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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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Gupta S, Sing JC, Röst HL. Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignment. Commun Biol 2023; 6:1101. [PMID: 37903988 PMCID: PMC10616189 DOI: 10.1038/s42003-023-05437-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 10/10/2023] [Indexed: 11/01/2023] Open
Abstract
DIA is a mainstream method for quantitative proteomics, but consistent quantification across multiple LC-MS/MS instruments remains a bottleneck in parallelizing data acquisition. One reason for this inconsistency and missing quantification is the retention time shift which current software does not adequately address for runs from multiple sites. We present multirun chromatogram alignment strategies to map peaks across columns, including the traditional reference-based Star method, and two novel approaches: MST and Progressive alignment. These reference-free strategies produce a quantitatively accurate data-matrix, even from heterogeneous multi-column studies. Progressive alignment also generates merged chromatograms from all runs which has not been previously achieved for LC-MS/MS data. First, we demonstrate the effectiveness of multirun alignment strategies on a gold-standard annotated dataset, resulting in a threefold reduction in quantitation error-rate compared to non-aligned DIA results. Subsequently, on a multi-species dataset that DIAlignR effectively controls the quantitative error rate, improves precision in protein measurements, and exhibits conservative peak alignment. We next show that the MST alignment reduces cross-site CV by 50% for highly abundant proteins when applied to a dataset from 11 different LC-MS/MS setups. Finally, the reanalysis of 949 plasma runs with multirun alignment revealed a more than 50% increase in insulin resistance (IR) and respiratory viral infection (RVI) proteins, identifying 11 and 13 proteins respectively, compared to prior analysis without it. The three strategies are implemented in our DIAlignR workflow (>2.3) and can be combined with linear, non-linear, or hybrid pairwise alignment.
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Affiliation(s)
- Shubham Gupta
- Terrence Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Justin C Sing
- Terrence Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Hannes L Röst
- Terrence Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.
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Fahrner M, Föll MC, Grüning BA, Bernt M, Röst H, Schilling O. Democratizing data-independent acquisition proteomics analysis on public cloud infrastructures via the Galaxy framework. Gigascience 2022; 11:giac005. [PMID: 35166338 PMCID: PMC8848309 DOI: 10.1093/gigascience/giac005] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/26/2021] [Accepted: 01/12/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Data-independent acquisition (DIA) has become an important approach in global, mass spectrometric proteomic studies because it provides in-depth insights into the molecular variety of biological systems. However, DIA data analysis remains challenging owing to the high complexity and large data and sample size, which require specialized software and vast computing infrastructures. Most available open-source DIA software necessitates basic programming skills and covers only a fraction of a complete DIA data analysis. In consequence, DIA data analysis often requires usage of multiple software tools and compatibility thereof, severely limiting the usability and reproducibility. FINDINGS To overcome this hurdle, we have integrated a suite of open-source DIA tools in the Galaxy framework for reproducible and version-controlled data processing. The DIA suite includes OpenSwath, PyProphet, diapysef, and swath2stats. We have compiled functional Galaxy pipelines for DIA processing, which provide a web-based graphical user interface to these pre-installed and pre-configured tools for their use on freely accessible, powerful computational resources of the Galaxy framework. This approach also enables seamless sharing workflows with full configuration in addition to sharing raw data and results. We demonstrate the usability of an all-in-one DIA pipeline in Galaxy by the analysis of a spike-in case study dataset. Additionally, extensive training material is provided to further increase access for the proteomics community. CONCLUSION The integration of an open-source DIA analysis suite in the web-based and user-friendly Galaxy framework in combination with extensive training material empowers a broad community of researches to perform reproducible and transparent DIA data analysis.
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Affiliation(s)
- Matthias Fahrner
- Institute for Surgical Pathology, Medical Center–University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Straße 115a, D-79106 Freiburg, Germany
- Faculty of Biology, Albert-Ludwigs-University Freiburg, Schänzlestraße 1, D-79104 Freiburg, Germany
- Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Albertstraße 19A, D-79104, Germany
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Medical Center–University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Straße 115a, D-79106 Freiburg, Germany
- Khoury College of Computer Sciences, Northeastern University, 440 Huntington Ave, Boston, MA 02115, USA
| | - Björn Andreas Grüning
- Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, D-79110 Freiburg, Germany
| | - Matthias Bernt
- Young Investigators Group Bioinformatics and Transcriptomics, Helmholtz Centre for Environmental Research–UFZ, Permoserstraße 15, D-04318 Leipzig, Germany
| | - Hannes Röst
- Donnelly Centre, University of Toronto, 160 College St, Toronto, ON M5S 3E1, Canada
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center–University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Straße 115a, D-79106 Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Hugstetter Straße 55, D-79106 Freiburg, Germany
- BIOSS Centre for Biological Signaling Studies, University of Freiburg, Schänzlestraße 18, D-79104 Freiburg, Germany
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Abstract
Mass spectrometry (MS) is a powerful technique for protein identification, quantification and characterization that is widely applied in biochemical studies, and which can provide data on the quantity, structural integrity and post-translational modifications of proteins. It is therefore a versatile and widely used analytic tool for quality control of biopharmaceuticals, especially in quantifying host-cell protein impurities, identifying post-translation modifications and structural characterization of biopharmaceutical proteins. Here, we summarize recent advances in MS-based analyses of these key quality attributes of the biopharmaceutical development and manufacturing processes.
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