1
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Rojas Echeverri JC, Hause F, Iacobucci C, Ihling CH, Tänzler D, Shulman N, Riffle M, MacLean BX, Sinz A. A Workflow for Improved Analysis of Cross-Linking Mass Spectrometry Data Integrating Parallel Accumulation-Serial Fragmentation with MeroX and Skyline. Anal Chem 2024; 96:7373-7379. [PMID: 38696819 PMCID: PMC11099889 DOI: 10.1021/acs.analchem.4c00829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/24/2024] [Accepted: 04/29/2024] [Indexed: 05/04/2024]
Abstract
Cross-linking mass spectrometry (XL-MS) has evolved into a pivotal technique for probing protein interactions. This study describes the implementation of Parallel Accumulation-Serial Fragmentation (PASEF) on timsTOF instruments, enhancing the detection and analysis of protein interactions by XL-MS. Addressing the challenges in XL-MS, such as the interpretation of complex spectra, low abundant cross-linked peptides, and a data acquisition bias, our current study integrates a peptide-centric approach for the analysis of XL-MS data and presents the foundation for integrating data-independent acquisition (DIA) in XL-MS with a vendor-neutral and open-source platform. A novel workflow is described for processing data-dependent acquisition (DDA) of PASEF-derived information. For this, software by Bruker Daltonics is used, enabling the conversion of these data into a format that is compatible with MeroX and Skyline software tools. Our approach significantly improves the identification of cross-linked products from complex mixtures, allowing the XL-MS community to overcome current analytical limitations.
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Affiliation(s)
- Juan Camilo Rojas Echeverri
- Department
of Pharmaceutical Chemistry and Bioanalytics, Martin-Luther-University Halle-Wittenberg, 06120 Halle, Germany
- Center
for Structural Mass Spectrometry, Martin-Luther-University
Halle-Wittenberg, 06120 Halle, Germany
| | - Frank Hause
- Department
of Pharmaceutical Chemistry and Bioanalytics, Martin-Luther-University Halle-Wittenberg, 06120 Halle, Germany
- Center
for Structural Mass Spectrometry, Martin-Luther-University
Halle-Wittenberg, 06120 Halle, Germany
- Institute
for Molecular Medicine, Martin-Luther-University
Halle-Wittenberg, 06120 Halle, Germany
| | - Claudio Iacobucci
- Department
of Pharmaceutical Chemistry and Bioanalytics, Martin-Luther-University Halle-Wittenberg, 06120 Halle, Germany
- Center
for Structural Mass Spectrometry, Martin-Luther-University
Halle-Wittenberg, 06120 Halle, Germany
- Department
of Physical and Chemical Sciences, University
of L’Aquila, 67100 L’Aquila, Italy
| | - Christian H. Ihling
- Department
of Pharmaceutical Chemistry and Bioanalytics, Martin-Luther-University Halle-Wittenberg, 06120 Halle, Germany
- Center
for Structural Mass Spectrometry, Martin-Luther-University
Halle-Wittenberg, 06120 Halle, Germany
| | - Dirk Tänzler
- Department
of Pharmaceutical Chemistry and Bioanalytics, Martin-Luther-University Halle-Wittenberg, 06120 Halle, Germany
- Center
for Structural Mass Spectrometry, Martin-Luther-University
Halle-Wittenberg, 06120 Halle, Germany
| | - Nicholas Shulman
- Department
of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Michael Riffle
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
| | - Brendan X. MacLean
- Department
of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Andrea Sinz
- Department
of Pharmaceutical Chemistry and Bioanalytics, Martin-Luther-University Halle-Wittenberg, 06120 Halle, Germany
- Center
for Structural Mass Spectrometry, Martin-Luther-University
Halle-Wittenberg, 06120 Halle, Germany
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2
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Tsantilas KA, Merrihew GE, Robbins JE, Johnson RS, Park J, Plubell DL, Huang E, Riffle M, Sharma V, MacLean BX, Eckels J, Wu CC, Bereman MS, Spencer SE, Hoofnagle AN, MacCoss MJ. A framework for quality control in quantitative proteomics. bioRxiv 2024:2024.04.12.589318. [PMID: 38645098 PMCID: PMC11030400 DOI: 10.1101/2024.04.12.589318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share real-world case studies applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis using Skyline, longitudinal QC metrics using AutoQC, and server-based data deposition using PanoramaWeb. We propose that this integrated approach to QC be used as a starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible.
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Affiliation(s)
- Kristine A. Tsantilas
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Gennifer E. Merrihew
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Julia E. Robbins
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Richard S. Johnson
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Jea Park
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Deanna L. Plubell
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Eric Huang
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Michael Riffle
- Department of Biochemistry, University of Washington, Washington 98195, United States
| | - Vagisha Sharma
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Brendan X. MacLean
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Josh Eckels
- LabKey, 500 Union St #1000, Seattle, Washington 98101, United States
| | - Christine C. Wu
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Michael S. Bereman
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27607
| | - Sandra E. Spencer
- Canada’s Michael Smith Genome Sciences Centre (BC Cancer Research Institute), University of British Columbia, Vancouver, British Columbia V5Z 4S6, Canada
| | - Andrew N. Hoofnagle
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington 98195, United States
| | - Michael J. MacCoss
- Department of Genome Sciences, University of Washington, Washington 98195, United States
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3
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Heil L, Damoc E, Arrey TN, Pashkova A, Denisov E, Petzoldt J, Peterson AC, Hsu C, Searle BC, Shulman N, Riffle M, Connolly B, MacLean BX, Remes PM, Senko MW, Stewart HI, Hock C, Makarov AA, Hermanson D, Zabrouskov V, Wu CC, MacCoss MJ. Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data-Independent Acquisition. J Proteome Res 2023; 22:3290-3300. [PMID: 37683181 PMCID: PMC10563156 DOI: 10.1021/acs.jproteome.3c00357] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Indexed: 09/10/2023]
Abstract
We evaluate the quantitative performance of the newly released Asymmetric Track Lossless (Astral) analyzer. Using data-independent acquisition, the Thermo Scientific Orbitrap Astral mass spectrometer quantifies 5 times more peptides per unit time than state-of-the-art Thermo Scientific Orbitrap mass spectrometers, which have long been the gold standard for high-resolution quantitative proteomics. Our results demonstrate that the Orbitrap Astral mass spectrometer can produce high-quality quantitative measurements across a wide dynamic range. We also use a newly developed extracellular vesicle enrichment protocol to reach new depths of coverage in the plasma proteome, quantifying over 5000 plasma proteins in a 60 min gradient with the Orbitrap Astral mass spectrometer.
