopic peak for which saturation does not occur is then utilized to re-calculate the precursor m/z and correct the intensity, resulting in both higher mass accuracy and greater dynamic range. The benefits of this approach were evaluated with proteomic and lipidomic datasets of varying complexities. After correcting the high concentration species, reduced mass errors and enhanced dynamic range were observed for both simple and complex omic samples. Specifically, the mass error dropped by more than 50% in most cases for highly saturated species and dynamic range increased by 1-2 orders of magnitude for peptides in a blood serum sample.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Erin S Baker
- Corresponding author at: 902 Battelle Blvd., P.O. Box 999, MSIN K8-98, Richland,WA 99352, USA. (E.S. Baker)
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41
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Koopmans F, Ho JTC, Smit AB, Li KW. Comparative Analyses of Data Independent Acquisition Mass Spectrometric Approaches: DIA, WiSIM-DIA, and Untargeted DIA. Proteomics 2018; 18:1700304. [PMID: 29134766 PMCID: PMC5817406 DOI: 10.1002/pmic.201700304] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 11/04/2017] [Indexed: 11/11/2022]
Abstract
Data-independent acquisition (DIA) is an emerging technology for quantitative proteomics. Current DIA focusses on the identification and quantitation of fragment ions that are generated from multiple peptides contained in the same selection window of several to tens of m/z. An alternative approach is WiSIM-DIA, which combines conventional DIA with wide-SIM (wide selected-ion monitoring) windows to partition the precursor m/z space to produce high-quality precursor ion chromatograms. However, WiSIM-DIA has been underexplored; it remains unclear if it is a viable alternative to DIA. We demonstrate that WiSIM-DIA quantified more than 24 000 unique peptides over five orders of magnitude in a single 2 h analysis of a neuronal synapse-enriched fraction, compared to 31 000 in DIA. There is a strong correlation between abundance values of peptides quantified in both the DIA and WiSIM-DIA datasets. Interestingly, the S/N ratio of these peptides is not correlated. We further show that peptide identification directly from DIA spectra identified >2000 proteins, which included unique peptides not found in spectral libraries generated by DDA.
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Affiliation(s)
- Frank Koopmans
- Department of Molecular and Cellular NeurobiologyCNCRAmsterdam NeuroscienceVrije UniversiteitAmsterdamThe Netherlands
| | | | - August B. Smit
- Department of Molecular and Cellular NeurobiologyCNCRAmsterdam NeuroscienceVrije UniversiteitAmsterdamThe Netherlands
| | - Ka Wan Li
- Department of Molecular and Cellular NeurobiologyCNCRAmsterdam NeuroscienceVrije UniversiteitAmsterdamThe Netherlands
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42
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Borràs E, Sabidó E. What is targeted proteomics? A concise revision of targeted acquisition and targeted data analysis in mass spectrometry. Proteomics 2017; 17. [PMID: 28719092 DOI: 10.1002/pmic.201700180] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2017] [Revised: 07/06/2017] [Accepted: 07/11/2017] [Indexed: 12/14/2022]
Abstract
Targeted proteomics has gained significant popularity in mass spectrometry-based protein quantification as a method to detect proteins of interest with high sensitivity, quantitative accuracy and reproducibility. However, with the emergence of a wide variety of targeted proteomics methods, some of them with high-throughput capabilities, it is easy to overlook the essence of each method and to determine what makes each of them a targeted proteomics method. In this viewpoint, we revisit the main targeted proteomics methods and classify them in four categories differentiating those methods that perform targeted data acquisition from targeted data analysis, and those methods that are based on peptide ion data (MS1 targeted methods) from those that rely on the peptide fragments (MS2 targeted methods).
