1
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Shi M, Huang C, Chen R, Chen DDY, Yan B. A New Evaluation Metric for Quantitative Accuracy of LC-MS/MS-Based Proteomics with Data-Independent Acquisition. J Proteome Res 2024; 23:3780-3790. [PMID: 39193824 DOI: 10.1021/acs.jproteome.4c00088] [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] [Indexed: 08/29/2024]
Abstract
Data-independent acquisition (DIA) has improved the identification and quantitation coverage of peptides and proteins in liquid chromatography-tandem mass spectrometry-based proteomics. However, different DIA data-processing tools can produce very different identification and quantitation results for the same data set. Currently, benchmarking studies of DIA tools are predominantly focused on comparing the identification results, while the quantitative accuracy of DIA measurements is acknowledged to be important but insufficiently investigated, and the absence of suitable metrics for comparing quantitative accuracy is one of the reasons. A new metric is proposed for the evaluation of quantitative accuracy to avoid the influence of differences in false discovery rate control stringency. The part of the quantitation results with high reliability was acquired from each DIA tool first, and the quantitative accuracy was evaluated by comparing quantification error rates at the same number of accurate ratios. From the results of four benchmark data sets, the proposed metric was shown to be more sensitive to discriminating the quantitative performance of DIA tools. Moreover, the DIA tools with advantages in quantitative accuracy were consistently revealed by this metric. The proposed metric can also help researchers in optimizing algorithms of the same DIA tool and sample preprocessing methods to enhance quantitative accuracy.
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Affiliation(s)
- Mengtian Shi
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
| | - Chiyuan Huang
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
| | - Renhui Chen
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
| | - David Da Yong Chen
- Department of Chemistry, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
| | - Binjun Yan
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
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2
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Junior SM, Levander F. Automated multiplexed affinity-based enrichment of peptides for LC-MS/MS plasma proteomics. Proteomics 2024:e2400049. [PMID: 39192483 DOI: 10.1002/pmic.202400049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 08/05/2024] [Accepted: 08/19/2024] [Indexed: 08/29/2024]
Abstract
Plasma proteomics offers high potential for biomarker discovery, as plasma is collected through a minimally invasive procedure and constitutes the most complex human-derived proteome. However, the wide dynamic range poses a significant challenge. Here, we propose a semi-automated method based on the use of multiple single chain variable fragment antibodies, each enriching for peptides found in up to a few hundred proteins. This approach allows for the analysis of a complementary fraction compared to full proteome analysis. Proteins from pooled plasma were extracted and digested before testing the performance of 29 different antibodies with the aim of reproducibly maximizing peptide enrichment. Our results demonstrate the enrichment of 3662 peptides not detected in neat plasma or negative controls. Moreover, most antibodies were able to enrich for at least 155 peptides across different levels of abundance in plasma. To further reduce analysis time, a combination of antibodies was used in a multiplexed setting. Repeated sample analyses showed low coefficients of variation, and the method is flexible in terms of affinity binders. It does not impose drastic increases in instrument time, thus showing excellent potential for usage in large scale discovery projects.
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Affiliation(s)
| | - Fredrik Levander
- Department of Immunotechnology, Lund University, Lund, Sweden
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Lund University, Lund, Sweden
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3
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Ivanov MV, Kopeykina AS, Gorshkov MV. Reanalysis of DIA Data Demonstrates the Capabilities of MS/MS-Free Proteomics to Reveal New Biological Insights in Disease-Related Samples. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:1775-1785. [PMID: 38938158 DOI: 10.1021/jasms.4c00134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
Data-independent acquisition (DIA) at the shortened data acquisition time is becoming a method of choice for quantitative proteomic applications requiring high throughput analysis of large cohorts of samples. With the advent of the combination of high resolution mass spectrometry with an asymmetric track lossless analyzer, these DIA capabilities were further extended with the recent demonstration of quantitative analyses at the speed of up to hundreds of samples per day. In particular, the proteomic data for the brain samples related to multiple system atrophy disease were acquired using 7 and 28 min chromatography gradients (Guzman et al., Nat. Biotech. 2024). In this work, we applied the recently introduced DirectMS1 method to reanalysis of these data using only MS1 spectra. Both DirectMS1 and DIA results were matched against long gradient DDA analysis from the earlier study of the same sample cohort. While the quantitation efficiency of DirectMS1 was comparable with DIA on the same data sets, we found an additional five proteins of biological significance relevant to the analyzed tissue samples. Among the findings, DirectMS1 was able to detect decreased caspase activity for Vimentin protein in the multiple system atrophy samples missed by the MS/MS-based quantitation methods. Our study suggests that DirectMS1 can be an efficient MS1-only addition to the analysis of DIA data in high-throughput quantitative proteomic studies.
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Affiliation(s)
- Mark V Ivanov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, Moscow 119334, Russia
| | - Anna S Kopeykina
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, Moscow 119334, Russia
| | - Mikhail V Gorshkov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, Moscow 119334, Russia
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4
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Fröhlich K, Fahrner M, Brombacher E, Seredynska A, Maldacker M, Kreutz C, Schmidt A, Schilling O. Data-Independent Acquisition: A Milestone and Prospect in Clinical Mass Spectrometry-Based Proteomics. Mol Cell Proteomics 2024; 23:100800. [PMID: 38880244 PMCID: PMC11380018 DOI: 10.1016/j.mcpro.2024.100800] [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: 02/02/2024] [Revised: 06/08/2024] [Accepted: 06/13/2024] [Indexed: 06/18/2024] Open
Abstract
Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given m/z range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.
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Affiliation(s)
- Klemens Fröhlich
- Proteomics Core Facility, Biozentrum Basel, University of Basel, Basel, Switzerland
| | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany
| | - Eva Brombacher
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany; Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Freiburg, Germany; Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg, Germany; Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Adrianna Seredynska
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany; Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Maximilian Maldacker
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany; Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Freiburg, Germany
| | - Alexander Schmidt
- Proteomics Core Facility, Biozentrum Basel, University of Basel, Basel, Switzerland
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany.
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5
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Humphries EM, Xavier D, Ashman K, Hains PG, Robinson PJ. High-Throughput Proteomics and Phosphoproteomics of Rat Tissues Using Microflow Zeno SWATH. J Proteome Res 2024; 23:2355-2366. [PMID: 38819404 DOI: 10.1021/acs.jproteome.4c00010] [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] [Indexed: 06/01/2024]
Abstract
High-throughput tissue proteomics has great potential in the advancement of precision medicine. Here, we investigated the combined sensitivity of trap-elute microflow liquid chromatography with a ZenoTOF for DIA proteomics and phosphoproteomics. Method optimization was conducted on HEK293T cell lines to determine the optimal variable window size, MS2 accumulation time and gradient length. The ZenoTOF 7600 was then compared to the previous generation TripleTOF 6600 using eight rat organs, finding up to 23% more proteins using a fifth of the sample load and a third of the instrument time. Spectral reference libraries generated from Zeno SWATH data in FragPipe (MSFragger-DIA/DIA-NN) contained 4 times more fragment ions than the DIA-NN only library and quantified more proteins. Replicate single-shot phosphopeptide enrichments of 50-100 μg of rat tryptic peptide were analyzed by microflow HPLC using Zeno SWATH without fractionation. Using Spectronaut we quantified a shallow phosphoproteome containing 1000-3000 phosphoprecursors per organ. Promisingly, clear hierarchical clustering of organs was observed with high Pearson correlation coefficients >0.95 between replicate enrichments and median CV of 20%. The combined sensitivity of microflow HPLC with Zeno SWATH allows for the high-throughput quantitation of an extensive proteome and shallow phosphoproteome from small tissue samples.
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Affiliation(s)
- Erin M Humphries
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales 2145, Australia
| | - Dylan Xavier
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales 2145, Australia
| | - Keith Ashman
- Sciex, 96 Ricketts Road,Mount Waverley, Victoria 3149, Australia
| | - Peter G Hains
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales 2145, Australia
| | - Phillip J Robinson
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales 2145, Australia
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6
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Peng H, Wang H, Kong W, Li J, Goh WWB. Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference. Nat Commun 2024; 15:3922. [PMID: 38724498 PMCID: PMC11082229 DOI: 10.1038/s41467-024-47899-w] [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: 06/19/2023] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Identification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, matrix normalization, missing value imputation (MVI), and differential expression analysis. The plethora of options in each step makes it challenging to identify optimal workflows that maximize the identification of differentially expressed proteins. To identify optimal workflows and their common properties, we conduct an extensive study involving 34,576 combinatoric experiments on 24 gold standard spike-in datasets. Applying frequent pattern mining techniques to top-ranked workflows, we uncover high-performing rules that demonstrate optimality has conserved properties. Via machine learning, we confirm optimal workflows are indeed predictable, with average cross-validation F1 scores and Matthew's correlation coefficients surpassing 0.84. We introduce an ensemble inference to integrate results from individual top-performing workflows for expanding differential proteome coverage and resolve inconsistencies. Ensemble inference provides gains in pAUC (up to 4.61%) and G-mean (up to 11.14%) and facilitates effective aggregation of information across varied quantification approaches such as topN, directLFQ, MaxLFQ intensities, and spectral counts. However, further development and evaluation are needed to establish acceptable frameworks for conducting ensemble inference on multiple proteomics workflows.