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Affiliation(s)
- Lilian
R. Heil
- Department
of Genome Sciences, University of Washington, 3720 15th Street NE, Seattle, Washington 98195, United States
| | - Eugen Damoc
- Thermo
Fisher Scientific, Hanna-Kunath
Ste. 11, 28199 Bremen, Germany
| | - Tabiwang N. Arrey
- Thermo
Fisher Scientific, Hanna-Kunath
Ste. 11, 28199 Bremen, Germany
| | - Anna Pashkova
- Thermo
Fisher Scientific, Hanna-Kunath
Ste. 11, 28199 Bremen, Germany
| | - Eduard Denisov
- Thermo
Fisher Scientific, Hanna-Kunath
Ste. 11, 28199 Bremen, Germany
| | - Johannes Petzoldt
- Thermo
Fisher Scientific, Hanna-Kunath
Ste. 11, 28199 Bremen, Germany
| | | | - Chris Hsu
- Department
of Genome Sciences, University of Washington, 3720 15th Street NE, Seattle, Washington 98195, United States
| | - Brian C. Searle
- Pelotonia
Institute for Immuno-Oncology, The Ohio
State University Comprehensive Cancer Center, Columbus, Ohio 43210, United States
- Department
of Biomedical Informatics, The Ohio State
University, Columbus, Ohio 43210, United States
| | - Nicholas Shulman
- Department
of Genome Sciences, University of Washington, 3720 15th Street NE, Seattle, Washington 98195, United States
| | - Michael Riffle
- Department
of Genome Sciences, University of Washington, 3720 15th Street NE, Seattle, Washington 98195, United States
| | - Brian Connolly
- Department
of Genome Sciences, University of Washington, 3720 15th Street NE, Seattle, Washington 98195, United States
| | - Brendan X. MacLean
- Department
of Genome Sciences, University of Washington, 3720 15th Street NE, Seattle, Washington 98195, United States
| | - Philip M. Remes
- Thermo
Fisher Scientific, 355 River Oaks Parkway, San Jose, California 95134, United States
| | - Michael W. Senko
- Thermo
Fisher Scientific, 355 River Oaks Parkway, San Jose, California 95134, United States
| | - Hamish I. Stewart
- Thermo
Fisher Scientific, Hanna-Kunath
Ste. 11, 28199 Bremen, Germany
| | - Christian Hock
- Thermo
Fisher Scientific, Hanna-Kunath
Ste. 11, 28199 Bremen, Germany
| | | | - Daniel Hermanson
- Thermo
Fisher Scientific, 355 River Oaks Parkway, San Jose, California 95134, United States
| | - Vlad Zabrouskov
- Thermo
Fisher Scientific, 355 River Oaks Parkway, San Jose, California 95134, United States
| | - Christine C. Wu
- Department
of Genome Sciences, University of Washington, 3720 15th Street NE, Seattle, Washington 98195, United States
| | - Michael J. MacCoss
- Department
of Genome Sciences, University of Washington, 3720 15th Street NE, Seattle, Washington 98195, United States
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4
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Heil LR, Damoc E, Arrey TN, Pashkova A, Denisov E, Petzoldt J, Peterson AC, Hsu C, Searle BC, Shulman N, Riffle M, Connolly B, MacLean BX, Remes PM, Senko MW, Stewart HI, Hock C, Makarov AA, Hermanson D, Zabrouskov V, Wu CC, MacCoss MJ. Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data Independent Acquisition. bioRxiv 2023:2023.06.03.543570. [PMID: 37398334 PMCID: PMC10312564 DOI: 10.1101/2023.06.03.543570] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
We evaluate the quantitative performance of the newly released Asymmetric Track Lossless (Astral) analyzer. Using data independent acquisition, the Thermo Scientific™ Orbitrap™ Astral™ mass spectrometer quantifies 5 times more peptides per unit time than state-of-the-art Thermo Scientific™ Orbitrap™ mass spectrometers, which have long been the gold standard for high resolution quantitative proteomics. Our results demonstrate that the Orbitrap Astral mass spectrometer can produce high quality quantitative measurements across a wide dynamic range. We also use a newly developed extra-cellular vesicle enrichment protocol to reach new depths of coverage in the plasma proteome, quantifying over 5,000 plasma proteins in a 60-minute gradient with the Orbitrap Astral mass spectrometer.