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Affiliation(s)
- Eva Borràs
- Proteomics Unit, Centre de Regulació Genòmica, Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Eduard Sabidó
- Proteomics Unit, Centre de Regulació Genòmica, Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
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43
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Bowler RP, Wendt CH, Fessler MB, Foster MW, Kelly RS, Lasky-Su J, Rogers AJ, Stringer KA, Winston BW. New Strategies and Challenges in Lung Proteomics and Metabolomics. An Official American Thoracic Society Workshop Report. Ann Am Thorac Soc 2017; 14:1721-1743. [PMID: 29192815 PMCID: PMC5946579 DOI: 10.1513/annalsats.201710-770ws] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
This document presents the proceedings from the workshop entitled, "New Strategies and Challenges in Lung Proteomics and Metabolomics" held February 4th-5th, 2016, in Denver, Colorado. It was sponsored by the National Heart Lung Blood Institute, the American Thoracic Society, the Colorado Biological Mass Spectrometry Society, and National Jewish Health. The goal of this workshop was to convene, for the first time, relevant experts in lung proteomics and metabolomics to discuss and overcome specific challenges in these fields that are unique to the lung. The main objectives of this workshop were to identify, review, and/or understand: (1) emerging technologies in metabolomics and proteomics as applied to the study of the lung; (2) the unique composition and challenges of lung-specific biological specimens for metabolomic and proteomic analysis; (3) the diverse informatics approaches and databases unique to metabolomics and proteomics, with special emphasis on the lung; (4) integrative platforms across genetic and genomic databases that can be applied to lung-related metabolomic and proteomic studies; and (5) the clinical applications of proteomics and metabolomics. The major findings and conclusions of this workshop are summarized at the end of the report, and outline the progress and challenges that face these rapidly advancing fields.
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44
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Dalla Pozza E, Manfredi M, Brandi J, Buzzi A, Conte E, Pacchiana R, Cecconi D, Marengo E, Donadelli M. Trichostatin A alters cytoskeleton and energy metabolism of pancreatic adenocarcinoma cells: An in depth proteomic study. J Cell Biochem 2017; 119:2696-2707. [PMID: 29095525 DOI: 10.1002/jcb.26436] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 10/18/2017] [Indexed: 01/03/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal of all human cancers with a high mortality rate. Resistance to conventional treatments and chemotherapeutics is a typical feature of PDAC. To investigate the causes of drug resistance it is essential to deeply investigate the mechanism of action of chemotherapeutics. In this study, we performed an in depth shotgun proteomic approach using the label-free proteomic SWATH-MS analysis to investigate novel insights of the mechanism of action of the histone deacetylase (HDAC) inhibitor trichostatin A (TSA) in PDAC cells. This proteomic analysis in PaCa44 cells and data elaboration of TSA-regulated proteins by bioinformatics showed an overall up-regulation of cytokeratins and other proteins related to the cytoskeleton organization, keratinization, and apoptotic cell death. On the contrary, a large amount of the down-regulated proteins by TSA treatment belongs to the cellular energetic metabolism and to the machinery of protein synthesis, such as ribosomal proteins, determining synergistic cell growth inhibition by the combined treatment of TSA and the glycolytic inhibitor 2-deoxy-d-glucose in a panel of PDAC cell lines. Data are available via ProteomeXchange with identifier PXD007801.