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Affiliation(s)
- Hui Peng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - He Wang
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Weijia Kong
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Jinyan Li
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore.
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore.
- Center of AI in Medicine, Nanyang Technological University, Singapore, Singapore.
- Division of Neurology, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.
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7
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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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8
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Gerault MA, Camoin L, Granjeaud S. DIAgui: a Shiny application to process the output from DIA-NN. BIOINFORMATICS ADVANCES 2024; 4:vbae001. [PMID: 38249340 PMCID: PMC10799745 DOI: 10.1093/bioadv/vbae001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/24/2023] [Accepted: 01/04/2024] [Indexed: 01/23/2024]
Abstract
Summary DIAgui is an R package to simplify the processing of the report file from the DIA-NN software thanks to a Shiny application. It returns the quantification of either the precursors, the peptides, the proteins, or the genes thanks to the MaxLFQ algorithm. In addition, the latest version provides the Top3 and iBAQ quantification and the number of peptides used for the quantification. In the end, DIAgui produces ready-to-interpret files from the results of DIA mass spectrometry analysis and provides visualization and statistical tools that can be used in a user-friendly way. Availability and implementation Code and documentation are available on GitHub at https://github.com/marseille-proteomique/DIAgui. The package is written in R and also uses C++ code. A vignette shows its use in an R command line workflow.
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Affiliation(s)
- Marc-Antoine Gerault
- Aix-Marseille University, INSERM, CNRS, Institut Paoli-Calmettes, CRCM, Marseille Protéomique, Marseille F-13009, France
- Centrale Marseille School, Marseille F-13013, France
| | - Luc Camoin
- Aix-Marseille University, INSERM, CNRS, Institut Paoli-Calmettes, CRCM, Marseille Protéomique, Marseille F-13009, France
| | - Samuel Granjeaud
- Aix-Marseille University, INSERM, CNRS, Institut Paoli-Calmettes, CRCM, Marseille Protéomique, Marseille F-13009, France
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9
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Hay BN, Akinlaja MO, Baker TC, Houfani AA, Stacey RG, Foster LJ. Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities. Proteomics 2023; 23:e2200278. [PMID: 37144656 DOI: 10.1002/pmic.202200278] [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: 10/28/2022] [Revised: 04/03/2023] [Accepted: 04/14/2023] [Indexed: 05/06/2023]
Abstract
Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.
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Affiliation(s)
- Brenna N Hay
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Mopelola O Akinlaja
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Teesha C Baker
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Aicha Asma Houfani
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - R Greg Stacey
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Leonard J Foster
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
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10
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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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11
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Bennike TB. Advances in proteomics: characterization of the innate immune system after birth and during inflammation. Front Immunol 2023; 14:1254948. [PMID: 37868984 PMCID: PMC10587584 DOI: 10.3389/fimmu.2023.1254948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 09/26/2023] [Indexed: 10/24/2023] Open
Abstract
Proteomics is the characterization of the protein composition, the proteome, of a biological sample. It involves the large-scale identification and quantification of proteins, peptides, and post-translational modifications. This review focuses on recent developments in mass spectrometry-based proteomics and provides an overview of available methods for sample preparation to study the innate immune system. Recent advancements in the proteomics workflows, including sample preparation, have significantly improved the sensitivity and proteome coverage of biological samples including the technically difficult blood plasma. Proteomics is often applied in immunology and has been used to characterize the levels of innate immune system components after perturbations such as birth or during chronic inflammatory diseases like rheumatoid arthritis (RA) and inflammatory bowel disease (IBD). In cancers, the tumor microenvironment may generate chronic inflammation and release cytokines to the circulation. In these situations, the innate immune system undergoes profound and long-lasting changes, the large-scale characterization of which may increase our biological understanding and help identify components with translational potential for guiding diagnosis and treatment decisions. With the ongoing technical development, proteomics will likely continue to provide increasing insights into complex biological processes and their implications for health and disease. Integrating proteomics with other omics data and utilizing multi-omics approaches have been demonstrated to give additional valuable insights into biological systems.
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Affiliation(s)
- Tue Bjerg Bennike
- Medical Microbiology and Immunology, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
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12
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Zhang B, Bassani-Sternberg M. Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery. J Immunother Cancer 2023; 11:e007073. [PMID: 37899131 PMCID: PMC10619091 DOI: 10.1136/jitc-2023-007073] [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] [Accepted: 07/21/2023] [Indexed: 10/31/2023] Open
Abstract
Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that rely on T cell recognition and killing of tumor cells. Mass spectrometry (MS)-based immunopeptidomics enables high-throughput, direct identification of HLA-bound peptides from a variety of cell lines, tumor tissues, and healthy tissues. It involves immunoaffinity purification of HLA complexes followed by MS profiling of the extracted peptides using data-dependent acquisition, data-independent acquisition, or targeted approaches. By incorporating DNA, RNA, and ribosome sequencing data into immunopeptidomics data analysis, the proteogenomic approach provides a powerful means for identifying tumor antigens encoded within the canonical open reading frames of annotated coding genes and non-canonical tumor antigens derived from presumably non-coding regions of our genome. We discuss emerging computational challenges in immunopeptidomics data analysis and tumor antigen identification, highlighting key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, careful incorporation of predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination, and immunogenicity prediction. The advancements in technology and computation is continually enabling us to identify tumor antigens with higher sensitivity and accuracy, paving the way toward the development of more effective cancer immunotherapies.
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Affiliation(s)
- Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
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13
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Zhang F, Ge W, Huang L, Li D, Liu L, Dong Z, Xu L, Ding X, Zhang C, Sun Y, A J, Gao J, Guo T. A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry. Mol Cell Proteomics 2023; 22:100623. [PMID: 37481071 PMCID: PMC10458344 DOI: 10.1016/j.mcpro.2023.100623] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 06/12/2023] [Accepted: 07/18/2023] [Indexed: 07/24/2023] Open
Abstract
Data-independent acquisition (DIA) mass spectrometry-based proteomics generates reproducible proteome data. The complex processing of the DIA data has led to the development of multiple data analysis tools. In this study, we assessed the performance of five tools (OpenSWATH, EncyclopeDIA, Skyline, DIA-NN, and Spectronaut) using six DIA datasets obtained from TripleTOF, Orbitrap, and TimsTOF Pro instruments. By comparing identification and quantification metrics and examining shared and unique cross-tool identifications, we evaluated both library-based and library-free approaches. Our findings indicate that library-free approaches outperformed library-based methods when the spectral library had limited comprehensiveness. However, our results also suggest that constructing a comprehensive library still offers benefits for most DIA analyses. This study provides comprehensive guidance for DIA data analysis tools, benefiting both experienced and novice users of DIA-mass spectrometry technology.
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Affiliation(s)
- Fangfei Zhang
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China.
| | - Weigang Ge
- Westlake Omics, Ltd, Hangzhou, Zhejiang Province, China
| | | | - Dan Li
- Westlake Omics, Ltd, Hangzhou, Zhejiang Province, China
| | - Lijuan Liu
- Westlake Omics, Ltd, Hangzhou, Zhejiang Province, China
| | - Zhen Dong
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Luang Xu
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Xuan Ding
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Cheng Zhang
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Yingying Sun
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Jun A
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Jinlong Gao
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Tiannan Guo
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China.
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14
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George AL, Sidgwick FR, Watt JE, Martin MP, Trost M, Marín-Rubio JL, Dueñas ME. Comparison of Quantitative Mass Spectrometric Methods for Drug Target Identification by Thermal Proteome Profiling. J Proteome Res 2023; 22:2629-2640. [PMID: 37439223 PMCID: PMC10407934 DOI: 10.1021/acs.jproteome.3c00111] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Indexed: 07/14/2023]
Abstract
Thermal proteome profiling (TPP) provides a powerful approach to studying proteome-wide interactions of small therapeutic molecules and their target and off-target proteins, complementing phenotypic-based drug screens. Detecting differences in thermal stability due to target engagement requires high quantitative accuracy and consistent detection. Isobaric tandem mass tags (TMTs) are used to multiplex samples and increase quantification precision in TPP analysis by data-dependent acquisition (DDA). However, advances in data-independent acquisition (DIA) can provide higher sensitivity and protein coverage with reduced costs and sample preparation steps. Herein, we explored the performance of different DIA-based label-free quantification approaches compared to TMT-DDA for thermal shift quantitation. Acute myeloid leukemia cells were treated with losmapimod, a known inhibitor of MAPK14 (p38α). Label-free DIA approaches, and particularly the library-free mode in DIA-NN, were comparable of TMT-DDA in their ability to detect target engagement of losmapimod with MAPK14 and one of its downstream targets, MAPKAPK3. Using DIA for thermal shift quantitation is a cost-effective alternative to labeled quantitation in the TPP pipeline.
