Metabolites

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20 pages, 2639 KiB

Open AccessArticle

Lipid Annotator: Towards Accurate Annotation in Non-Targeted Liquid Chromatography High-Resolution Tandem Mass Spectrometry (LC-HRMS/MS) Lipidomics Using a Rapid and User-Friendly Software

by Jeremy P. Koelmel, Xiangdong Li, Sarah M. Stow, Mark J. Sartain, Adithya Murali, Robin Kemperman, Hiroshi Tsugawa, Mikiko Takahashi, Vasilis Vasiliou, John A. Bowden, Richard A. Yost, Timothy J. Garrett and Norton Kitagawa

Metabolites 2020, 10(3), 101; https://doi.org/10.3390/metabo10030101 - 12 Mar 2020

Cited by 62 | Viewed by 10265

Abstract

Lipidomics has great promise in various applications; however, a major bottleneck in lipidomics is the accurate and comprehensive annotation of high-resolution tandem mass spectral data. While the number of available lipidomics software has drastically increased over the past five years, the reduction of [...] Read more.

Lipidomics has great promise in various applications; however, a major bottleneck in lipidomics is the accurate and comprehensive annotation of high-resolution tandem mass spectral data. While the number of available lipidomics software has drastically increased over the past five years, the reduction of false positives and the realization of obtaining structurally accurate annotations remains a significant challenge. We introduce Lipid Annotator, which is a user-friendly software for lipidomic analysis of data collected by liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). We validate annotation accuracy against lipid standards and other lipidomics software. Lipid Annotator was integrated into a workflow applying an iterative exclusion MS/MS acquisition strategy to National Institute of Standards and Technology (NIST) SRM 1950 Metabolites in Frozen Human Plasma using reverse phase LC-HRMS/MS. Lipid Annotator, LipidMatch, and MS-DIAL produced consensus annotations at the level of lipid class for 98% and 96% of features detected in positive and negative mode, respectively. Lipid Annotator provides percentages of fatty acyl constituent species and employs scoring algorithms based on probability theory, which is less subjective than the tolerance and weighted match scores commonly used by available software. Lipid Annotator enables analysis of large sample cohorts and improves data-processing throughput as compared to previous lipidomics software. Full article

(This article belongs to the Special Issue Compound Identification of Small Molecules)

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12 pages, 1885 KiB

Open AccessArticle

Study of the Royal Jelly Free Fatty Acids by Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS)

by Maroula G. Kokotou, Christiana Mantzourani, Rodalia Babaiti and George Kokotos

Metabolites 2020, 10(1), 40; https://doi.org/10.3390/metabo10010040 - 16 Jan 2020

Cited by 19 | Viewed by 4807

Abstract

The lipidome of royal jelly (RJ) consists of medium-chained (8–12 carbon atoms) free fatty acids. We present herein a liquid chromatography-high resolution mass spectrometry (HRMS) method that permits the determination of RJ fatty acids and at the same time the detection of suspect [...] Read more.

The lipidome of royal jelly (RJ) consists of medium-chained (8–12 carbon atoms) free fatty acids. We present herein a liquid chromatography-high resolution mass spectrometry (HRMS) method that permits the determination of RJ fatty acids and at the same time the detection of suspect fatty acids. The method allows for the direct quantification of seven free fatty acids of RJ, avoiding any derivatization step. It was validated and applied in seven RJ samples, where the major RJ fatty acid trans-10-hydroxy-2-decenoic acid (10-HDA) was found to vary from 0.771 ± 0.08 to 0.928 ± 0.04 g/100 g fresh RJ. Four additional suspect fatty acids were simultaneously detected taking advantage of the HRMS detection. Full article

(This article belongs to the Special Issue Compound Identification of Small Molecules)

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17 pages, 3239 KiB

Open AccessArticle

Generation of a Collision Cross Section Library for Multi-Dimensional Plant Metabolomics Using UHPLC-Trapped Ion Mobility-MS/MS

by Mark Schroeder, Sven W. Meyer, Heino M. Heyman, Aiko Barsch and Lloyd W. Sumner

Metabolites 2020, 10(1), 13; https://doi.org/10.3390/metabo10010013 - 24 Dec 2019

Cited by 55 | Viewed by 7377

Abstract

The utility of metabolomics is well documented; however, its full scientific promise has not yet been realized due to multiple technical challenges. These grand challenges include accurate chemical identification of all observable metabolites and the limiting depth-of-coverage of current metabolomics methods. Here, we [...] Read more.