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Affiliation(s)
- Elisa Dalla Pozza
- Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy
| | - Marcello Manfredi
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Alessandria, Italy.,ISALIT S.r.l., Spin-off of University of Piemonte Orientale, Novara, Italy
| | - Jessica Brandi
- Department of Biotechnology, Proteomics and Mass Spectrometry Laboratory, University of Verona, Verona, Italy
| | - Arianna Buzzi
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Alessandria, Italy
| | - Eleonora Conte
- ISALIT S.r.l., Spin-off of University of Piemonte Orientale, Novara, Italy
| | - Raffaella Pacchiana
- Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy
| | - Daniela Cecconi
- Department of Biotechnology, Proteomics and Mass Spectrometry Laboratory, University of Verona, Verona, Italy
| | - Emilio Marengo
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Alessandria, Italy
| | - Massimo Donadelli
- Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy
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Song Y, Zhong L, Zhou J, Lu M, Xing T, Ma L, Shen J. Data-Independent Acquisition-Based Quantitative Proteomic Analysis Reveals Potential Biomarkers of Kidney Cancer. Proteomics Clin Appl 2017; 11. [PMID: 28975715 DOI: 10.1002/prca.201700066] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Revised: 08/27/2017] [Indexed: 12/16/2022]
Abstract
PURPOSE Renal cell carcinoma (RCC) is a malignant and metastatic cancer with 95% mortality, and clear cell RCC (ccRCC) is the most observed among the five major subtypes of RCC. Specific biomarkers that can distinguish cancer tissues from adjacent normal tissues should be developed to diagnose this disease in early stages and conduct a reliable prognostic evaluation. EXPERIMENTAL DESIGN Data-independent acquisition (DIA) strategy has been widely employed in proteomic analysis because of various advantages, including enhanced protein coverage and reliable data acquisition. In this study, a DIA workflow is constructed on a quadrupole-Orbitrap LC-MS platform to reveal dysregulated proteins between ccRCC and adjacent normal tissues. RESULTS More than 4000 proteins are identified, 436 of these proteins are dysregulated in ccRCC tissues. Bioinformatic analysis reveals that multiple pathways and Gene Ontology items are strongly associated with ccRCC. The expression levels of L-lactate dehydrogenase A chain, annexin A4, nicotinamide N-methyltransferase, and perilipin-2 examined through RT-qPCR, Western blot, and immunohistochemistry confirm the validity of the proteomic analysis results. CONCLUSIONS AND CLINICAL RELEVANCE The proposed DIA workflow yields optimum time efficiency and data reliability and provides a good choice for proteomic analysis in biological and clinical studies, and these dysregulated proteins might be potential biomarkers for ccRCC diagnosis.
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Affiliation(s)
- Yimeng Song
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Lijun Zhong
- Medical and Health Analytical Center, Peking University Health Science Center, Beijing, China
| | - Juntuo Zhou
- Department of Pathology, School of Basic Medical Science, Peking University Health Science Center, Beijing, China
| | - Min Lu
- Department of Pathology, School of Basic Medical Science, Peking University Health Science Center, Beijing, China
| | - Tianying Xing
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Lulin Ma
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Jing Shen
- Key Laboratory of Carcinogenesis and Translational Research Ministry of Education/Beijing, Central Laboratory, Peking University Cancer Hospital and Institute, Beijing, 100142, China
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Rosenberger G, Bludau I, Schmitt U, Heusel M, Hunter CL, Liu Y, MacCoss MJ, MacLean BX, Nesvizhskii AI, Pedrioli PGA, Reiter L, Röst HL, Tate S, Ting YS, Collins BC, Aebersold R. Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses. Nat Methods 2017; 14:921-927. [PMID: 28825704 PMCID: PMC5581544 DOI: 10.1038/nmeth.4398] [Citation(s) in RCA: 145] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Accepted: 07/07/2017] [Indexed: 12/18/2022]
Abstract
Liquid chromatography coupled to tandem mass spectrometry is the main method for high-throughput identification and quantification of peptides and inferred proteins. Within this field, data-independent acquisition (DIA) combined with peptide-centric scoring, exemplified by SWATH-MS, emerged as a scalable method to achieve deep and consistent proteome coverage across large-scale datasets. Here we discuss the adaptation of statistical concepts developed for discovery proteomics based on spectrum-centric scoring to large-scale DIA experiments analyzed with peptide-centric scoring strategies and provide guidance on their application. We show that optimal tradeoffs between sensitivity and specificity require careful considerations of the relationship between proteins in the samples and proteins represented in the spectral library. We propose the application of a global analyte constraint to prevent accumulation of false positives across large-scale datasets. Furthermore, to increase the quality and reproducibility of published proteomic results, well-established confidence criteria should be reported for detected peptide queries, peptides and inferred proteins.