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Affiliation(s)
- Amy L. George
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
| | - Frances R. Sidgwick
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
| | - Jessica E. Watt
- Newcastle
Cancer Centre, Northern Institute for Cancer Research, Medical School, Newcastle University, Paul O’Gorman Building, Framlington Place, Newcastle upon Tyne NE2 4HH, U.K.
| | - Mathew P. Martin
- Newcastle
Cancer Centre, Northern Institute for Cancer Research, Medical School, Newcastle University, Paul O’Gorman Building, Framlington Place, Newcastle upon Tyne NE2 4HH, U.K.
| | - Matthias Trost
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
| | - José Luis Marín-Rubio
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
| | - Maria Emilia Dueñas
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
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15
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Beaumal C, Beck A, Hernandez-Alba O, Carapito C. Advanced mass spectrometry workflows for accurate quantification of trace-level host cell proteins in drug products: Benefits of FAIMS separation and gas-phase fractionation DIA. Proteomics 2023; 23:e2300172. [PMID: 37148167 DOI: 10.1002/pmic.202300172] [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: 03/31/2023] [Revised: 04/19/2023] [Accepted: 04/19/2023] [Indexed: 05/08/2023]
Abstract
Therapeutic monoclonal antibodies (mAb) production relies on multiple purification steps before release as a drug product (DP). A few host cell proteins (HCPs) may co-purify with the mAb. Their monitoring is crucial due to the considerable risk they represent for mAb stability, integrity, and efficacy and their potential immunogenicity. Enzyme-linked immunosorbent assays (ELISA) commonly used for global HCP monitoring present limitations in terms of identification and quantification of individual HCPs. Therefore, liquid chromatography tandem mass spectrometry (LC-MS/MS) has emerged as a promising alternative. Challenging DP samples show an extreme dynamic range requiring high performing methods to detect and reliably quantify trace-level HCPs. Here, we investigated the benefits of adding high-field asymmetric ion mobility spectrometry (FAIMS) separation and gas phase fractionation (GPF) prior to data independent acquisition (DIA). FAIMS LC-MS/MS analysis allowed the identification of 221 HCPs among which 158 were reliably quantified for a global amount of 880 ng/mg of NIST mAb Reference Material. Our methods have also been successfully applied to two FDA/EMA approved DPs and allowed digging deeper into the HCP landscape with the identification and quantification of a few tens of HCPs with sensitivity down to the sub-ng/mg of mAb level.
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Affiliation(s)
- Corentin Beaumal
- Laboratoire de Spectrométrie de Masse BioOrganique, IPHC UMR 7178, CNRS, Université de Strasbourg, Strasbourg, France
- Infrastructure Nationale de Protéomique ProFI - FR2048, Strasbourg, France
| | - Alain Beck
- IRPF, Centre d'Immunologie Pierre-Fabre (CIPF), Saint-Julien-en-Genevois, France
| | - Oscar Hernandez-Alba
- Laboratoire de Spectrométrie de Masse BioOrganique, IPHC UMR 7178, CNRS, Université de Strasbourg, Strasbourg, France
- Infrastructure Nationale de Protéomique ProFI - FR2048, Strasbourg, France
| | - Christine Carapito
- Laboratoire de Spectrométrie de Masse BioOrganique, IPHC UMR 7178, CNRS, Université de Strasbourg, Strasbourg, France
- Infrastructure Nationale de Protéomique ProFI - FR2048, Strasbourg, France
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16
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Lu S, Lu H, Zheng T, Yuan H, Du H, Gao Y, Liu Y, Pan X, Zhang W, Fu S, Sun Z, Jin J, He QY, Chen Y, Zhang G. A multi-omics dataset of human transcriptome and proteome stable reference. Sci Data 2023; 10:455. [PMID: 37443183 PMCID: PMC10344951 DOI: 10.1038/s41597-023-02359-w] [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: 01/31/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Abstract
The development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits their application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. The transcriptome and proteome of most cell lines shift during culturing, which limits their applicability as standard samples. In this study, we demonstrated that the human hepatocellular cell line MHCC97H has a very stable transcriptome (r = 0.983~0.997) and proteome (r = 0.966~0.988 for data-dependent acquisition, r = 0.970~0.994 for data-independent acquisition) after 9 subculturing generations, which allows this steady standard sample to be consistently produced on an industrial scale in long term. Moreover, this stability was maintained across labs and platforms. In sum, our study provides omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine.
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Affiliation(s)
- Shaohua Lu
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China.
- Sino-French Hoffmann Institute, School of Basic Medical Sciences, State Key Laboratory of Respiratory Disease, Guangzhou Medical University, Guangzhou, China.
| | - Hong Lu
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China
| | - Tingkai Zheng
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China
| | - Huiming Yuan
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian, China
| | - Hongli Du
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Youhe Gao
- Department of Biochemistry and Molecular Biology, Beijing Key Laboratory of Gene Engineering Drug and Biotechnology, Beijing Normal University, Beijing, China
| | - Yongtao Liu
- Department of Biochemistry and Molecular Biology, Beijing Key Laboratory of Gene Engineering Drug and Biotechnology, Beijing Normal University, Beijing, China
| | - Xuanzhen Pan
- Department of Biochemistry and Molecular Biology, Beijing Key Laboratory of Gene Engineering Drug and Biotechnology, Beijing Normal University, Beijing, China
| | - Wenlu Zhang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Shuying Fu
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Zhenghua Sun
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China
| | - Jingjie Jin
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China
| | - Qing-Yu He
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China
| | - Yang Chen
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China.
| | - Gong Zhang
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China.
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17
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Yu F, Teo GC, Kong AT, Fröhlich K, Li GX, Demichev V, Nesvizhskii AI. Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform. Nat Commun 2023; 14:4154. [PMID: 37438352 PMCID: PMC10338508 DOI: 10.1038/s41467-023-39869-5] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 06/28/2023] [Indexed: 07/14/2023] Open
Abstract
Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.
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Affiliation(s)
- Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
| | - Guo Ci Teo
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Andy T Kong
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Klemens Fröhlich
- Proteomics Core Facility, Biozentrum, University of Basel, Basel, Switzerland
| | - Ginny Xiaohe Li
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Vadim Demichev
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
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18
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Souza Junior DR, Silva ARM, Ronsein GE. Strategies for consistent and automated quantification of HDL proteome using data-independent acquisition (DIA). J Lipid Res 2023:100397. [PMID: 37286042 PMCID: PMC10339053 DOI: 10.1016/j.jlr.2023.100397] [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: 01/31/2023] [Revised: 05/11/2023] [Accepted: 05/31/2023] [Indexed: 06/09/2023] Open
Abstract
The introduction of mass spectrometry-based proteomics has revolutionized HDL field, with the description, characterization and implication of HDL-associated proteins in an array of pathologies. However, acquiring robust, reproducible data is still a challenge in the quantitative assessment of HDL proteome. Data-independent acquisition (DIA) is a mass spectrometry methodology that allows the acquisition of reproducible data, but data analysis remains a challenge in the field. Up to date, there is no consensus in how to process DIA-derived data for HDL proteomics. Here, we developed a pipeline aiming to standardize HDL proteome quantification. We optimized instrument parameters, and compared the performance of four freely available, user-friendly software tools (DIA-NN, EncyclopeDIA, MaxDIA and Skyline) in processing DIA data. Importantly, pooled samples were used as quality controls throughout our experimental setup. A carefully evaluation of precision, linearity, and detection limits, first using E. coli background for HDL proteomics, and second using HDL proteome and synthetic peptides, was undertaken. Finally, as a proof of concept, we employed our optimized and automated pipeline to quantify the proteome of HDL and apolipoprotein B (APOB)-containing lipoproteins. Our results show that determination of precision is key to confidently and consistently quantify HDL proteins. Taking this precaution, any of the available software tested here would be appropriate for quantification of HDL proteome, although their performance varied considerably.
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Affiliation(s)
| | | | - Graziella Eliza Ronsein
- Department of Biochemistry, Institute of Chemistry, University of São Paulo, São Paulo, Brazil.
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19
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Zhao J, Yang Y, Teng M, Zheng J, Wang B, Mallawaarachchi V, Lin Y, Fang Z, Shen C, Yu S, Yang F, Qiao L, Wang L. Metaproteomics profiling of the microbial communities in fermentation starters ( Daqu) during multi-round production of Chinese liquor. Front Nutr 2023; 10:1139836. [PMID: 37324728 PMCID: PMC10267310 DOI: 10.3389/fnut.2023.1139836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 05/09/2023] [Indexed: 06/17/2023] Open
Abstract
Introduction The special flavor and fragrance of Chinese liquor are closely related to microorganisms in the fermentation starter Daqu. The changes of microbial community can affect the stability of liquor yield and quality. Methods In this study, we used data-independent acquisition mass spectrometry (DIA-MS) for cohort study of the microbial communities of a total of 42 Daqu samples in six production cycles at different times of a year. The DIA MS data were searched against a protein database constructed by metagenomic sequencing. Results The microbial composition and its changes across production cycles were revealed. Functional analysis of the differential proteins was carried out and the metabolic pathways related to the differential proteins were explored. These metabolic pathways were related to the saccharification process in liquor fermentation and the synthesis of secondary metabolites to form the unique flavor and aroma in the Chinese liquor. Discussion We expect that the metaproteome profiling of Daqu from different production cycles will serve as a guide for the control of fermentation process of Chinese liquor in the future.
