Lipid Data Analyzer: Automated Identification of Lipid Structures in High-Throughput LC-MSn Data

Publikation: Konferenzbeitrag(Altdaten) Vortrag oder PräsentationForschungBegutachtung

Abstract

Introduction: LC-MS is the method of choice to measure quantitative changes of hundreds of lipids in complex mixtures simultaneously. However, many lipid species are isobaric, resulting in ambiguities about the true identity of the lipid in MS1 data. MSn spectra carry the potential to elucidate structural features: the lipid class, their constituent fatty acids, and in many cases the regioisomeric position. However, MSn spectra of a lipid vary tremendously as fragmentation processes depend on the mass spectrometer used, the collision energy, and adduct ions. Currently available tools use static databases containing fragment masses, and are as such limited to specific instrumental setups, we developed a flexible algorithm for automated identification of lipid structures.

Methods: The presented algorithm identifies structural information of lipids based on MSn spectra. Expected MSn fragments and their intensity relationships are defined. Quantitation is performed with a sophisticated 3D approach and identification specificity of species without MSn spectra is increased with an optional retention time prediction algorithm. The algorithm was validated using a pool of 97 lipid standards with known fatty acid composition, regioisomeric position and at defined quantities, covering the lipid classes Cer, DG, PC, LPC, pPC, PE, LPE, pPE, PG, PI, PS, LPS, SM, and TG.

Results: We developed rules defining the theoretical appearance of MSn spectra for the lipid classes above, including the description of fragments and their intensity relationships, which can reveal regiospecific information, and are invaluable quality criteria for the correctness of a hit. The rules are embedded in LDA2, an easy to use application for the specific identification and accurate quantitation of lipid species in MSn data. We offer complete rule sets for various MS instruments and collision energies, and a graphical user interface for rule definition with direct visual feedback based on spectra.
In the standard pool, we could identify 99% of the lipid species, with 88% correct fatty acid composition and 84% correct regioisomeric position. Additionally, we successfully applied our algorithm in the analysis of the mouse liver lipidome ,where we could identify 621 distinct lipid species in the classes PC, PE, PILPC, LPE, PC, PE, DG, and TG.

Originalspracheenglisch
PublikationsstatusVeröffentlicht - 31 Mär 2016
VeranstaltungAnalytical Chemistry Workshop - BOKU University, Vienna, Österreich
Dauer: 31 Mär 20161 Apr 2016

Workshop

WorkshopAnalytical Chemistry Workshop
LandÖsterreich
OrtVienna
Zeitraum31/03/161/04/16

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Throughput
Lipids
Fatty Acids
Mass spectrometers
Graphical user interfaces
Complex Mixtures
Chemical analysis
Liver
Ions
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Lipid Data Analyzer: Automated Identification of Lipid Structures in High-Throughput LC-MSn Data. / Thallinger, Gerhard.

2016. Analytical Chemistry Workshop, Vienna, Österreich.

Publikation: Konferenzbeitrag(Altdaten) Vortrag oder PräsentationForschungBegutachtung

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abstract = "Introduction: LC-MS is the method of choice to measure quantitative changes of hundreds of lipids in complex mixtures simultaneously. However, many lipid species are isobaric, resulting in ambiguities about the true identity of the lipid in MS1 data. MSn spectra carry the potential to elucidate structural features: the lipid class, their constituent fatty acids, and in many cases the regioisomeric position. However, MSn spectra of a lipid vary tremendously as fragmentation processes depend on the mass spectrometer used, the collision energy, and adduct ions. Currently available tools use static databases containing fragment masses, and are as such limited to specific instrumental setups, we developed a flexible algorithm for automated identification of lipid structures.Methods: The presented algorithm identifies structural information of lipids based on MSn spectra. Expected MSn fragments and their intensity relationships are defined. Quantitation is performed with a sophisticated 3D approach and identification specificity of species without MSn spectra is increased with an optional retention time prediction algorithm. The algorithm was validated using a pool of 97 lipid standards with known fatty acid composition, regioisomeric position and at defined quantities, covering the lipid classes Cer, DG, PC, LPC, pPC, PE, LPE, pPE, PG, PI, PS, LPS, SM, and TG.Results: We developed rules defining the theoretical appearance of MSn spectra for the lipid classes above, including the description of fragments and their intensity relationships, which can reveal regiospecific information, and are invaluable quality criteria for the correctness of a hit. The rules are embedded in LDA2, an easy to use application for the specific identification and accurate quantitation of lipid species in MSn data. We offer complete rule sets for various MS instruments and collision energies, and a graphical user interface for rule definition with direct visual feedback based on spectra.In the standard pool, we could identify 99{\%} of the lipid species, with 88{\%} correct fatty acid composition and 84{\%} correct regioisomeric position. Additionally, we successfully applied our algorithm in the analysis of the mouse liver lipidome ,where we could identify 621 distinct lipid species in the classes PC, PE, PILPC, LPE, PC, PE, DG, and TG.",
author = "Gerhard Thallinger",
year = "2016",
month = "3",
day = "31",
language = "English",
note = "Analytical Chemistry Workshop ; Conference date: 31-03-2016 Through 01-04-2016",