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Affiliation(s)
- George Rosenberger
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Isabell Bludau
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Uwe Schmitt
- ID Scientific IT Services, ETH Zurich, Zurich, Switzerland
| | - Moritz Heusel
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,PhD program in Molecular and Translational Biomedicine, Competence Center Personalized Medicine (CC-PM), ETH Zurich and University of Zurich, Zurich, Switzerland
| | | | - Yansheng Liu
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Brendan X MacLean
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.,Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Patrick G A Pedrioli
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | | | - Hannes L Röst
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | | | - Ying S Ting
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - 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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47
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Rosenberger G, Liu Y, Röst HL, Ludwig C, Buil A, Bensimon A, Soste M, Spector TD, Dermitzakis ET, Collins BC, Malmström L, Aebersold R. Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS. Nat Biotechnol 2017; 35:781-788. [PMID: 28604659 PMCID: PMC5593115 DOI: 10.1038/nbt.3908] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 05/22/2017] [Indexed: 01/01/2023]
Abstract
Consistent detection and quantification of protein post-translational modifications (PTMs) across sample cohorts is a prerequisite for functional analysis of biological processes. Data-independent acquisition (DIA) is a bottom-up mass spectrometry approach that provides complete information on precursor and fragment ions. However, owing to the convoluted structure of DIA data sets, confident, systematic identification and quantification of peptidoforms has remained challenging. Here, we present inference of peptidoforms (IPF), a fully automated algorithm that uses spectral libraries to query, validate and quantify peptidoforms in DIA data sets. The method was developed on data acquired by the DIA method SWATH-MS and benchmarked using a synthetic phosphopeptide reference data set and phosphopeptide-enriched samples. IPF reduced false site-localization by more than sevenfold compared with previous approaches, while recovering 85.4% of the true signals. Using IPF, we quantified peptidoforms in DIA data acquired from >200 samples of blood plasma of a human twin cohort and assessed the contribution of heritable, environmental and longitudinal effects on their PTMs.
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Affiliation(s)
- George Rosenberger
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Yansheng Liu
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Hannes L Röst
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Department of Genetics, Stanford University, Stanford, California, USA
| | - Christina Ludwig
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), Technical University Munich, Freising, Germany
| | - Alfonso Buil
- Research Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Roskilde, Denmark
| | - Ariel Bensimon
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Martin Soste
- Department of Biology, Institute of Biochemistry, ETH Zurich, Zurich, Switzerland
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital Campus, London, UK
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Ben C Collins
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Lars Malmström
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,S3IT, University of 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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48
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Kreimer S, Gao Y, Ray S, Jin M, Tan Z, Mussa NA, Tao L, Li Z, Ivanov AR, Karger BL. Host Cell Protein Profiling by Targeted and Untargeted Analysis of Data Independent Acquisition Mass Spectrometry Data with Parallel Reaction Monitoring Verification. Anal Chem 2017; 89:5294-5302. [PMID: 28402653 DOI: 10.1021/acs.analchem.6b04892] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Host cell proteins (HCPs) are process-related impurities of biopharmaceuticals that remain at trace levels despite multiple stages of downstream purification. Currently, there is interest in implementing LC-MS in biopharmaceutical HCP profiling alongside conventional ELISA, because individual species can be identified and quantitated. Conventional data dependent LC-MS is hampered by the low concentration of HCP-derived peptides, which are 5-6 orders of magnitude less abundant than the biopharmaceutical-derived peptides. In this paper, we present a novel data independent acquisition (DIA)-MS workflow to identify HCP peptides using automatically combined targeted and untargeted data processing, followed by verification and quantitation using parallel reaction monitoring (PRM). Untargeted data processing with DIA-Umpire provided a means of identifying HCPs not represented in the assay library used for targeted, peptide-centric, data analysis. An IgG1 monoclonal antibody (mAb) purified by Protein A column elution, cation exchange chromatography, and ultrafiltration was analyzed using the workflow with 1D-LC. Five protein standards added at 0.5 to 100 ppm concentrations were detected in the background of the purified mAb, demonstrating sensitivity to low ppm levels. A calibration curve was constructed on the basis of the summed peak areas of the three highest intensity fragment ions from the highest intensity peptide of each protein standard. Sixteen HCPs were identified and quantitated on the basis of the calibration curve over the range of low ppm to over 100 ppm in the purified mAb sample. The developed approach achieves rapid HCP profiling using 1D-LC and specific identification exploiting the high mass accuracy and resolution of the mass spectrometer.