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Affiliation(s)
- Jinzhi Zhao
- Kweichow Moutai Group, Renhuai, Guizhou, China
- Department of Chemistry, Fudan University, Shanghai, China
| | - Yi Yang
- Department of Chemistry, Fudan University, Shanghai, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, China
| | | | | | - Bing Wang
- College of Food Science and Technology, Shanghai Ocean University, Shanghai, China
| | - Vijini Mallawaarachchi
- College of Engineering and Computer Science, The Australian National University, Canberra, ACT, Australia
- Flinders Accelerator for Microbiome Exploration, Flinders University, Bedford Park, SA, Australia
| | - Yu Lin
- College of Engineering and Computer Science, The Australian National University, Canberra, ACT, Australia
| | - Ziyu Fang
- Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY, United States
| | | | - Shaoning Yu
- Zhejiang Provincial Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang, China
| | - Fan Yang
- Kweichow Moutai Group, Renhuai, Guizhou, China
| | - Liang Qiao
- Department of Chemistry, Fudan University, Shanghai, China
| | - Li Wang
- Kweichow Moutai Group, Renhuai, Guizhou, China
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20
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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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21
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Woo J, Zhang Q. A Streamlined High-Throughput Plasma Proteomics Platform for Clinical Proteomics with Improved Proteome Coverage, Reproducibility, and Robustness. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:754-762. [PMID: 36975161 PMCID: PMC10080683 DOI: 10.1021/jasms.3c00022] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 06/18/2023]
Abstract
Mass spectrometry-based clinical proteomics requires high throughput, reproducibility, robustness, and comprehensive coverage to serve the needs of clinical diagnosis, prognosis, and personalized medicine. Oftentimes these requirements are contradictory to each other. We report the development of a streamlined High-Throughput Plasma Proteomics (sHTPP) platform for untargeted profiling of the blood plasma proteome, which includes 96-well plates and simplified procedures for sample preparation, disposable trap column for peptide loading, robust liquid chromatographic system for separation, data-independent acquisition in tandem mass spectrometry, and DIA-NN, FragPipe, and in-house peptide spectral library-based data analysis. Using the optimized platform at a throughput of 60 samples per day, over 600 protein groups including 57 FDA-approved biomarkers can be consistently identified from whole human plasma, and more than 85% of the detected proteins have 100% completeness in quantitative values across 300 samples. The balance achieved between proteome coverage, throughput, and reproducibility of this sHTPP platform makes it promising in clinical settings, where a large number of samples are to be measured quickly and reliably to support various needs of clinical medicine.
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Affiliation(s)
- Jongmin Woo
- Center
for Translational Biomedical Research, University
of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, North Carolina 28081, United States
| | - Qibin Zhang
- Center
for Translational Biomedical Research, University
of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, North Carolina 28081, United States
- Department
of Chemistry & Biochemistry, University
of North Carolina at Greensboro, Greensboro, North Carolina 27402, United States
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22
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Bons J, Pan D, Shah S, Bai R, Chen‐Tanyolac C, Wang X, Elliott DRF, Urisman A, O'Broin A, Basisty N, Rose J, Sangwan V, Camilleri‐Broët S, Tankel J, Gascard P, Ferri L, Tlsty TD, Schilling B. Data-independent acquisition and quantification of extracellular matrix from human lung in chronic inflammation-associated carcinomas. Proteomics 2023; 23:e2200021. [PMID: 36228107 PMCID: PMC10391693 DOI: 10.1002/pmic.202200021] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/17/2022] [Accepted: 09/21/2022] [Indexed: 11/06/2022]
Abstract
Early events associated with chronic inflammation and cancer involve significant remodeling of the extracellular matrix (ECM), which greatly affects its composition and functional properties. Using lung squamous cell carcinoma (LSCC), a chronic inflammation-associated cancer (CIAC), we optimized a robust proteomic pipeline to discover potential biomarker signatures and protein changes specifically in the stroma. We combined ECM enrichment from fresh human tissues, data-independent acquisition (DIA) strategies, and stringent statistical processing to analyze "Tumor" and matched adjacent histologically normal ("Matched Normal") tissues from patients with LSCC. Overall, 1802 protein groups were quantified with at least two unique peptides, and 56% of those proteins were annotated as "extracellular." Confirming dramatic ECM remodeling during CIAC progression, 529 proteins were significantly altered in the "Tumor" compared to "Matched Normal" tissues. The signature was typified by a coordinated loss of basement membrane proteins and small leucine-rich proteins. The dramatic increase in the stromal levels of SERPINH1/heat shock protein 47, that was discovered using our ECM proteomic pipeline, was validated by immunohistochemistry (IHC) of "Tumor" and "Matched Normal" tissues, obtained from an independent cohort of LSCC patients. This integrated workflow provided novel insights into ECM remodeling during CIAC progression, and identified potential biomarker signatures and future therapeutic targets.
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Affiliation(s)
- Joanna Bons
- Buck Institute for Research on AgingNovatoCaliforniaUSA
| | - Deng Pan
- Department of PathologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Samah Shah
- Buck Institute for Research on AgingNovatoCaliforniaUSA
| | - Rosemary Bai
- Department of PathologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | | | - Xianhong Wang
- Department of PathologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Daffolyn R. Fels Elliott
- Department of PathologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Present address:
Pathology and Laboratory MedicineKansas University Medical Center, the University of KansasKansas CityKansasUSA
| | - Anatoly Urisman
- Department of PathologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Amy O'Broin
- Buck Institute for Research on AgingNovatoCaliforniaUSA
| | | | - Jacob Rose
- Buck Institute for Research on AgingNovatoCaliforniaUSA
| | - Veena Sangwan
- Division of Thoracic and Upper Gastrointestinal SurgeryMontreal General HospitalMcGill University Health CentreMontrealQuebecCanada
| | | | - James Tankel
- Division of Thoracic and Upper Gastrointestinal SurgeryMontreal General HospitalMcGill University Health CentreMontrealQuebecCanada
| | - Philippe Gascard
- Department of PathologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Lorenzo Ferri
- Division of Thoracic and Upper Gastrointestinal SurgeryMontreal General HospitalMcGill University Health CentreMontrealQuebecCanada
| | - Thea D. Tlsty
- Department of PathologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
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23
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Goff JL, Chen Y, Thorgersen MP, Hoang LT, Poole FL, Szink EG, Siuzdak G, Petzold CJ, Adams MWW. Mixed heavy metal stress induces global iron starvation response. THE ISME JOURNAL 2023; 17:382-392. [PMID: 36572723 PMCID: PMC9938188 DOI: 10.1038/s41396-022-01351-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 12/08/2022] [Accepted: 12/13/2022] [Indexed: 12/28/2022]
Abstract
Multiple heavy metal contamination is an increasingly common global problem. Heavy metals have the potential to disrupt microbially mediated biogeochemical cycling. However, systems-level studies on the effects of combinations of heavy metals on bacteria are lacking. For this study, we focused on the Oak Ridge Reservation (ORR; Oak Ridge, TN, USA) subsurface which is contaminated with several heavy metals and high concentrations of nitrate. Using a native Bacillus cereus isolate that represents a dominant species at this site, we assessed the combined impact of eight metal contaminants, all at site-relevant concentrations, on cell processes through an integrated multi-omics approach that included discovery proteomics, targeted metabolomics, and targeted gene-expression profiling. The combination of eight metals impacted cell physiology in a manner that could not have been predicted from summing phenotypic responses to the individual metals. Exposure to the metal mixture elicited a global iron starvation response not observed during individual metal exposures. This disruption of iron homeostasis resulted in decreased activity of the iron-cofactor-containing nitrate and nitrite reductases, both of which are important in biological nitrate removal at the site. We propose that the combinatorial effects of simultaneous exposure to multiple heavy metals is an underappreciated yet significant form of cell stress in the environment with the potential to disrupt global nutrient cycles and to impede bioremediation efforts at mixed waste sites. Our work underscores the need to shift from single- to multi-metal studies for assessing and predicting the impacts of complex contaminants on microbial systems.
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Affiliation(s)
- Jennifer L. Goff
- grid.213876.90000 0004 1936 738XDepartment of Biochemistry and Molecular Biology, University of Georgia, Athens, GA USA
| | - Yan Chen
- grid.184769.50000 0001 2231 4551Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA USA
| | - Michael P. Thorgersen
- grid.213876.90000 0004 1936 738XDepartment of Biochemistry and Molecular Biology, University of Georgia, Athens, GA USA
| | - Linh T. Hoang
- grid.214007.00000000122199231Scripps Center for Metabolomics, Scripps Research, La Jolla, CA USA
| | - Farris L. Poole
- grid.213876.90000 0004 1936 738XDepartment of Biochemistry and Molecular Biology, University of Georgia, Athens, GA USA
| | - Elizabeth G. Szink
- grid.213876.90000 0004 1936 738XDepartment of Biochemistry and Molecular Biology, University of Georgia, Athens, GA USA
| | - Gary Siuzdak
- grid.214007.00000000122199231Scripps Center for Metabolomics, Scripps Research, La Jolla, CA USA
| | - Christopher J. Petzold
- grid.184769.50000 0001 2231 4551Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA USA
| | - Michael W. W. Adams
- grid.213876.90000 0004 1936 738XDepartment of Biochemistry and Molecular Biology, University of Georgia, Athens, GA USA
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24
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Proteomics as a New-Generation Tool for Studying Moulds Related to Food Safety and Quality. Int J Mol Sci 2023; 24:ijms24054709. [PMID: 36902140 PMCID: PMC10003330 DOI: 10.3390/ijms24054709] [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: 01/31/2023] [Revised: 02/24/2023] [Accepted: 02/26/2023] [Indexed: 03/06/2023] Open
Abstract
Mould development in foodstuffs is linked to both spoilage and the production of mycotoxins, provoking food quality and food safety concerns, respectively. The high-throughput technology proteomics applied to foodborne moulds is of great interest to address such issues. This review presents proteomics approaches useful for boosting strategies to minimise the mould spoilage and the hazard related to mycotoxins in food. Metaproteomics seems to be the most effective method for mould identification despite the current problems related to the bioinformatics tool. More interestingly, different high resolution mass spectrometry tools are suitable for evaluating the proteome of foodborne moulds able to unveil the mould's response under certain environmental conditions and the presence of biocontrol agents or antifungals, being sometimes combined with a method with limited ability to separate proteins, the two-dimensional gel electrophoresis. However, the matrix complexity, the high ranges of protein concentrations needed and the performing of multiple steps are some of the proteomics limitations for the application to foodborne moulds. To overcome some of these limitations, model systems have been developed and proteomics applied to other scientific fields, such as library-free data independent acquisition analyses, the implementation of ion mobility, and the evaluation of post-translational modifications, are expected to be gradually implemented in this field for avoiding undesirable moulds in foodstuffs.