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Affiliation(s)
- Lilian R. Heil
- Department of Genome Sciences, University of Washington, 3720 15th Street NE, Seattle, Washington 98195, United States
| | - Eugen Damoc
- Thermo Fisher Scientific, Hanna-Kunath Ste. 11, 28199 Bremen, Germany
| | - Tabiwang N. Arrey
- Thermo Fisher Scientific, Hanna-Kunath Ste. 11, 28199 Bremen, Germany
| | - Anna Pashkova
- Thermo Fisher Scientific, Hanna-Kunath Ste. 11, 28199 Bremen, Germany
| | - Eduard Denisov
- Thermo Fisher Scientific, Hanna-Kunath Ste. 11, 28199 Bremen, Germany
| | - Johannes Petzoldt
- Thermo Fisher Scientific, Hanna-Kunath Ste. 11, 28199 Bremen, Germany
| | | | - Chris Hsu
- Department of Genome Sciences, University of Washington, 3720 15th Street NE, Seattle, Washington 98195, United States
| | - Brian C. Searle
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio 43210, United States
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, United States
| | - Nicholas Shulman
- Department of Genome Sciences, University of Washington, 3720 15th Street NE, Seattle, Washington 98195, United States
| | - Michael Riffle
- Department of Genome Sciences, University of Washington, 3720 15th Street NE, Seattle, Washington 98195, United States
| | - Brian Connolly
- Department of Genome Sciences, University of Washington, 3720 15th Street NE, Seattle, Washington 98195, United States
| | - Brendan X. MacLean
- Department of Genome Sciences, University of Washington, 3720 15th Street NE, Seattle, Washington 98195, United States
| | - Philip M. Remes
- Thermo Fisher Scientific, 355 River Oaks Parkway, San Jose, California 95134, United States
| | - Michael W. Senko
- Thermo Fisher Scientific, 355 River Oaks Parkway, San Jose, California 95134, United States
| | - Hamish I. Stewart
- Thermo Fisher Scientific, Hanna-Kunath Ste. 11, 28199 Bremen, Germany
| | - Christian Hock
- Thermo Fisher Scientific, Hanna-Kunath Ste. 11, 28199 Bremen, Germany
| | | | - Daniel Hermanson
- Thermo Fisher Scientific, 355 River Oaks Parkway, San Jose, California 95134, United States
| | - Vlad Zabrouskov
- Thermo Fisher Scientific, 355 River Oaks Parkway, San Jose, California 95134, United States
| | - Christine C. Wu
- Department of Genome Sciences, University of Washington, 3720 15th Street NE, Seattle, Washington 98195, United States
| | - Michael J. MacCoss
- Department of Genome Sciences, University of Washington, 3720 15th Street NE, Seattle, Washington 98195, United States
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5
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Park J, Wilkins C, Avtonomov D, Hong J, Back S, Kim H, Shulman N, MacLean BX, Lee SW, Kim S. Targeted proteomics data interpretation with DeepMRM. Cell Rep Methods 2023; 3:100521. [PMID: 37533638 PMCID: PMC10391571 DOI: 10.1016/j.crmeth.2023.100521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/18/2023] [Accepted: 06/15/2023] [Indexed: 08/04/2023]
Abstract
Targeted proteomics is widely utilized in clinical proteomics; however, researchers often devote substantial time to manual data interpretation, which hinders the transferability, reproducibility, and scalability of this approach. We introduce DeepMRM, a software package based on deep learning algorithms for object detection developed to minimize manual intervention in targeted proteomics data analysis. DeepMRM was evaluated on internal and public datasets, demonstrating superior accuracy compared with the community standard tool Skyline. To promote widespread adoption, we have incorporated a stand-alone graphical user interface for DeepMRM and integrated its algorithm into the Skyline software package as an external tool.
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Affiliation(s)
| | | | | | - Jiwon Hong
- Department of Chemistry, Center for Proteogenomic Research, Korea University, Seoul 02841, Republic of Korea
| | - Seunghoon Back
- Department of Chemistry, Center for Proteogenomic Research, Korea University, Seoul 02841, Republic of Korea
| | - Hokeun Kim
- Department of Chemistry, Center for Proteogenomic Research, Korea University, Seoul 02841, Republic of Korea
| | - Nicholas Shulman
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Brendan X. MacLean
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Sang-Won Lee
- Department of Chemistry, Center for Proteogenomic Research, Korea University, Seoul 02841, Republic of Korea
| | - Sangtae Kim
- Bertis Bioscience, Inc., San Diego, CA 92121, USA
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6
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Kohler D, Staniak M, Tsai TH, Huang T, Shulman N, Bernhardt OM, MacLean BX, Nesvizhskii AI, Reiter L, Sabido E, Choi M, Vitek O. MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale. J Proteome Res 2023; 22:1466-1482. [PMID: 37018319 PMCID: PMC10629259 DOI: 10.1021/acs.jproteome.2c00834] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Indexed: 04/06/2023]
Abstract
The MSstats R-Bioconductor family of packages is widely used for statistical analyses of quantitative bottom-up mass spectrometry-based proteomic experiments to detect differentially abundant proteins. It is applicable to a variety of experimental designs and data acquisition strategies and is compatible with many data processing tools used to identify and quantify spectral features. In the face of ever-increasing complexities of experiments and data processing strategies, the core package of the family, with the same name MSstats, has undergone a series of substantial updates. Its new version MSstats v4.0 improves the usability, versatility, and accuracy of statistical methodology, and the usage of computational resources. New converters integrate the output of upstream processing tools directly with MSstats, requiring less manual work by the user. The package's statistical models have been updated to a more robust workflow. Finally, MSstats' code has been substantially refactored to improve memory use and computation speed. Here we detail these updates, highlighting methodological differences between the new and old versions. An empirical comparison of MSstats v4.0 to its previous implementations, as well as to the packages MSqRob and DEqMS, on controlled mixtures and biological experiments demonstrated a stronger performance and better usability of MSstats v4.0 as compared to existing methods.
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Affiliation(s)
- Devon Kohler
- Khoury College
of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States
| | | | - Tsung-Heng Tsai
- Khoury College
of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States
| | - Ting Huang
- Khoury College
of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States
| | - Nicholas Shulman
- Department
of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | | | - Brendan X. MacLean
- Department
of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Alexey I. Nesvizhskii
- Department
of Pathology and Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States
| | | | - Eduard Sabido
- Center for
Genomic Regulation, Barcelona Institute
of Science and Technology, Barcelona 08003, Spain
- Universitat
Pompeu Fabra, Barcelona 08002, Spain
| | - Meena Choi
- Khoury College
of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States
| | - Olga Vitek
- Khoury College
of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States
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7
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Reiter KH, Zelter A, Janowska MK, Riffle M, Shulman N, MacLean BX, Tamura K, Chambers MC, MacCoss MJ, Davis TN, Guttman M, Brzovic PS, Klevit RE. Cullin-independent recognition of HHARI substrates by a dynamic RBR catalytic domain. Structure 2022; 30:1269-1284.e6. [PMID: 35716664 PMCID: PMC9444911 DOI: 10.1016/j.str.2022.05.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 04/15/2022] [Accepted: 05/24/2022] [Indexed: 11/27/2022]
Abstract
RING-between-RING (RBR) E3 ligases mediate ubiquitin transfer through an obligate E3-ubiquitin thioester intermediate prior to substrate ubiquitination. Although RBRs share a conserved catalytic module, substrate recruitment mechanisms remain enigmatic, and the relevant domains have yet to be identified for any member of the class. Here we characterize the interaction between the auto-inhibited RBR, HHARI (AriH1), and its target protein, 4EHP, using a combination of XL-MS, HDX-MS, NMR, and biochemical studies. The results show that (1) a di-aromatic surface on the catalytic HHARI Rcat domain forms a binding platform for substrates and (2) a phosphomimetic mutation on the auto-inhibitory Ariadne domain of HHARI promotes release and reorientation of Rcat for transthiolation and substrate modification. The findings identify a direct binding interaction between a RING-between-RING ligase and its substrate and suggest a general model for RBR substrate recognition.