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N2 - Introduction: LC-MS is the method of choice to measure quantitative changes of hundreds of lipids in complex mixtures simultaneously. However, many lipid species are isobaric, resulting in ambiguities about the true identity of the lipid in MS1 data. MSn spectra carry the potential to elucidate structural features: the lipid class, their constituent fatty acids, and in many cases the regioisomeric position. However, MSn spectra of a lipid vary tremendously as fragmentation processes depend on the mass spectrometer used, the collision energy, and adduct ions. Currently available tools use static databases containing fragment masses, and are as such limited to specific instrumental setups, we developed a flexible algorithm for automated identification of lipid structures.Methods: The presented algorithm identifies structural information of lipids based on MSn spectra. Expected MSn fragments and their intensity relationships are defined. Quantitation is performed with a sophisticated 3D approach and identification specificity of species without MSn spectra is increased with an optional retention time prediction algorithm. The algorithm was validated using a pool of 97 lipid standards with known fatty acid composition, regioisomeric position and at defined quantities, covering the lipid classes Cer, DG, PC, LPC, pPC, PE, LPE, pPE, PG, PI, PS, LPS, SM, and TG.Results: We developed rules defining the theoretical appearance of MSn spectra for the lipid classes above, including the description of fragments and their intensity relationships, which can reveal regiospecific information, and are invaluable quality criteria for the correctness of a hit. The rules are embedded in LDA2, an easy to use application for the specific identification and accurate quantitation of lipid species in MSn data. We offer complete rule sets for various MS instruments and collision energies, and a graphical user interface for rule definition with direct visual feedback based on spectra.In the standard pool, we could identify 99% of the lipid species, with 88% correct fatty acid composition and 84% correct regioisomeric position. Additionally, we successfully applied our algorithm in the analysis of the mouse liver lipidome ,where we could identify 621 distinct lipid species in the classes PC, PE, PILPC, LPE, PC, PE, DG, and TG.

AB - Introduction: LC-MS is the method of choice to measure quantitative changes of hundreds of lipids in complex mixtures simultaneously. However, many lipid species are isobaric, resulting in ambiguities about the true identity of the lipid in MS1 data. MSn spectra carry the potential to elucidate structural features: the lipid class, their constituent fatty acids, and in many cases the regioisomeric position. However, MSn spectra of a lipid vary tremendously as fragmentation processes depend on the mass spectrometer used, the collision energy, and adduct ions. Currently available tools use static databases containing fragment masses, and are as such limited to specific instrumental setups, we developed a flexible algorithm for automated identification of lipid structures.Methods: The presented algorithm identifies structural information of lipids based on MSn spectra. Expected MSn fragments and their intensity relationships are defined. Quantitation is performed with a sophisticated 3D approach and identification specificity of species without MSn spectra is increased with an optional retention time prediction algorithm. The algorithm was validated using a pool of 97 lipid standards with known fatty acid composition, regioisomeric position and at defined quantities, covering the lipid classes Cer, DG, PC, LPC, pPC, PE, LPE, pPE, PG, PI, PS, LPS, SM, and TG.Results: We developed rules defining the theoretical appearance of MSn spectra for the lipid classes above, including the description of fragments and their intensity relationships, which can reveal regiospecific information, and are invaluable quality criteria for the correctness of a hit. The rules are embedded in LDA2, an easy to use application for the specific identification and accurate quantitation of lipid species in MSn data. We offer complete rule sets for various MS instruments and collision energies, and a graphical user interface for rule definition with direct visual feedback based on spectra.In the standard pool, we could identify 99% of the lipid species, with 88% correct fatty acid composition and 84% correct regioisomeric position. Additionally, we successfully applied our algorithm in the analysis of the mouse liver lipidome ,where we could identify 621 distinct lipid species in the classes PC, PE, PILPC, LPE, PC, PE, DG, and TG.

M3 - (Old data) Lecture or Presentation

ER -