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25
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Bons J, Rose J, Zhang R, Burton JB, Carrico C, Verdin E, Schilling B. In-depth analysis of the Sirtuin 5-regulated mouse brain malonylome and succinylome using library-free data-independent acquisitions. Proteomics 2023; 23:e2100371. [PMID: 36479818 PMCID: PMC10363399 DOI: 10.1002/pmic.202100371] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 10/29/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022]
Abstract
Post-translational modifications (PTMs) dynamically regulate proteins and biological pathways, typically through the combined effects of multiple PTMs. Lysine residues are targeted for various PTMs, including malonylation and succinylation. However, PTMs offer specific challenges to mass spectrometry-based proteomics during data acquisition and processing. Thus, novel and innovative workflows using data-independent acquisition (DIA) ensure confident PTM identification, precise site localization, and accurate and robust label-free quantification. In this study, we present a powerful approach that combines antibody-based enrichment with comprehensive DIA acquisitions and spectral library-free data processing using directDIA (Spectronaut). Identical DIA data can be used to generate spectral libraries and comprehensively identify and quantify PTMs, reducing the amount of enriched sample and acquisition time needed, while offering a fully automated workflow. We analyzed brains from wild-type and Sirtuin 5 (SIRT5)-knock-out mice, and discovered and quantified 466 malonylated and 2211 succinylated peptides. SIRT5 regulation remodeled the acylomes by targeting 164 malonylated and 578 succinylated sites. Affected pathways included carbohydrate and lipid metabolisms, synaptic vesicle cycle, and neurodegenerative diseases. We found 48 common SIRT5-regulated malonylation and succinylation sites, suggesting potential PTM crosstalk. This innovative and efficient workflow offers deeper insights into the mouse brain lysine malonylome and succinylome.
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Affiliation(s)
- Joanna Bons
- Buck Institute for Research on Aging, Novato, California, USA
| | - Jacob Rose
- Buck Institute for Research on Aging, Novato, California, USA
| | - Ran Zhang
- Buck Institute for Research on Aging, Novato, California, USA
| | - Jordan B Burton
- Buck Institute for Research on Aging, Novato, California, USA
| | | | - Eric Verdin
- Buck Institute for Research on Aging, Novato, California, USA
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26
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Zhao J, Yang Y, Xu H, Zheng J, Shen C, Chen T, Wang T, Wang B, Yi J, Zhao D, Wu E, Qin Q, Xia L, Qiao L. Data-independent acquisition boosts quantitative metaproteomics for deep characterization of gut microbiota. NPJ Biofilms Microbiomes 2023; 9:4. [PMID: 36693863 PMCID: PMC9873935 DOI: 10.1038/s41522-023-00373-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 01/11/2023] [Indexed: 01/26/2023] Open
Abstract
Metaproteomics can provide valuable insights into the functions of human gut microbiota (GM), but is challenging due to the extreme complexity and heterogeneity of GM. Data-independent acquisition (DIA) mass spectrometry (MS) has been an emerging quantitative technique in conventional proteomics, but is still at the early stage of development in the field of metaproteomics. Herein, we applied library-free DIA (directDIA)-based metaproteomics and compared the directDIA with other MS-based quantification techniques for metaproteomics on simulated microbial communities and feces samples spiked with bacteria with known ratios, demonstrating the superior performance of directDIA by a comprehensive consideration of proteome coverage in identification as well as accuracy and precision in quantification. We characterized human GM in two cohorts of clinical fecal samples of pancreatic cancer (PC) and mild cognitive impairment (MCI). About 70,000 microbial proteins were quantified in each cohort and annotated to profile the taxonomic and functional characteristics of GM in different diseases. Our work demonstrated the utility of directDIA in quantitative metaproteomics for investigating intestinal microbiota and its related disease pathogenesis.
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Affiliation(s)
- Jinzhi Zhao
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
| | - Yi Yang
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311200, Hangzhou, China
| | - Hua Xu
- Department of Core Facility of Basic Medical Sciences, and Department of Psychiatry of Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 200000, Shanghai, China
| | - Jianxujie Zheng
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
| | - Chengpin Shen
- Shanghai Omicsolution Co., Ltd, 201100, Shanghai, China
| | - Tian Chen
- Changhai Hospital, The Naval Military Medical University, 200433, Shanghai, China
| | - Tao Wang
- Department of Core Facility of Basic Medical Sciences, and Department of Psychiatry of Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 200000, Shanghai, China
| | - Bing Wang
- College of Food Science and Technology, Shanghai Ocean University, 201306, Shanghai, China
| | - Jia Yi
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
| | - Dan Zhao
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
| | - Enhui Wu
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
| | - Qin Qin
- Changhai Hospital, The Naval Military Medical University, 200433, Shanghai, China.
| | - Li Xia
- Department of Core Facility of Basic Medical Sciences, and Department of Psychiatry of Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 200000, Shanghai, China.
| | - Liang Qiao
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China.
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27
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Zhao J, Yang Y, Chen L, Zheng J, Lv X, Li D, Fang Z, Shen C, Mallawaarachchi V, Lin Y, Yu S, Yang F, Wang L, Qiao L. Quantitative metaproteomics reveals composition and metabolism characteristics of microbial communities in Chinese liquor fermentation starters. Front Microbiol 2023; 13:1098268. [PMID: 36699582 PMCID: PMC9868298 DOI: 10.3389/fmicb.2022.1098268] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 12/12/2022] [Indexed: 01/11/2023] Open
Abstract
Introduction Daqu, the Chinese liquor fermentation starter, contains complex microbial communities that are important for the yield, quality, and unique flavor of produced liquor. However, the composition and metabolism of microbial communities in the different types of high-temperature Daqu (i.e., white, yellow, and black Daqu) have not been well understood. Methods Herein, we used quantitative metaproteomics based on data-independent acquisition (DIA) mass spectrometry to analyze a total of 90 samples of white, yellow, and black Daqu collected in spring, summer, and autumn, revealing the taxonomic and metabolic profiles of different types of Daqu across seasons. Results Taxonomic composition differences were explored across types of Daqu and seasons, where the under-fermented white Daqu showed the higher microbial diversity and seasonal stability. It was demonstrated that yellow Daqu had higher abundance of saccharifying enzymes for raw material degradation. In addition, considerable seasonal variation of microbial protein abundance was discovered in the over-fermented black Daqu, suggesting elevated carbohydrate and amino acid metabolism in autumn black Daqu. Discussion We expect that this study will facilitate the understanding of the key microbes and their metabolism in the traditional fermentation process of Chinese liquor production.
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Affiliation(s)
- Jinzhi Zhao
- Department of Chemistry and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | - Yi Yang
- Department of Chemistry and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | | | - Jianxujie Zheng
- Department of Chemistry and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | - Xibin Lv
- Kweichow Moutai Group, Renhuai, Guizhou, China
| | - Dandan Li
- Department of Chemistry and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | - Ziyu Fang
- Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY, United States
| | | | - Vijini Mallawaarachchi
- College of Engineering and Computer Science, The Australian National University, Canberra, ACT, Australia
| | - Yu Lin
- College of Engineering and Computer Science, The Australian National University, Canberra, ACT, Australia
| | - Shaoning Yu
- Zhejiang Provincial Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo, China
| | - Fan Yang
- Kweichow Moutai Group, Renhuai, Guizhou, China
| | - Li Wang
- Kweichow Moutai Group, Renhuai, Guizhou, China
| | - Liang Qiao
- Department of Chemistry and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
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28
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Lou R, Cao Y, Li S, Lang X, Li Y, Zhang Y, Shui W. Benchmarking commonly used software suites and analysis workflows for DIA proteomics and phosphoproteomics. Nat Commun 2023; 14:94. [PMID: 36609502 PMCID: PMC9822986 DOI: 10.1038/s41467-022-35740-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 12/20/2022] [Indexed: 01/07/2023] Open
Abstract
A plethora of software suites and multiple classes of spectral libraries have been developed to enhance the depth and robustness of data-independent acquisition (DIA) data processing. However, how the combination of a DIA software tool and a spectral library impacts the outcome of DIA proteomics and phosphoproteomics data analysis has been rarely investigated using benchmark data that mimics biological complexity. In this study, we create DIA benchmark data sets simulating the regulation of thousands of proteins in a complex background, which are collected on both an Orbitrap and a timsTOF instruments. We evaluate four commonly used software suites (DIA-NN, Spectronaut, MaxDIA and Skyline) combined with seven different spectral libraries in global proteome analysis. Moreover, we assess their performances in analyzing phosphopeptide standards and TNF-α-induced phosphoproteome regulation. Our study provides a practical guidance on how to construct a robust data analysis pipeline for different proteomics studies implementing the DIA technique.