The utility of metabolomics is well documented; however, its full scientific promise has not yet been realized due to multiple technical challenges. These grand challenges include accurate chemical identification of all observable metabolites and the limiting depth-of-coverage of current metabolomics methods. Here, we report a combinatorial solution to aid in both grand challenges using UHPLC-trapped ion mobility spectrometry coupled to tandem mass spectrometry (UHPLC-TIMS-TOF-MS). TIMS offers additional depth-of-coverage through increased peak capacities realized with the multi-dimensional UHPLC-TIMS separations. Metabolite identification confidence is simultaneously enhanced by incorporating orthogonal collision cross section (CCS) data matching. To facilitate metabolite identifications, we created a CCS library of 146 plant natural products. This library was generated using TIMS with N₂ drift gas to record the ^TIMSCCS_N2 of plant natural products with a high degree of reproducibility; i.e., average RSD = 0.10%. The robustness of ^TIMSCCS_N2 data matching was tested using authentic standards spiked into complex plant extracts, and the precision of CCS measurements were determined to be independent of matrix affects. The utility of the UHPLC-TIMS-TOF-MS/MS in metabolomics was then demonstrated using extracts from the model legume Medicago truncatula and metabolites were confidently identified based on retention time, accurate mass, molecular formula, and CCS. Full article

(This article belongs to the Special Issue Compound Identification of Small Molecules)

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15 pages, 2901 KiB

Open AccessArticle

Comprehensive Evaluation of Parameters Affecting One-Step Method for Quantitative Analysis of Fatty Acids in Meat

by Michael P. Agnew, Cameron R. Craigie, Gayani Weralupitiya, Marlon M. Reis, Patricia L. Johnson and Mariza G. Reis

Metabolites 2019, 9(9), 189; https://doi.org/10.3390/metabo9090189 - 18 Sep 2019

Cited by 25 | Viewed by 3394

Abstract

Despite various direct transmethylation methods having been published and applied to analysis of meat fatty acid (FA) composition, there are still conflicting ideas about the best method for overcoming all the difficulties posed by analysis of complex mixtures of FA in meat. This [...] Read more.

Despite various direct transmethylation methods having been published and applied to analysis of meat fatty acid (FA) composition, there are still conflicting ideas about the best method for overcoming all the difficulties posed by analysis of complex mixtures of FA in meat. This study performed a systematic investigation of factors affecting a one-step method for quantitative analysis of fatty acids in freeze-dried animal tissue. Approximately 280 reactions, selected using factorial design, were performed to investigate the effect of temperature, reaction time, acid concentration, solvent volume, sample weight and sample moisture. The reaction yield for different types of fatty acids, including saturated, unsaturated (cis, trans and conjugated) and long-chain polyunsaturated fatty acids was determined. The optimised condition for one-step transmethylation was attained with four millilitres 5% sulfuric acid in methanol (as acid catalyst), four millilitres toluene (as co-solvent), 300 mg of freeze-dried meat and incubation at 70 °C for 2 h, with interim mixing by inversion at 30, 60 and 90 min for 15 s. The optimised condition was applied to meat samples from different species, covering a broad range of fat content and offers a simplified and reliable method for analysis of fatty acids from meat samples. Full article

(This article belongs to the Special Issue Compound Identification of Small Molecules)

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14 pages, 1358 KiB

Open AccessArticle

Improved Small Molecule Identification through Learning Combinations of Kernel Regression Models

by Céline Brouard, Antoine Bassé, Florence d’Alché-Buc and Juho Rousu

Metabolites 2019, 9(8), 160; https://doi.org/10.3390/metabo9080160 - 1 Aug 2019

Cited by 12 | Viewed by 3921

Abstract

In small molecule identification from tandem mass (MS/MS) spectra, input–output kernel regression (IOKR) currently provides the state-of-the-art combination of fast training and prediction and high identification rates. The IOKR approach can be simply understood as predicting a fingerprint vector from the MS/MS spectrum [...] Read more.

In small molecule identification from tandem mass (MS/MS) spectra, input–output kernel regression (IOKR) currently provides the state-of-the-art combination of fast training and prediction and high identification rates. The IOKR approach can be simply understood as predicting a fingerprint vector from the MS/MS spectrum of the unknown molecule, and solving a pre-image problem to find the molecule with the most similar fingerprint. In this paper, we bring forward the following improvements to the IOKR framework: firstly, we formulate the IOKRreverse model that can be understood as mapping molecular structures into the MS/MS feature space and solving a pre-image problem to find the molecule whose predicted spectrum is the closest to the input MS/MS spectrum. Secondly, we introduce an approach to combine several IOKR and IOKRreverse models computed from different input and output kernels, called IOKRfusion. The method is based on minimizing structured Hinge loss of the combined model using a mini-batch stochastic subgradient optimization. Our experiments show a consistent improvement of top-k accuracy both in positive and negative ionization mode data. Full article

(This article belongs to the Special Issue Compound Identification of Small Molecules)

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12 pages, 2720 KiB

Open AccessArticle

Simultaneous Synthesis of Vitamins D₂, D₄, D₅, D₆, and D₇ from Commercially Available Phytosterol, β-Sitosterol, and Identification of Each Vitamin D by HSQC NMR

by Shiro Komba, Eiichi Kotake-Nara and Wakako Tsuzuki

Metabolites 2019, 9(6), 107; https://doi.org/10.3390/metabo9060107 - 6 Jun 2019

Cited by 8 | Viewed by 4184

Abstract

We succeeded in simultaneously synthesizing the vitamin D family, vitamins D₂, D₄, D₅, D₆, and D₇, from β-sitosterol, which is sold as a commercially available reagent from Tokyo Chemical Industry Co., Ltd. It [...] Read more.