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Affiliation(s)
- Katherine H Reiter
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Alex Zelter
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Maria K Janowska
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Michael Riffle
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Nicholas Shulman
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Brendan X MacLean
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Kaipo Tamura
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Matthew C Chambers
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Trisha N Davis
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Miklos Guttman
- Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA
| | - Peter S Brzovic
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Rachel E Klevit
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA.
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8
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Kirkwood KI, Pratt BS, Shulman N, Tamura K, MacCoss MJ, MacLean BX, Baker ES. Utilizing Skyline to analyze lipidomics data containing liquid chromatography, ion mobility spectrometry and mass spectrometry dimensions. Nat Protoc 2022; 17:2415-2430. [PMID: 35831612 DOI: 10.1038/s41596-022-00714-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 04/21/2022] [Indexed: 12/26/2022]
Abstract
Lipidomics studies suffer from analytical and annotation challenges because of the great structural similarity of many of the lipid species. To improve lipid characterization and annotation capabilities beyond those afforded by traditional mass spectrometry (MS)-based methods, multidimensional separation methods such as those integrating liquid chromatography, ion mobility spectrometry, collision-induced dissociation and MS (LC-IMS-CID-MS) may be used. Although LC-IMS-CID-MS and other multidimensional methods offer valuable hydrophobicity, structural and mass information, the files are also complex and difficult to assess. Thus, the development of software tools to rapidly process and facilitate confident lipid annotations is essential. In this Protocol Extension, we use the freely available, vendor-neutral and open-source software Skyline to process and annotate multidimensional lipidomic data. Although Skyline ( https://skyline.ms/skyline.url ) was established for targeted processing of LC-MS-based proteomics data, it has since been extended such that it can be used to analyze small-molecule data as well as data containing the IMS dimension. This protocol uses Skyline's recently expanded capabilities, including small-molecule spectral libraries, indexed retention time and ion mobility filtering, and provides a step-by-step description for importing data, predicting retention times, validating lipid annotations, exporting results and editing our manually validated 500+ lipid library. Although the time required to complete the steps outlined here varies on the basis of multiple factors such as dataset size and familiarity with Skyline, this protocol takes ~5.5 h to complete when annotations are rigorously verified for maximum confidence.
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Affiliation(s)
- Kaylie I Kirkwood
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA
| | - Brian S Pratt
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Nicholas Shulman
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Kaipo Tamura
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Brendan X MacLean
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA. .,Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA.
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9
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Abstract
![]()
Skyline Batch is
a newly developed Windows forms application that
enables the easy and consistent reprocessing of data with Skyline.
Skyline has made previous advances in this direction; however, none
enable seamless automated reprocessing of local and remote files.
Skyline keeps a log of all of the steps that were taken in the document;
however, reproducing these steps takes time and allows room for human
error. Skyline also has a command-line interface, enabling it to be
run from a batch script, but using the program in this way requires
expertise in editing these scripts. By formalizing the workflow of
a highly used set of batch scripts into an intuitive and powerful
user interface, Skyline Batch can reprocess data stored in remote
repositories just by opening and running a Skyline Batch configuration
file. When run, a Skyline Batch configuration downloads all necessary
remote files and then runs a four-step Skyline workflow. By condensing
the steps needed to reprocess the data into one file, Skyline Batch
gives researchers the opportunity to publish their processing along
with their data and other analysis files. These easily run configuration
files will greatly increase the transparency and reproducibility of
published work. Skyline Batch is freely available at https://skyline.ms/batch.url.
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Affiliation(s)
- Alexandra N Marsh
- Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Vagisha Sharma
- Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Surya K Mani
- Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts 02115, United States
| | - Michael J MacCoss
- Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Brendan X MacLean
- Genome Sciences, University of Washington, Seattle, Washington 98195, United States
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10
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Kirkwood KI, Christopher MW, Burgess JL, Littau SR, Foster K, Richey K, Pratt BS, Shulman N, Tamura K, MacCoss MJ, MacLean BX, Baker ES. Development and Application of Multidimensional Lipid Libraries to Investigate Lipidomic Dysregulation Related to Smoke Inhalation Injury Severity. J Proteome Res 2021; 21:232-242. [PMID: 34874736 DOI: 10.1021/acs.jproteome.1c00820] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The implication of lipid dysregulation in diseases, toxic exposure outcomes, and inflammation has brought great interest to lipidomic studies. However, lipids have proven to be analytically challenging due to their highly isomeric nature and vast concentration ranges in biological matrices. Therefore, multidimensional techniques such as those integrating liquid chromatography, ion mobility spectrometry, collision-induced dissociation, and mass spectrometry (LC-IMS-CID-MS) have been implemented to separate lipid isomers as well as provide structural information and increased identification confidence. These data sets are however extremely large and complex, resulting in challenges for data processing and annotation. Here, we have overcome these challenges by developing sample-specific multidimensional lipid libraries using the freely available software Skyline. Specifically, the human plasma library developed for this work contains over 500 unique lipids and is combined with adapted Skyline functions such as indexed retention time (iRT) for retention time prediction and IMS drift time filtering for enhanced selectivity. For comparison with other studies, this database was used to annotate LC-IMS-CID-MS data from a NIST SRM 1950 extract. The same workflow was then utilized to assess plasma and bronchoalveolar lavage fluid (BALF) samples from patients with varying degrees of smoke inhalation injury to identify lipid-based patient prognostic and diagnostic markers.