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Affiliation(s)
- Ronghui Lou
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ye Cao
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China
| | - Shanshan Li
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
| | - Xiaoyu Lang
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yunxia Li
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China
| | - Yaoyang Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China.
- Shanghai Key Laboratory of Aging Studies, 100 Haike Rd., Shanghai, 201210, China.
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China.
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
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29
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Leeming MG, Ang CS, Nie S, Varshney S, Williamson NA. Simulation of mass spectrometry-based proteomics data with Synthedia. BIOINFORMATICS ADVANCES 2022; 3:vbac096. [PMID: 36698761 PMCID: PMC9825309 DOI: 10.1093/bioadv/vbac096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 11/08/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022]
Abstract
Motivation A large number of experimental and bioinformatic parameters must be set to identify and quantify peptides in mass spectrometry experiments and each of these will impact the results. An ability to simulate raw data with known contents would allow researchers to rapidly explore the effects of varying experimental parameters and systematically investigate downstream processing software. A range of data simulators are available for established data-dependent acquisition methodologies, but these do not extend to the rapidly developing field of data-independent acquisition (DIA) strategies. Results Here, we present Synthedia-a software package to simulate DIA liquid chromatography-mass spectrometry for bottom-up proteomics experiments. Synthedia can generate datasets with known peptide precursor ions and fragments and allows for the customization of a wide variety of chromatographic and mass spectrometry parameters. Availability and implementation Synthedia is freely available via the internet and can be used through a graphical website (https://synthedia.org/) or locally via the command line (https://github.com/mgleeming/synthedia/). Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
| | - Ching-Seng Ang
- Bio21 Molecular Science & Biotechnology Institute, Melbourne Mass Spectrometry and Proteomics Facility, The University of Melbourne, Melbourne, VIC 3052, Australia
| | - Shuai Nie
- Bio21 Molecular Science & Biotechnology Institute, Melbourne Mass Spectrometry and Proteomics Facility, The University of Melbourne, Melbourne, VIC 3052, Australia
| | - Swati Varshney
- Bio21 Molecular Science & Biotechnology Institute, Melbourne Mass Spectrometry and Proteomics Facility, The University of Melbourne, Melbourne, VIC 3052, Australia
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30
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Xian F, Sondermann JR, Gomez Varela D, Schmidt M. Deep proteome profiling reveals signatures of age and sex differences in paw skin and sciatic nerve of naïve mice. eLife 2022; 11:e81431. [PMID: 36448997 PMCID: PMC9711526 DOI: 10.7554/elife.81431] [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: 06/27/2022] [Accepted: 11/16/2022] [Indexed: 12/03/2022] Open
Abstract
The age and sex of studied animals profoundly impact experimental outcomes in biomedical research. However, most preclinical studies in mice use a wide-spanning age range from 4 to 20 weeks and do not assess male and female mice in parallel. This raises concerns regarding reproducibility and neglects potentially relevant age and sex differences, which are largely unknown at the molecular level in naïve mice. Here, we employed an optimized quantitative proteomics workflow in order to deeply profile mouse paw skin and sciatic nerves (SCN) - two tissues implicated in nociception and pain as well as diseases linked to inflammation, injury, and demyelination. Remarkably, we uncovered significant differences when comparing male and female mice at adolescent (4 weeks) and adult (14 weeks) age. Our analysis deciphered protein subsets and networks that were correlated with the age and/or sex of mice. Notably, among these were proteins/biological pathways with known (patho)physiological relevance, e.g., homeostasis and epidermal signaling in skin, and, in SCN, multiple myelin proteins and regulators of neuronal development. Extensive comparisons with available databases revealed that various proteins associated with distinct skin diseases and pain exhibited significant abundance changes in dependence on age and/or sex. Taken together, our study uncovers hitherto unknown sex and age differences at the level of proteins and protein networks. Overall, we provide a unique proteome resource that facilitates mechanistic insights into somatosensory and skin biology, and integrates age and sex as biological variables - a prerequisite for successful preclinical studies in mouse disease models.
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Affiliation(s)
- Feng Xian
- Systems Biology of Pain, Division of Pharmacology & Toxicology, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of ViennaViennaAustria
| | - Julia Regina Sondermann
- Systems Biology of Pain, Division of Pharmacology & Toxicology, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of ViennaViennaAustria
| | - David Gomez Varela
- Systems Biology of Pain, Division of Pharmacology & Toxicology, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of ViennaViennaAustria
| | - Manuela Schmidt
- Systems Biology of Pain, Division of Pharmacology & Toxicology, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of ViennaViennaAustria
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31
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Mosquim Junior S, Siino V, Rydén L, Vallon-Christersson J, Levander F. Choice of High-Throughput Proteomics Method Affects Data Integration with Transcriptomics and the Potential Use in Biomarker Discovery. Cancers (Basel) 2022; 14:cancers14235761. [PMID: 36497242 PMCID: PMC9736226 DOI: 10.3390/cancers14235761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/18/2022] [Accepted: 11/19/2022] [Indexed: 11/25/2022] Open
Abstract
In recent years, several advances have been achieved in breast cancer (BC) classification and treatment. However, overdiagnosis, overtreatment, and recurrent disease are still significant causes of complication and death. Here, we present the development of a protocol aimed at parallel transcriptome and proteome analysis of BC tissue samples using mass spectrometry, via Data Dependent and Independent Acquisitions (DDA and DIA). Protein digestion was semi-automated and performed on flowthroughs after RNA extraction. Data for 116 samples were acquired in DDA and DIA modes and processed using MaxQuant, EncyclopeDIA, or DIA-NN. DIA-NN showed an increased number of identified proteins, reproducibility, and correlation with matching RNA-seq data, therefore representing the best alternative for this setup. Gene Set Enrichment Analysis pointed towards complementary information being found between transcriptomic and proteomic data. A decision tree model, designed to predict the intrinsic subtypes based on differentially abundant proteins across different conditions, selected protein groups that recapitulate important clinical features, such as estrogen receptor status, HER2 status, proliferation, and aggressiveness. Taken together, our results indicate that the proposed protocol performed well for the application. Additionally, the relevance of the selected proteins points to the possibility of using such data as a biomarker discovery tool for personalized medicine.
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Affiliation(s)
| | - Valentina Siino
- Department of Immunotechnology, Lund University, 223 81 Lund, Sweden
| | - Lisa Rydén
- Division of Surgery, Department of Clinical Sciences Lund, Lund University, 223 81 Lund, Sweden
- Department of Surgery and Gastroenterology, Skåne University Hospital, 214 28 Malmö, Sweden
| | | | - Fredrik Levander
- Department of Immunotechnology, Lund University, 223 81 Lund, Sweden
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Lund University, 223 81 Lund, Sweden
- Correspondence:
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32
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Gagné D, Shajari E, Thibault MP, Noël JF, Boisvert FM, Babakissa C, Levy E, Gagnon H, Brunet MA, Grynspan D, Ferretti E, Bertelle V, Beaulieu JF. Proteomics Profiling of Stool Samples from Preterm Neonates with SWATH/DIA Mass Spectrometry for Predicting Necrotizing Enterocolitis. Int J Mol Sci 2022; 23:ijms231911601. [PMID: 36232903 PMCID: PMC9569884 DOI: 10.3390/ijms231911601] [Citation(s) in RCA: 3] [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: 08/24/2022] [Revised: 09/15/2022] [Accepted: 09/20/2022] [Indexed: 11/05/2022] Open
Abstract
Necrotizing enterocolitis (NEC) is a life-threatening condition for premature infants in neonatal intensive care units. Finding indicators that can predict NEC development before symptoms appear would provide more time to apply targeted interventions. In this study, stools from 132 very-low-birth-weight (VLBW) infants were collected daily in the context of a multi-center prospective study aimed at investigating the potential of fecal biomarkers for NEC prediction using proteomics technology. Eight of the VLBW infants received a stage-3 NEC diagnosis. Stools collected from the NEC infants up to 10 days before their diagnosis were available for seven of them. Their samples were matched with those from seven pairs of non-NEC controls. The samples were processed for liquid chromatography-tandem mass spectrometry analysis using SWATH/DIA acquisition and cross-compatible proteomic software to perform label-free quantification. ROC curve and principal component analyses were used to explore discriminating information and to evaluate candidate protein markers. A series of 36 proteins showed the most efficient capacity with a signature that predicted all seven NEC infants at least a week in advance. Overall, our study demonstrates that multiplexed proteomic signature detection constitutes a promising approach for the early detection of NEC development in premature infants.