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Affiliation(s)
- Simion Kreimer
- Barnett Institute and Department of Chemistry and Chemical Biology, Northeastern University , Boston, Massachusetts 02115, United States
| | - Yuanwei Gao
- Barnett Institute and Department of Chemistry and Chemical Biology, Northeastern University , Boston, Massachusetts 02115, United States
| | - Somak Ray
- Barnett Institute and Department of Chemistry and Chemical Biology, Northeastern University , Boston, Massachusetts 02115, United States
| | - Mi Jin
- Bristol-Myers Squibb , Biologics Process and Product Development, 38 Jackson Road, Devens, Massachusetts 01434, United States
| | - Zhijun Tan
- Bristol-Myers Squibb , Biologics Process and Product Development, 38 Jackson Road, Devens, Massachusetts 01434, United States
| | - Nesredin A Mussa
- Bristol-Myers Squibb , Biologics Process and Product Development, 38 Jackson Road, Devens, Massachusetts 01434, United States
| | - Li Tao
- Bristol-Myers Squibb , Biologics Process and Product Development, 38 Jackson Road, Devens, Massachusetts 01434, United States
| | - Zhengjian Li
- Bristol-Myers Squibb , Biologics Process and Product Development, 38 Jackson Road, Devens, Massachusetts 01434, United States
| | - Alexander R Ivanov
- Barnett Institute and Department of Chemistry and Chemical Biology, Northeastern University , Boston, Massachusetts 02115, United States
| | - Barry L Karger
- Barnett Institute and Department of Chemistry and Chemical Biology, Northeastern University , Boston, Massachusetts 02115, United States
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49
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Meyer JG, Schilling B. Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques. Expert Rev Proteomics 2017; 14:419-429. [PMID: 28436239 PMCID: PMC5671767 DOI: 10.1080/14789450.2017.1322904] [Citation(s) in RCA: 100] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
INTRODUCTION While selected/multiple-reaction monitoring (SRM or MRM) is considered the gold standard for quantitative protein measurement, emerging data-independent acquisition (DIA) using high-resolution scans have opened a new dimension of high-throughput, comprehensive quantitative proteomics. These newer methodologies are particularly well suited for discovery of biomarker candidates from human disease samples, and for investigating and understanding human disease pathways. Areas covered: This article reviews the current state of targeted and untargeted DIA mass spectrometry-based proteomic workflows, including SRM, parallel-reaction monitoring (PRM) and untargeted DIA (e.g., SWATH). Corresponding bioinformatics strategies, as well as application in biological and clinical studies are presented. Expert commentary: Nascent application of highly-multiplexed untargeted DIA, such as SWATH, for accurate protein quantification from clinically relevant and disease-related samples shows great potential to comprehensively investigate biomarker candidates and understand disease.
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Affiliation(s)
- Jesse G Meyer
- a Mass Spectrometry Core , Buck Institute for Research on Aging , Novato , CA , USA
| | - Birgit Schilling
- a Mass Spectrometry Core , Buck Institute for Research on Aging , Novato , CA , USA
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50
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Cho JY, Lee HJ, Jeong SK, Paik YK. Epsilon-Q: An Automated Analyzer Interface for Mass Spectral Library Search and Label-Free Protein Quantification. J Proteome Res 2017; 16:4435-4445. [DOI: 10.1021/acs.jproteome.6b01019] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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