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Affiliation(s)
- Kaylie I Kirkwood
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Michael W Christopher
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Jefferey L Burgess
- Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona 85721, United States
| | - Sally R Littau
- Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona 85721, United States
| | - Kevin Foster
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Karen Richey
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Brian S Pratt
- Arizona Burn Center, Valleywise Health, Phoenix, Arizona 85008, United States
| | - Nicholas Shulman
- Arizona Burn Center, Valleywise Health, Phoenix, Arizona 85008, United States
| | - Kaipo Tamura
- Arizona Burn Center, Valleywise Health, Phoenix, Arizona 85008, United States
| | - Michael J MacCoss
- Arizona Burn Center, Valleywise Health, Phoenix, Arizona 85008, United States
| | - Brendan X MacLean
- Arizona Burn Center, Valleywise Health, Phoenix, Arizona 85008, United States
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States.,Comparative Medicine Institute, North Carolina State University, Raleigh, North Carolina 27695, United States
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11
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Rohde T, Chupalov R, Shulman N, Sharma V, Eckels J, Pratt BS, MacCoss MJ, MacLean BX. Audit logs to enforce document integrity in Skyline and Panorama. Bioinformatics 2020; 36:4366-4368. [PMID: 32467974 PMCID: PMC7520049 DOI: 10.1093/bioinformatics/btaa547] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 04/27/2020] [Accepted: 05/24/2020] [Indexed: 11/14/2022] Open
Abstract
SUMMARY Skyline is a Windows application for targeted mass spectrometry method creation and quantitative data analysis. Like most graphical user interface (GUI) tools, it has a complex user interface with many ways for users to edit their files which makes the task of logging user actions challenging and is the reason why audit logging of every change is not common in GUI tools. We present an object comparison-based approach to audit logging for Skyline that is extensible to other GUI tools. The new audit logging system keeps track of all document modifications made through the GUI or the command line and displays them in an interactive grid. The audit log can also be uploaded and viewed in Panorama, a web repository for Skyline documents that can be configured to only accept documents with a valid audit log, based on embedded hashes to protect log integrity. This makes workflows involving Skyline and Panorama more reproducible. AVAILABILITY AND IMPLEMENTATION Skyline is freely available at https://skyline.ms. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tobias Rohde
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Rita Chupalov
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Nicholas Shulman
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Vagisha Sharma
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | | | - Brian S Pratt
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Brendan X MacLean
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
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12
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Tsai TH, Choi M, Banfai B, Liu Y, MacLean BX, Dunkley T, Vitek O. Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments. Mol Cell Proteomics 2020; 19:944-959. [PMID: 32234965 PMCID: PMC7261813 DOI: 10.1074/mcp.ra119.001792] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 02/27/2020] [Indexed: 12/11/2022] Open
Abstract
In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein (e.g. precursor ions, transitions or fragments) in a single value per protein per run. When abundances of some features are inconsistent with the overall protein profile (for technological reasons such as interferences, or for biological reasons such as post-translational modifications), the protein-level summaries and the downstream conclusions are undermined. We propose a statistical approach that automatically detects spectral features with such inconsistent patterns. The detected features can be separately investigated, and if necessary, removed from the data set. We evaluated the proposed approach on a series of benchmark-controlled mixtures and biological investigations with DDA, DIA and SRM data acquisitions. The results demonstrated that it could facilitate and complement manual curation of the data. Moreover, it can improve the estimation accuracy, sensitivity and specificity of detecting differentially abundant proteins, and reproducibility of conclusions across different data processing tools. The approach is implemented as an option in the open-source R-based software MSstats.
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Affiliation(s)
- Tsung-Heng Tsai
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts
| | - Meena Choi
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts
| | - Balazs Banfai
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Yansheng Liu
- Department of Pharmacology, Yale Cancer Biology Institute, Yale University School of Medicine, West Haven, Connecticut
| | - Brendan X MacLean
- Department of Genome Sciences, University of Washington, Seattle, Washington
| | - Tom Dunkley
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts.
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13
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Adams KJ, Pratt B, Bose N, Dubois LG, St John-Williams L, Perrott KM, Ky K, Kapahi P, Sharma V, MacCoss MJ, Moseley MA, Colton CA, MacLean BX, Schilling B, Thompson JW. Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics. J Proteome Res 2020; 19:1447-1458. [PMID: 31984744 DOI: 10.1021/acs.jproteome.9b00640] [Citation(s) in RCA: 200] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Vendor-independent software tools for quantification of small molecules and metabolites are lacking, especially for targeted analysis workflows. Skyline is a freely available, open-source software tool for targeted quantitative mass spectrometry method development and data processing with a 10 year history supporting six major instrument vendors. Designed initially for proteomics analysis, we describe the expansion of Skyline to data for small molecule analysis, including selected reaction monitoring, high-resolution mass spectrometry, and calibrated quantification. This fundamental expansion of Skyline from a peptide-sequence-centric tool to a molecule-centric tool makes it agnostic to the source of the molecule while retaining Skyline features critical for workflows in both peptide and more general biomolecular research. The data visualization and interrogation features already available in Skyline, such as peak picking, chromatographic alignment, and transition selection, have been adapted to support small molecule data, including metabolomics. Herein, we explain the conceptual workflow for small molecule analysis using Skyline, demonstrate Skyline performance benchmarked against a comparable instrument vendor software tool, and present additional real-world applications. Further, we include step-by-step instructions on using Skyline for small molecule quantitative method development and data analysis on data acquired with a variety of mass spectrometers from multiple instrument vendors.