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Affiliation(s)
- David Gagné
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Elmira Shajari
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Marie-Pier Thibault
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Jean-François Noël
- PhenoSwitch Bioscience Inc., 975 Rue Léon-Trépanier, Sherbrooke, QC J1G 5J6, Canada
| | - François-Michel Boisvert
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Corentin Babakissa
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Pediatrics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Emile Levy
- Research Center, Centre Hospitalier Universitaire Ste-Justine, Université de Montréal, Montréal, QC H3T 1C5, Canada
| | - Hugo Gagnon
- PhenoSwitch Bioscience Inc., 975 Rue Léon-Trépanier, Sherbrooke, QC J1G 5J6, Canada
| | - Marie A. Brunet
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Pediatrics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - David Grynspan
- Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Colombia, Vancouver, BC V6T 2B5, Canada
| | - Emanuela Ferretti
- Division of Neonatology, Department of Pediatrics, Children’s Hospital of Eastern Ontario (CHEO) and CHEO Research Institute, Ottawa, ON K1H 8L1, Canada
| | - Valérie Bertelle
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Division of Neonatology, Department of Pediatrics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Jean-François Beaulieu
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Correspondence:
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33
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Ma C, Zhang Y, Dou X, Liu L, Zhang W, Ye J. Combining multiple acquisition modes and computational data annotation for structural characterization in traditional Chinese medicine: Miao Nationality medicine Qijiao Shengbai Capsule as a case study. RSC Adv 2022; 12:27781-27792. [PMID: 36320242 PMCID: PMC9520537 DOI: 10.1039/d2ra04720a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/21/2022] [Indexed: 11/26/2022] Open
Abstract
Qijiao Shengbai Capsule (QSC) is a reputable Miao Nationality medicine used for treating leukopenia, but its chemical composition has not yet been elucidated. We herein present a strategy, by integrating multiple data acquisition, computational data annotation and processing methods to visualize and identify the complicated constituents in QSC based on ultra-high-performance liquid chromatography coupled with traveling wave ion mobility quadrupole time-of-flight mass spectrometry (UPLC-TWIMS-QTOF-MS). The multiple data acquisition modes, including data-independent mass spectrometryEnergy (MSE), data-independent high-definition mass spectrometryEnergy (HDMSE), and fast data-dependent acquisition (fast-DDA), in both positive and negative ion modes, were conducted on a Waters-SYNAPT G2-Si mass spectrometer with an ESI source. An in-house library built by the UNIFI platform could efficiently process the peak annotation of known compounds, whilst different structural types were clustered in the molecular networks for the analogous classification and structural annotation of the unknown ones. Neutral loss, diagnostic ions, feature fragmentation behaviors, and community curation of mass spectrometry data of known compounds helped exploit those similar neighboring nodes of unknown compounds. Moreover, by combination of the predicted CCS values from CCS platform with the experimental CCS values from HDMSE, as well as diagnostic fragment ions, isomer compounds were annotated. By integrating reference compound comparison, a total of 202 constituents, including 94 flavonoids, 12 saponins, 30 phthalides, 38 organic acids, 3 amino acids, 7 alkaloids and 18 others, were unambiguously characterized or tentatively identified in QSC. Among them, 5 potential new compounds were detected and 12 pairs of isomers were comprehensively distinguished. Conclusively, the established multiple acquisition modes, computational data processing and analysis strategy proved to be useful for the in-depth structural identification of QSC.
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Affiliation(s)
- Chi Ma
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine Shanghai 201203 China +86 021 81871244
| | - Yuhao Zhang
- School of Traditional Chinese Pharmacy, China Pharmaceutical University Nanjing 211198 China
| | - Xiuxiu Dou
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine Shanghai 201203 China +86 021 81871244
| | - Li Liu
- Guizhou Hanfang Pharmaceutical Co., Ltd. Guizhou 550014 China
| | - Weidong Zhang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine Shanghai 201203 China +86 021 81871244
- School of Pharmacy, Naval Medical University Shanghai 200433 China +86 021 81871248
- School of Traditional Chinese Pharmacy, China Pharmaceutical University Nanjing 211198 China
| | - Ji Ye
- School of Pharmacy, Naval Medical University Shanghai 200433 China +86 021 81871248
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34
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Ivanov MV, Bubis JA, Gorshkov V, Tarasova IA, Levitsky LI, Solovyeva EM, Lipatova AV, Kjeldsen F, Gorshkov MV. DirectMS1Quant: Ultrafast Quantitative Proteomics with MS/MS-Free Mass Spectrometry. Anal Chem 2022; 94:13068-13075. [PMID: 36094425 DOI: 10.1021/acs.analchem.2c02255] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Recently, we presented the DirectMS1 method of ultrafast proteome-wide analysis based on minute-long LC gradients and MS1-only mass spectra acquisition. Currently, the method provides the depth of human cell proteome coverage of 2500 proteins at a 1% false discovery rate (FDR) when using 5 min LC gradients and 7.3 min runtime in total. While the standard MS/MS approaches provide 4000-5000 protein identifications within a couple of hours of instrumentation time, we advocate here that the higher number of identified proteins does not always translate into better quantitation quality of the proteome analysis. To further elaborate on this issue, we performed a one-on-one comparison of quantitation results obtained using DirectMS1 with three popular MS/MS-based quantitation methods: label-free (LFQ) and tandem mass tag quantitation (TMT), both based on data-dependent acquisition (DDA) and data-independent acquisition (DIA). For comparison, we performed a series of proteome-wide analyses of well-characterized (ground truth) and biologically relevant samples, including a mix of UPS1 proteins spiked at different concentrations into an Echerichia coli digest used as a background and a set of glioblastoma cell lines. MS1-only data was analyzed using a novel quantitation workflow called DirectMS1Quant developed in this work. The results obtained in this study demonstrated comparable quantitation efficiency of 5 min DirectMS1 with both TMT and DIA methods, yet the latter two utilized a 10-20-fold longer instrumentation time.
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Affiliation(s)
- Mark V Ivanov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Julia A Bubis
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Vladimir Gorshkov
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, DK-5230 Odense M, Denmark
| | - Irina A Tarasova
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Lev I Levitsky
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Elizaveta M Solovyeva
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Anastasiya V Lipatova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Frank Kjeldsen
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, DK-5230 Odense M, Denmark
| | - Mikhail V Gorshkov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
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35
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Jones AR, Deutsch EW, Vizcaíno JA. Is DIA proteomics data FAIR? Current data sharing practices, available bioinformatics infrastructure and recommendations for the future. Proteomics 2022; 23:e2200014. [PMID: 36074795 PMCID: PMC10155627 DOI: 10.1002/pmic.202200014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/27/2022] [Accepted: 08/29/2022] [Indexed: 11/06/2022]
Abstract
Data independent acquisition (DIA) proteomics techniques have matured enormously in recent years, thanks to multiple technical developments in e.g. instrumentation and data analysis approaches. However, there are many improvements that are still possible for DIA data in the area of the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include more tailored data sharing practices and open data standards, since public databases and data standards for proteomics were mostly designed with DDA data in mind. Here we first describe the current state of the art in the context of FAIR data for proteomics in general, and for DIA approaches in particular. For improving the current situation for DIA data, we make the following recommendations for the future: (i) development of an open data standard for spectral libraries; (ii) make mandatory the availability of the spectral libraries used in DIA experiments in ProteomeXchange resources; (iii) improve the support for DIA data in the data standards developed by the Proteomics Standards Initiative; and (iv) improve the support for DIA datasets in ProteomeXchange resources, including more tailored metadata requirements. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Andrew R Jones
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 3BX, UK
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington, 98109, USA
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
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36
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Boys EL, Liu J, Robinson PJ, Reddel RR. Clinical applications of mass spectrometry-based proteomics in cancer: where are we? Proteomics 2022; 23:e2200238. [PMID: 35968695 DOI: 10.1002/pmic.202200238] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/07/2022] [Accepted: 08/09/2022] [Indexed: 11/12/2022]
Abstract
Tumor tissue processing methodologies in combination with data-independent acquisition mass spectrometry (DIA-MS) have emerged that can comprehensively analyze the proteome of multiple tumor samples accurately and reproducibly. Increasing recognition and adoption of these technologies has resulted in a tranche of studies providing novel insights into cancer classification systems, functional tumor biology, cancer biomarkers, treatment response and drug targets. Despite this, with some limited exceptions, MS-based proteomics has not yet been implemented in routine cancer clinical practice. Here, we summarize the use of DIA-MS in studies that may pave the way for future clinical cancer applications, and highlight the role of alternative MS technologies and multi-omic strategies. We discuss limitations and challenges of studies in this field to date and propose steps for integrating proteomic data into the cancer clinic. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Emma L Boys
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Jia Liu
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia.,The Kinghorn Cancer Centre, St Vincent's Hospital, Darlinghurst, NSW, Australia.,School of Clinical Medicine, St Vincent's Campus, University of New South Wales, Sydney, NSW, Australia
| | - Phillip J Robinson
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Roger R Reddel
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
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37
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Walzer M, García-Seisdedos D, Prakash A, Brack P, Crowther P, Graham RL, George N, Mohammed S, Moreno P, Papatheodorou I, Hubbard SJ, Vizcaíno JA. Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas. Sci Data 2022; 9:335. [PMID: 35701420 PMCID: PMC9197839 DOI: 10.1038/s41597-022-01380-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 05/12/2022] [Indexed: 11/14/2022] Open
Abstract
The number of mass spectrometry (MS)-based proteomics datasets in the public domain keeps increasing, particularly those generated by Data Independent Acquisition (DIA) approaches such as SWATH-MS. Unlike Data Dependent Acquisition datasets, the re-use of DIA datasets has been rather limited to date, despite its high potential, due to the technical challenges involved. We introduce a (re-)analysis pipeline for public SWATH-MS datasets which includes a combination of metadata annotation protocols, automated workflows for MS data analysis, statistical analysis, and the integration of the results into the Expression Atlas resource. Automation is orchestrated with Nextflow, using containerised open analysis software tools, rendering the pipeline readily available and reproducible. To demonstrate its utility, we reanalysed 10 public DIA datasets from the PRIDE database, comprising 1,278 SWATH-MS runs. The robustness of the analysis was evaluated, and the results compared to those obtained in the original publications. The final expression values were integrated into Expression Atlas, making SWATH-MS experiments more widely available and combining them with expression data originating from other proteomics and transcriptomics datasets.
