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Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics

  • Kendra J. Adams
    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
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  • , 
  • Brian Pratt
    Brian Pratt
    Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
    More by Brian Pratt
  • , 
  • Neelanjan Bose
    Neelanjan Bose
    Buck Institute for Research on Aging, Novato, California 94945, United States
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  • , 
  • Laura G. Dubois
    Laura G. Dubois
    Proteomics and Metabolomics Shared Resource, Duke University, Durham, North Carolina 27701, United States
    More by Laura G. Dubois
  • , 
  • Lisa St. John-Williams
    Lisa St. John-Williams
    Proteomics and Metabolomics Shared Resource, Duke University, Durham, North Carolina 27701, United States
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  • , 
  • Kevin M. Perrott
    Kevin M. Perrott
    Buck Institute for Research on Aging, Novato, California 94945, United States
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  • , 
  • Karina Ky
    Karina Ky
    University of California San Francisco, San Francisco, California 94143, United States
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  • , 
  • Pankaj Kapahi
    Pankaj Kapahi
    Buck Institute for Research on Aging, Novato, California 94945, United States
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  • , 
  • Vagisha Sharma
    Vagisha Sharma
    Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
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  • , 
  • Michael J. MacCoss
    Michael J. MacCoss
    Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
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  • , 
  • M. Arthur Moseley
    M. Arthur Moseley
    Proteomics and Metabolomics Shared Resource, Duke University, Durham, North Carolina 27701, United States
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  • , 
  • Carol A. Colton
    Carol A. Colton
    Department of Neurology, Duke University, Durham, North Carolina 27710, United States
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  • , 
  • Brendan X. MacLean
    Brendan X. MacLean
    Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
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  • , 
  • Birgit Schilling*
    Birgit Schilling
    Buck Institute for Research on Aging, Novato, California 94945, United States
    *E-mail: [email protected] (B.S.).
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  • , 
  • J. Will Thompson*
    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
    *E-mail: [email protected] (J.W.T.).
    More by J. Will Thompson
    Orcidhttp://orcid.org/0000-0001-6567-1268
  • , and 
  • Alzheimer’s Disease Metabolomics Consortium
    Alzheimer’s Disease Metabolomics Consortium
    Proteomics and Metabolomics Shared Resource, Duke University, Durham, North Carolina 27701, United States
    Department of Neurology, Duke University, Durham, North Carolina 27710, United States
    Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
    Buck Institute for Research on Aging, Novato, California 94945, United States
    University of California San Francisco, San Francisco, California 94143, United States
    Department of Pharmacology and Cancer Biology, Duke University, Durham, North Carolina 27710, United States
    More by Alzheimer’s Disease Metabolomics Consortium
Cite this: J. Proteome Res. 2020, 19, 4, 1447–1458
Publication Date (Web):January 27, 2020
https://doi.org/10.1021/acs.jproteome.9b00640
Copyright © 2020 American Chemical Society
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    Abstract

    Abstract Image

    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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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.9b00640.

    • Ion transitions, collision energy, cell exit potential, declustering potential, and entrance potential for the purine and pyrimidine LC–MS/MS panel (Table S1); on transitions, collision energy values, retention times, and internal standards for targeted compounds in the polyamine custom assay (Table S2); screenshot of the small molecule filter tab in Skyline, allowing the user to define the precursor and fragment ion adducts desired for analysis; additionally, the user can utilize the “ion-type” field to define the analysis of a precursor (“p”) or fragment (“f”) in a document-wide fashion (Figure S1); collision energy optimization enabled via Settings → Transition Settings → Prediction tab (Figure S2); (A) peak area of targeted compounds with different mobile phase additives; peak shape and intensity for MTA elution with (B) acetic acid (0.1%) and HFBA (0.025%), (C) formic acid (0.1%) and PFHA (0.0125%), (D) and acetic acid (0.1%) and PFHA (0.0125%) (Figure S3); examples of how to set up the transition list (A) and the full-scan parameter settings (B) for the Skyline analysis of high-resolution metabolomics data; specifically, in this case, the transition list allows for analysis of two diacylglycerol lipids, each with two adduct ionization forms (Figure S4); a beginner tutorial called “Getting Started with Skyline for Small Molecules” is included in the Supporting Information; this tutorial, as well as additional tutorials for detailed features of Skyline analysis for small molecules, is available at https://skyline.ms/tutorials.url; also, a short webinar is available at the following link: https://skyline.ms/tutorial_small_molecule.url (PDF)

    Accession Codes

    All Skyline files are located on the public Panorama Share: https://panoramaweb.org/SkylineForSmallMolecules.url.

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    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

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