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Affiliation(s)
- Kendra J Adams
- Proteomics and Metabolomics Shared Resource, Duke University, Durham, North Carolina 27701, United States.,Department of Neurology, Duke University, Durham, North Carolina 27710, United States
| | - Brian Pratt
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Neelanjan Bose
- Buck Institute for Research on Aging, Novato, California 94945, United States
| | - Laura G Dubois
- Proteomics and Metabolomics Shared Resource, Duke University, Durham, North Carolina 27701, United States
| | - Lisa St John-Williams
- Proteomics and Metabolomics Shared Resource, Duke University, Durham, North Carolina 27701, United States
| | - Kevin M Perrott
- Buck Institute for Research on Aging, Novato, California 94945, United States
| | - Karina Ky
- University of California San Francisco, San Francisco, California 94143, United States
| | - Pankaj Kapahi
- Buck Institute for Research on Aging, Novato, California 94945, United States
| | - Vagisha Sharma
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - M Arthur Moseley
- Proteomics and Metabolomics Shared Resource, Duke University, Durham, North Carolina 27701, United States
| | - Carol A Colton
- Department of Neurology, Duke University, Durham, North Carolina 27710, United States
| | - Brendan X MacLean
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Birgit Schilling
- Buck Institute for Research on Aging, Novato, California 94945, United States
| | - J Will Thompson
- Proteomics and Metabolomics Shared Resource, Duke University, Durham, North Carolina 27701, United States.,Department of Pharmacology and Cancer Biology, Duke University, Durham, North Carolina 27710, United States
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14
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Abstract
Porous graphitized carbon (PGC) based chromatography achieves high-resolution separation of glycan structures released from glycoproteins. This approach is especially valuable when resolving structurally similar isomers and for discovery of novel and/or sample-specific glycan structures. However, the implementation of PGC-based separations in glycomics studies has been limited because system-independent retention values have not been established to normalize technical variation. To address this limitation, this study combined the use of hydrolyzed dextran as an internal standard and Skyline software for post-acquisition normalization to reduce retention time and peak area technical variation in PGC-based glycan analyses. This approach allowed assignment of system-independent retention values that are applicable to typical PGC-based glycan separations and supported the construction of a library containing >300 PGC-separated glycan structures with normalized glucose unit (GU) retention values. To enable the automation of this normalization method, a spectral MS/MS library was developed of the dextran ladder, achieving confident discrimination against isomeric glycans. The utility of this approach is demonstrated in two ways. First, to inform the search space for bioinformatically predicted but unobserved glycan structures, predictive models for two structural modifications, core-fucosylation and bisecting GlcNAc, were developed based on the GU library. Second, the applicability of this method for the analysis of complex biological samples is evidenced by the ability to discriminate between cell culture and tissue sample types by the normalized intensity of N-glycan structures alone. Overall, the methods and data described here are expected to support the future development of more automated approaches to glycan identification and quantitation.
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Affiliation(s)
- Christopher Ashwood
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia. and ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, Sydney, NSW, Australia and Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Brian Pratt
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Brendan X MacLean
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Rebekah L Gundry
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA and Center for Biomedical Mass Spectrometry Research, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Nicolle H Packer
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia. and ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, Sydney, NSW, Australia
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15
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Amodei D, Egertson J, MacLean BX, Johnson R, Merrihew GE, Keller A, Marsh D, Vitek O, Mallick P, MacCoss MJ. Improving Precursor Selectivity in Data-Independent Acquisition Using Overlapping Windows. J Am Soc Mass Spectrom 2019; 30:669-684. [PMID: 30671891 PMCID: PMC6445824 DOI: 10.1007/s13361-018-2122-8] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 12/03/2018] [Accepted: 12/05/2018] [Indexed: 05/22/2023]
Abstract
A major goal of proteomics research is the accurate and sensitive identification and quantification of a broad range of proteins within a sample. Data-independent acquisition (DIA) approaches that acquire MS/MS spectra independently of precursor information have been developed to overcome the reproducibility challenges of data-dependent acquisition and the limited breadth of targeted proteomics strategies. Typical DIA implementations use wide MS/MS isolation windows to acquire comprehensive fragment ion data. However, wide isolation windows produce highly chimeric spectra, limiting the achievable sensitivity and accuracy of quantification and identification. Here, we present a DIA strategy in which spectra are collected with overlapping (rather than adjacent or random) windows and then computationally demultiplexed. This approach improves precursor selectivity by nearly a factor of 2, without incurring any loss in mass range, mass resolution, chromatographic resolution, scan speed, or other key acquisition parameters. We demonstrate a 64% improvement in sensitivity and a 17% improvement in peptides detected in a 6-protein bovine mix spiked into a yeast background. To confirm the method's applicability to a realistic biological experiment, we also analyze the regulation of the proteasome in yeast grown in rapamycin and show that DIA experiments with overlapping windows can help elucidate its adaptation toward the degradation of oxidatively damaged proteins. Our integrated computational and experimental DIA strategy is compatible with any DIA-capable instrument. The computational demultiplexing algorithm required to analyze the data has been made available as part of the open-source proteomics software tools Skyline and msconvert (Proteowizard), making it easy to apply as part of standard proteomics workflows. Graphical Abstract.
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Affiliation(s)
- Dario Amodei
- Department of Radiology, Stanford University, 3155 Porter Drive, Palo Alto, CA USA
| | - Jarrett Egertson
- Department of Genome Sciences, University of Washington, 3720 15th Ave. NE, Seattle, WA USA
| | - Brendan X. MacLean
- Department of Genome Sciences, University of Washington, 3720 15th Ave. NE, Seattle, WA USA
| | - Richard Johnson
- Department of Genome Sciences, University of Washington, 3720 15th Ave. NE, Seattle, WA USA
| | - Gennifer E. Merrihew
- Department of Genome Sciences, University of Washington, 3720 15th Ave. NE, Seattle, WA USA
| | - Austin Keller
- Department of Genome Sciences, University of Washington, 3720 15th Ave. NE, Seattle, WA USA
| | - Don Marsh
- Department of Genome Sciences, University of Washington, 3720 15th Ave. NE, Seattle, WA USA
| | - Olga Vitek
- College of Computer and Information Science, Northeastern University, 440 Huntington Ave, Boston, MA USA
| | - Parag Mallick
- Department of Radiology, Stanford University, 3155 Porter Drive, Palo Alto, CA USA
| | - Michael J. MacCoss
- Department of Genome Sciences, University of Washington, 3720 15th Ave. NE, Seattle, WA USA
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16
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MacLean BX, Pratt BS, Egertson JD, MacCoss MJ, Smith RD, Baker ES. Using Skyline to Analyze Data-Containing Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry Dimensions. J Am Soc Mass Spectrom 2018; 29:2182-2188. [PMID: 30047074 PMCID: PMC6191345 DOI: 10.1007/s13361-018-2028-5] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Revised: 06/19/2018] [Accepted: 06/21/2018] [Indexed: 05/04/2023]
Abstract
Recent advances in ion mobility spectrometry (IMS) have illustrated its power in determining the structural characteristics of a molecule, especially when coupled with other separations dimensions such as liquid chromatography (LC) and mass spectrometry (MS). However, these three separation techniques together greatly complicate data analyses, making better informatics tools essential for assessing the resulting data. In this manuscript, Skyline was adapted to analyze LC-IMS-CID-MS data from numerous instrument vendor datasets and determine the effect of adding the IMS dimension into the normal LC-MS molecular pipeline. For the initial evaluation, a tryptic digest of bovine serum albumin (BSA) was spiked into a yeast protein digest at seven different concentrations, and Skyline was able to rapidly analyze the MS and CID-MS data for 38 of the BSA peptides. Calibration curves for the precursor and fragment ions were assessed with and without the IMS dimension. In all cases, addition of the IMS dimension removed noise from co-eluting peptides with close m/z values, resulting in calibration curves with greater linearity and lower detection limits. This study presents an important informatics development since to date LC-IMS-CID-MS data from the different instrument vendors is often assessed manually and cannot be analyzed quickly. Because these evaluations require days for the analysis of only a few target molecules in a limited number of samples, it is unfeasible to evaluate hundreds of targets in numerous samples. Thus, this study showcases Skyline's ability to work with the multidimensional LC-IMS-CID-MS data and provide biological and environmental insights rapidly. Graphical Abstract ᅟ.