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Affiliation(s)
- Mathias Walzer
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom.
| | - David García-Seisdedos
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Ananth Prakash
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Paul Brack
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Oxford Road, Manchester, M13 9PT, United Kingdom
| | - Peter Crowther
- Melandra Limited, 16 Brook Road, Urmston, Manchester, M41 5RY, United Kingdom
| | - Robert L Graham
- School of Biological Sciences, Chlorine Gardens, Queen's University Belfast, Belfast, BT9 5DL, United Kingdom
| | - Nancy George
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Suhaib Mohammed
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Pablo Moreno
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Irene Papatheodorou
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Simon J Hubbard
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Oxford Road, Manchester, M13 9PT, United Kingdom
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom.
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38
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Fröhlich K, Brombacher E, Fahrner M, Vogele D, Kook L, Pinter N, Bronsert P, Timme-Bronsert S, Schmidt A, Bärenfaller K, Kreutz C, Schilling O. Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity. Nat Commun 2022; 13:2622. [PMID: 35551187 PMCID: PMC9098472 DOI: 10.1038/s41467-022-30094-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 04/14/2022] [Indexed: 12/25/2022] Open
Abstract
Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best. Data independent acquisition (DIA) has been gaining momentum in clinical proteomics. Here, the authors create a benchmark dataset comprising inter-patient heterogeneity to compare popular DIA data analysis workflows for identifying differentially abundant proteins.
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Affiliation(s)
- Klemens Fröhlich
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany.,Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg im Breisgau, Germany
| | - Eva Brombacher
- Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany.,Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg im Breisgau, Germany.,Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Freiburg im Breisgau, Germany
| | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany.,Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg im Breisgau, Germany
| | - Daniel Vogele
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany
| | - Lucas Kook
- Epidemiology, Biostatistics & Prevention Institute, University of Zurich, Zurich, Switzerland.,Institute for Data Analysis and Process Design, Zurich University of Applied Sciences, Winterthur, Switzerland
| | - Niko Pinter
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Peter Bronsert
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Tumorbank Comprehensive Cancer Center Freiburg, Medical Center University of Freiburg, Freiburg im Breisgau, Germany
| | - Sylvia Timme-Bronsert
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Tumorbank Comprehensive Cancer Center Freiburg, Medical Center University of Freiburg, Freiburg im Breisgau, Germany
| | - Alexander Schmidt
- Proteomics Core Facility, Biozentrum, University of Basel, Basel, Switzerland
| | - Katja Bärenfaller
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, and Swiss Institute of Bioinformatics (SIB), Wolfgang, Switzerland
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Freiburg im Breisgau, Germany
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany. .,German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany. .,BIOSS Centre for Biological Signaling Studies, University of Freiburg, Freiburg im Breisgau, Germany.
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39
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Balotf S, Wilson R, Tegg RS, Nichols DS, Wilson CR. Shotgun Proteomics as a Powerful Tool for the Study of the Proteomes of Plants, Their Pathogens, and Plant-Pathogen Interactions. Proteomes 2022; 10:5. [PMID: 35225985 PMCID: PMC8883913 DOI: 10.3390/proteomes10010005] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/12/2022] [Accepted: 01/17/2022] [Indexed: 12/31/2022] Open
Abstract
The interaction between plants and pathogenic microorganisms is a multifaceted process mediated by both plant- and pathogen-derived molecules, including proteins, metabolites, and lipids. Large-scale proteome analysis can quantify the dynamics of proteins, biological pathways, and posttranslational modifications (PTMs) involved in the plant-pathogen interaction. Mass spectrometry (MS)-based proteomics has become the preferred method for characterizing proteins at the proteome and sub-proteome (e.g., the phosphoproteome) levels. MS-based proteomics can reveal changes in the quantitative state of a proteome and provide a foundation for understanding the mechanisms involved in plant-pathogen interactions. This review is intended as a primer for biologists that may be unfamiliar with the diverse range of methodology for MS-based shotgun proteomics, with a focus on techniques that have been used to investigate plant-pathogen interactions. We provide a summary of the essential steps required for shotgun proteomic studies of plants, pathogens and plant-pathogen interactions, including methods for protein digestion, identification, separation, and quantification. Finally, we discuss how protein PTMs may directly participate in the interaction between a pathogen and its host plant.
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Affiliation(s)
- Sadegh Balotf
- New Town Research Laboratories, Tasmanian Institute of Agriculture, University of Tasmania, New Town, TAS 7008, Australia; (S.B.); (R.S.T.)
| | - Richard Wilson
- Central Science Laboratory, University of Tasmania, Hobart, TAS 7001, Australia;
| | - Robert S. Tegg
- New Town Research Laboratories, Tasmanian Institute of Agriculture, University of Tasmania, New Town, TAS 7008, Australia; (S.B.); (R.S.T.)
| | - David S. Nichols
- Central Science Laboratory, University of Tasmania, Hobart, TAS 7001, Australia;
| | - Calum R. Wilson
- New Town Research Laboratories, Tasmanian Institute of Agriculture, University of Tasmania, New Town, TAS 7008, Australia; (S.B.); (R.S.T.)
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40
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Zhou Q, Meng Q, Tan X, Ding W, Ma K, Xu Z, Huang X, Gao H. Protein Phosphorylation Changes During Systemic Acquired Resistance in Arabidopsis thaliana. FRONTIERS IN PLANT SCIENCE 2021; 12:748287. [PMID: 34858456 PMCID: PMC8632492 DOI: 10.3389/fpls.2021.748287] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 10/08/2021] [Indexed: 05/03/2023]
Abstract
Systemic acquired resistance (SAR) in plants is a defense response that provides resistance against a wide range of pathogens at the whole-plant level following primary infection. Although the molecular mechanisms of SAR have been extensively studied in recent years, the role of phosphorylation that occurs in systemic leaves of SAR-induced plants is poorly understood. We used a data-independent acquisition (DIA) phosphoproteomics platform based on high-resolution mass spectrometry in an Arabidopsis thaliana model to identify phosphoproteins related to SAR establishment. A total of 8011 phosphorylation sites from 3234 proteins were identified in systemic leaves of Pseudomonas syringae pv. maculicola ES4326 (Psm ES4326) and mock locally inoculated plants. A total of 859 significantly changed phosphoproteins from 1119 significantly changed phosphopeptides were detected in systemic leaves of Psm ES4326 locally inoculated plants, including numerous transcription factors and kinases. A variety of defense response-related proteins were found to be differentially phosphorylated in systemic leaves of Psm ES4326 locally inoculated leaves, suggesting that these proteins may be functionally involved in SAR through phosphorylation or dephosphorylation. Significantly changed phosphoproteins were enriched mainly in categories related to response to abscisic acid, regulation of stomatal movement, plant-pathogen interaction, MAPK signaling pathway, purine metabolism, photosynthesis-antenna proteins, and flavonoid biosynthesis. A total of 28 proteins were regulated at both protein and phosphorylation levels during SAR. RT-qPCR analysis revealed that changes in phosphorylation levels of proteins during SAR did not result from changes in transcript abundance. This study provides comprehensive details of key phosphoproteins associated with SAR, which will facilitate further research on the molecular mechanisms of SAR.
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Affiliation(s)
- Qingfeng Zhou
- College of Biology and Food, Shangqiu Normal University, Shangqiu, China
| | - Qi Meng
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology, College of Life Sciences, Northwest University, Xi’an, China
| | - Xiaomin Tan
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology, College of Life Sciences, Northwest University, Xi’an, China
| | - Wei Ding
- Shanghai Omicsspace Biotechnology Co., Ltd., Shanghai, China
| | - Kang Ma
- College of Biology and Food, Shangqiu Normal University, Shangqiu, China
| | - Ziqin Xu
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology, College of Life Sciences, Northwest University, Xi’an, China
| | - Xuan Huang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology, College of Life Sciences, Northwest University, Xi’an, China
- *Correspondence: Xuan Huang,
| | - Hang Gao
- College of Biology and Food, Shangqiu Normal University, Shangqiu, China
- Hang Gao,
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