We succeeded in simultaneously synthesizing the vitamin D family, vitamins D₂, D₄, D₅, D₆, and D₇, from β-sitosterol, which is sold as a commercially available reagent from Tokyo Chemical Industry Co., Ltd. It is officially sold as a mixture of four phytosterols {β-sitosterol (40–45%), campesterol (20–30%), stigmasterol, and brassicasterol}. Owing to this, we anticipated that, using this reagent, various vitamin D analogs could be synthesized simultaneously. We also synthesized vitamin D₃ from pure cholesterol and analyzed and compared all vitamin D analogs (D₂, D₃, D₄, D₅, D₆, and D₇) by HSQC NMR. We succeeded in clearly demonstrating the difference in the NMR chemical shifts for each vitamin D analog. Full article

(This article belongs to the Special Issue Compound Identification of Small Molecules)

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14 pages, 2510 KiB

Open AccessArticle

DynaStI: A Dynamic Retention Time Database for Steroidomics

by Santiago Codesido, Giuseppe Marco Randazzo, Fabio Lehmann, Víctor González-Ruiz, Arnaud García, Ioannis Xenarios, Robin Liechti, Alan Bridge, Julien Boccard and Serge Rudaz

Metabolites 2019, 9(5), 85; https://doi.org/10.3390/metabo9050085 - 30 Apr 2019

Cited by 19 | Viewed by 4083

Abstract

Steroidomics studies face the challenge of separating analytical compounds with very similar structures (i.e., isomers). Liquid chromatography (LC) is commonly used to this end, but the shared core structure of this family of compounds compromises effective separations among the numerous chemical analytes with [...] Read more.

Steroidomics studies face the challenge of separating analytical compounds with very similar structures (i.e., isomers). Liquid chromatography (LC) is commonly used to this end, but the shared core structure of this family of compounds compromises effective separations among the numerous chemical analytes with comparable physico-chemical properties. Careful tuning of the mobile phase gradient and an appropriate choice of the stationary phase can be used to overcome this problem, in turn modifying the retention times in different ways for each compound. In the usual workflow, this approach is suboptimal for the annotation of features based on retention times since it requires characterizing a library of known compounds for every fine-tuned configuration. We introduce a software solution, DynaStI, that is capable of annotating liquid chromatography-mass spectrometry (LC–MS) features by dynamically generating the retention times from a database containing intrinsic properties of a library of metabolites. DynaStI uses the well-established linear solvent strength (LSS) model for reversed-phase LC. Given a list of LC–MS features and some characteristics of the LC setup, this software computes the corresponding retention times for the internal database and then annotates the features using the exact masses with predicted retention times at the working conditions. DynaStI is able to automatically calibrate its predictions to compensate for deviations in the input parameters. The database also includes identification and structural information for each annotation, such as IUPAC name, CAS number, SMILES string, metabolic pathways, and links to external metabolomic or lipidomic databases. Full article

(This article belongs to the Special Issue Compound Identification of Small Molecules)

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14 pages, 4282 KiB

Open AccessArticle

Surfactant Lipidomics of Alveolar Lavage Fluid in Mice Based on Ultra-High-Performance Liquid Chromatography Coupled to Hybrid Quadrupole-Exactive Orbitrap Mass Spectrometry

by Rui Yang, Ying Zhang, Wenjuan Qian, Linxiu Peng, Lili Lin, Jia Xu, Tong Xie, Jianjian Ji, Xiuqin Zhan and Jinjun Shan

Metabolites 2019, 9(4), 80; https://doi.org/10.3390/metabo9040080 - 25 Apr 2019

Cited by 20 | Viewed by 4152

Abstract

Surfactant lipid metabolism is closely related to pulmonary diseases. Lipid metabolism disorder can cause lung diseases, vice versa. With this rationale, a useful method was established in this study to determine the lipidome in bronchoalveolar lavage fluid (BALF) of mice. The lipid components [...] Read more.

Surfactant lipid metabolism is closely related to pulmonary diseases. Lipid metabolism disorder can cause lung diseases, vice versa. With this rationale, a useful method was established in this study to determine the lipidome in bronchoalveolar lavage fluid (BALF) of mice. The lipid components in BALF were extracted by liquid–liquid extraction (methanol and methyl tert-butyl ether, and water). Ultra-high-performance liquid chromatography coupled to hybrid Quadrupole-Exactive Orbitrap mass spectrometry was used to analyze the extracted samples, which showed a broad scanning range of 215–1800 m/z. With MS-DIAL software and built-in LipidBlast database, we identified 38 lipids in positive, and 31 lipids in negative, ion mode, including lysophosphatidylcholine (lysoPC), phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), etc. Then, the changes of lipids in BALF of mice with acute lung injury (ALI) induced by lipopolysaccharide (LPS) was investigated, which may contribute to further exploration of the pathogenesis of ALI. Full article

(This article belongs to the Special Issue Compound Identification of Small Molecules)

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