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Affiliation(s)
- Brendan X MacLean
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Brian S Pratt
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Jarrett D Egertson
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd. MSIN K8-98, P.O. Box 999, Richland, WA, 99352, USA
| | - Erin S Baker
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd. MSIN K8-98, P.O. Box 999, Richland, WA, 99352, USA.
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17
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Litichevskiy L, Peckner R, Abelin JG, Asiedu JK, Creech AL, Davis JF, Davison D, Dunning CM, Egertson JD, Egri S, Gould J, Ko T, Johnson SA, Lahr DL, Lam D, Liu Z, Lyons NJ, Lu X, MacLean BX, Mungenast AE, Officer A, Natoli TE, Papanastasiou M, Patel J, Sharma V, Toder C, Tubelli AA, Young JZ, Carr SA, Golub TR, Subramanian A, MacCoss MJ, Tsai LH, Jaffe JD. A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations. Cell Syst 2018; 6:424-443.e7. [PMID: 29655704 PMCID: PMC5951639 DOI: 10.1016/j.cels.2018.03.012] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Revised: 01/26/2018] [Accepted: 03/14/2018] [Indexed: 01/05/2023]
Abstract
Although the value of proteomics has been demonstrated, cost and scale are typically prohibitive, and gene expression profiling remains dominant for characterizing cellular responses to perturbations. However, high-throughput sentinel assays provide an opportunity for proteomics to contribute at a meaningful scale. We present a systematic library resource (90 drugs × 6 cell lines) of proteomic signatures that measure changes in the reduced-representation phosphoproteome (P100) and changes in epigenetic marks on histones (GCP). A majority of these drugs elicited reproducible signatures, but notable cell line- and assay-specific differences were observed. Using the "connectivity" framework, we compared signatures across cell types and integrated data across assays, including a transcriptional assay (L1000). Consistent connectivity among cell types revealed cellular responses that transcended lineage, and consistent connectivity among assays revealed unexpected associations between drugs. We further leveraged the resource against public data to formulate hypotheses for treatment of multiple myeloma and acute lymphocytic leukemia. This resource is publicly available at https://clue.io/proteomics.
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Affiliation(s)
| | - Ryan Peckner
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | | | - Jacob K Asiedu
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | - Amanda L Creech
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | - John F Davis
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | - Desiree Davison
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | | | - Jarrett D Egertson
- University of Washington, Department of Genome Sciences, 3720 15th Avenue NE, Seattle, WA 98195, USA
| | - Shawn Egri
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | - Joshua Gould
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | - Tak Ko
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Sarah A Johnson
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | - David L Lahr
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | - Daniel Lam
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | - Zihan Liu
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | | | - Xiaodong Lu
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | - Brendan X MacLean
- University of Washington, Department of Genome Sciences, 3720 15th Avenue NE, Seattle, WA 98195, USA
| | - Alison E Mungenast
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Adam Officer
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | - Ted E Natoli
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | | | - Jinal Patel
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | - Vagisha Sharma
- University of Washington, Department of Genome Sciences, 3720 15th Avenue NE, Seattle, WA 98195, USA
| | - Courtney Toder
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | | | - Jennie Z Young
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Steven A Carr
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | - Todd R Golub
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA
| | | | - Michael J MacCoss
- University of Washington, Department of Genome Sciences, 3720 15th Avenue NE, Seattle, WA 98195, USA
| | - Li-Huei Tsai
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Jacob D Jaffe
- The Broad Institute, 415 Main Street, Cambridge, MA 02142, USA.
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18
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Rosenberger G, Bludau I, Schmitt U, Heusel M, Hunter CL, Liu Y, MacCoss MJ, MacLean BX, Nesvizhskii AI, Pedrioli PGA, Reiter L, Röst HL, Tate S, Ting YS, Collins BC, Aebersold R. Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses. Nat Methods 2017; 14:921-927. [PMID: 28825704 PMCID: PMC5581544 DOI: 10.1038/nmeth.4398] [Citation(s) in RCA: 139] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Accepted: 07/07/2017] [Indexed: 12/18/2022]
Abstract
Liquid chromatography coupled to tandem mass spectrometry is the main method for high-throughput identification and quantification of peptides and inferred proteins. Within this field, data-independent acquisition (DIA) combined with peptide-centric scoring, exemplified by SWATH-MS, emerged as a scalable method to achieve deep and consistent proteome coverage across large-scale datasets. Here we discuss the adaptation of statistical concepts developed for discovery proteomics based on spectrum-centric scoring to large-scale DIA experiments analyzed with peptide-centric scoring strategies and provide guidance on their application. We show that optimal tradeoffs between sensitivity and specificity require careful considerations of the relationship between proteins in the samples and proteins represented in the spectral library. We propose the application of a global analyte constraint to prevent accumulation of false positives across large-scale datasets. Furthermore, to increase the quality and reproducibility of published proteomic results, well-established confidence criteria should be reported for detected peptide queries, peptides and inferred proteins.
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Affiliation(s)
- George Rosenberger
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Isabell Bludau
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Uwe Schmitt
- ID Scientific IT Services, ETH Zurich, Zurich, Switzerland
| | - Moritz Heusel
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,PhD program in Molecular and Translational Biomedicine, Competence Center Personalized Medicine (CC-PM), ETH Zurich and University of Zurich, Zurich, Switzerland
| | | | - Yansheng Liu
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Brendan X MacLean
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.,Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Patrick G A Pedrioli
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | | | - Hannes L Röst
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | | | - Ying S Ting
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Ben C Collins
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Faculty of Science, University of Zurich, Zurich, Switzerland
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19
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Rardin MJ, Schilling B, Cheng LY, MacLean BX, Sorensen DJ, Sahu AK, MacCoss MJ, Vitek O, Gibson BW. MS1 Peptide Ion Intensity Chromatograms in MS2 (SWATH) Data Independent Acquisitions. Improving Post Acquisition Analysis of Proteomic Experiments. Mol Cell Proteomics 2015; 14:2405-19. [PMID: 25987414 PMCID: PMC4563724 DOI: 10.1074/mcp.o115.048181] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2015] [Indexed: 11/17/2022] Open
Abstract
Quantitative analysis of discovery-based proteomic workflows now relies on high-throughput large-scale methods for identification and quantitation of proteins and post-translational modifications. Advancements in label-free quantitative techniques, using either data-dependent or data-independent mass spectrometric acquisitions, have coincided with improved instrumentation featuring greater precision, increased mass accuracy, and faster scan speeds. We recently reported on a new quantitative method called MS1 Filtering (Schilling et al. (2012) Mol. Cell. Proteomics 11, 202–214) for processing data-independent MS1 ion intensity chromatograms from peptide analytes using the Skyline software platform. In contrast, data-independent acquisitions from MS2 scans, or SWATH, can quantify all fragment ion intensities when reference spectra are available. As each SWATH acquisition cycle typically contains an MS1 scan, these two independent label-free quantitative approaches can be acquired in a single experiment. Here, we have expanded the capability of Skyline to extract both MS1 and MS2 ion intensity chromatograms from a single SWATH data-independent acquisition in an Integrated Dual Scan Analysis approach. The performance of both MS1 and MS2 data was examined in simple and complex samples using standard concentration curves. Cases of interferences in MS1 and MS2 ion intensity data were assessed, as were the differentiation and quantitation of phosphopeptide isomers in MS2 scan data. In addition, we demonstrated an approach for optimization of SWATH m/z window sizes to reduce interferences using MS1 scans as a guide. Finally, a correlation analysis was performed on both MS1 and MS2 ion intensity data obtained from SWATH acquisitions on a complex mixture using a linear model that automatically removes signals containing interferences. This work demonstrates the practical advantages of properly acquiring and processing MS1 precursor data in addition to MS2 fragment ion intensity data in a data-independent acquisition (SWATH), and provides an approach to simultaneously obtain independent measurements of relative peptide abundance from a single experiment.
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Affiliation(s)
- Matthew J Rardin
- From the ‡Buck Institute for Research on Aging, 8001 Redwood Blvd, Novato, California 94945
| | - Birgit Schilling
- From the ‡Buck Institute for Research on Aging, 8001 Redwood Blvd, Novato, California 94945
| | - Lin-Yang Cheng
- §Department of Statistics, Purdue University, West Lafayette, IN 47907
| | - Brendan X MacLean
- ‖Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Dylan J Sorensen
- From the ‡Buck Institute for Research on Aging, 8001 Redwood Blvd, Novato, California 94945
| | - Alexandria K Sahu
- From the ‡Buck Institute for Research on Aging, 8001 Redwood Blvd, Novato, California 94945
| | - Michael J MacCoss
- ‖Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Olga Vitek
- ¶College of Science, College of Computer and Information Science, Northeastern University, Boston, Massachusetts 02115
| | - Bradford W Gibson
- From the ‡Buck Institute for Research on Aging, 8001 Redwood Blvd, Novato, California 94945; **Department of Pharmaceutical Chemistry, University of California, San Francisco, California 94143
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Schilling B, Rardin MJ, MacLean BX, Zawadzka AM, Frewen BE, Cusack MP, Sorensen DJ, Bereman MS, Jing E, Wu CC, Verdin E, Kahn CR, Maccoss MJ, Gibson BW. Platform-independent and label-free quantitation of proteomic data using MS1 extracted ion chromatograms in skyline: application to protein acetylation and phosphorylation. Mol Cell Proteomics 2012; 11:202-14. [PMID: 22454539 PMCID: PMC3418851 DOI: 10.1074/mcp.m112.017707] [Citation(s) in RCA: 339] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Despite advances in metabolic and postmetabolic labeling methods for quantitative proteomics, there remains a need for improved label-free approaches. This need is particularly pressing for workflows that incorporate affinity enrichment at the peptide level, where isobaric chemical labels such as isobaric tags for relative and absolute quantitation and tandem mass tags may prove problematic or where stable isotope labeling with amino acids in cell culture labeling cannot be readily applied. Skyline is a freely available, open source software tool for quantitative data processing and proteomic analysis. We expanded the capabilities of Skyline to process ion intensity chromatograms of peptide analytes from full scan mass spectral data (MS1) acquired during HPLC MS/MS proteomic experiments. Moreover, unlike existing programs, Skyline MS1 filtering can be used with mass spectrometers from four major vendors, which allows results to be compared directly across laboratories. The new quantitative and graphical tools now available in Skyline specifically support interrogation of multiple acquisitions for MS1 filtering, including visual inspection of peak picking and both automated and manual integration, key features often lacking in existing software. In addition, Skyline MS1 filtering displays retention time indicators from underlying MS/MS data contained within the spectral library to ensure proper peak selection. The modular structure of Skyline also provides well defined, customizable data reports and thus allows users to directly connect to existing statistical programs for post hoc data analysis. To demonstrate the utility of the MS1 filtering approach, we have carried out experiments on several MS platforms and have specifically examined the performance of this method to quantify two important post-translational modifications: acetylation and phosphorylation, in peptide-centric affinity workflows of increasing complexity using mouse and human models.
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Affiliation(s)
- Birgit Schilling
- Buck Institute for Research on Aging, Novato, California 94945, USA
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