bims-metlip 2021-12-26 papers

bims-metlip

Biomed News

on Methods and protocols in metabolomics and lipidomics

Issue of 2021‒12‒26
37 papers selected by
Sofia Costa
Icahn School of Medicine at Mount Sinai

Metabolomics Work Flow and Analytics in Systems Biology.
Dimethylcysteine (DiCys)/o-Phthalaldehyde Derivatization for Chiral Metabolite Analyses: Cross-Comparison of Six Chiral Thiols.
SMART-Miner: A Convolutional Neural Network-based Metabolite Identification from 1 H-13 C HSQC spectra.
Diagnostic Aspects of Vitamin D: Clinical Utility of Vitamin D Metabolite Profiling.
Optimized Workflow for On-Line Derivatization for Targeted Metabolomics Approach by Gas Chromatography-Mass Spectrometry.
Analysis of 10 Urinary BTEX Metabolites by Liquid Chromatography Tandem Mass Spectrometry.
Comparative study for analysis of carbohydrates in biological samples.
Enhancing Metabolomic Coverage in Positive Ionization Mode Using Dicationic Reagents by Infrared Matrix-Assisted Laser Desorption Electrospray Ionization.
Untargeted Analysis of Lemna minor Metabolites: Workflow and Prioritization Strategy Comparing Highly Confident Features between Different Mass Spectrometers.
High-Throughput Analysis of Amino Acids for Protein Quantification in Plant and Animal-Derived Samples Using High Resolution Mass Spectrometry.
LINT-Web: A Web-Based Lipidomic Data Mining Tool Using Intra-Omic Integrative Correlation Strategy.
Is Current Practice Adhering to Guidelines Proposed for Metabolite Identification in LC-MS Untargeted Metabolomics? A Meta-Analysis of the Literature.
A reversed phase ultra-high-performance liquid chromatography-data independent mass spectrometry method for the rapid identification of mycobacterial lipids.
LC-MS Identification and Quantification of Phenolic Compounds in Solid Residues from the Essential Oil Industry.
MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra.
A Sensitive Liquid Chromatography-Mass Spectrometry Method for Determination of Toosendanin in Rat Plasma and its Application to Pharmacokinetic Study.
Automated data-driven mass spectrometry for improved analysis of lipids with dual dissociation techniques.
1H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints.
Advances in Lipid Extraction Methods-A Review.
Organophosphate ester metabolites in human breast milk determined by online solid phase extraction coupled to high pressure liquid chromatography tandem mass spectrometry.
Fully Automated Quantitative Measurement of Serum Organic Acids via LC-MS/MS for the Diagnosis of Organic Acidemias: Establishment of an Automation System and a Proof-of-Concept Validation.
Rapid identification of plasmalogen molecular species using targeted multiplexed selected reaction monitoring mass spectrometry.
Magnetic Solid-Phase Extraction Based on Magnetic Sulfonated Reduced Graphene Oxide for HPLC-MS/MS Analysis of Illegal Basic Dyes in Foods.
Grape Lipidomics: An Extensive Profiling thorough UHPLC-MS/MS Method.
Evaluation of rapid and standard tandem mass spectrometric methods to analyse veterinary drugs and their metabolites in antemortem bodily fluids from food animals.
A pilot study optimizing metabolomic and lipidomic acquisition in serum for biomarker discovery in nonalcoholic fatty liver disease.
Forced Degradation Study of Zanubrutinib: An LC-PDA and LC-MS Approach.
Managing of Unassigned Mass Spectrometric Data by Neural Network for Cancer Phenotypes Classification.
Development and validation of a LC-PDA method for methylphenidate analysis in sewage.
A Novel Approach to Characterize the Lipidome of Marine Archaeon Nitrosopumilus maritimus by Ion Mobility Mass Spectrometry.
GraphOmics: an interactive platform to explore and integrate multi-omics data.
Rapid Targeted Method of Detecting Abused Piperazine Designer Drugs.
Bacterial species identification using MALDI-TOF mass spectrometry and machine learning techniques: A large-scale benchmarking study.
Mapping the Chemistry of Hair Strands by Mass Spectrometry Imaging-A Review.
BATL: Bayesian annotations for targeted lipidomics.
Stability of lipids in plasma and serum: Effects of temperature-related storage conditions on the human lipidome.
The effect of sample age on the metabolic information extracted from formalin-fixed and paraffin embedded tissue samples using desorption electrospray ionization mass spectrometry imaging.

Curr Mol Med. 2021 Dec 16.

Metabolomics Work Flow and Analytics in Systems Biology.

Sanoj Chacko, Yumna B Haseeb, Sohaib Haseeb.

  Metabolomics is an omics approach of systems biology that involves the development and assessment of large-scale, comprehensive biochemical analysis tools for metabolites in biological systems. This review describes the metabolomics workflow and provides an overview of current analytic tools used for the quantification of metabolic profiles. We explain analytic tools such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, ionization techniques, and approaches for data extraction and analysis.

Keywords:  Biomarker; NMR spectroscopy; liquid chromatography; mass spectrometry; metabolomics; proteome

DOI:  https://doi.org/10.2174/1566524022666211217102105
Molecules. 2021 Dec 07. pii: 7416. [Epub ahead of print]26(24):

Dimethylcysteine (DiCys)/o-Phthalaldehyde Derivatization for Chiral Metabolite Analyses: Cross-Comparison of Six Chiral Thiols.

Ankhbayar Lkhagva, Hwan-Ching Tai.

  Metabolomics profiling using liquid chromatography-mass spectrometry (LC-MS) has become an important tool in biomedical research. However, resolving enantiomers still represents a significant challenge in the metabolomics study of complex samples. Here, we introduced N,N-dimethyl-l-cysteine (dimethylcysteine, DiCys), a chiral thiol, for the o-phthalaldehyde (OPA) derivatization of enantiomeric amine metabolites. We took interest in DiCys because of its potential for multiplex isotope-tagged quantification. Here, we characterized the usefulness of DiCys in reversed-phase LC-MS analyses of chiral metabolites, compared against five commonly used chiral thiols: N-acetyl-l-cysteine (NAC); N-acetyl-d-penicillamine (NAP); isobutyryl-l-cysteine (IBLC); N-(tert-butoxycarbonyl)-l-cysteine methyl ester (NBC); and N-(tert-butylthiocarbamoyl)-l-cysteine ethyl ester (BTCC). DiCys and IBLC showed the best overall performance in terms of chiral separation, fluorescence intensity, and ionization efficiency. For chiral separation of amino acids, DiCys/OPA also outperformed Marfey's reagents: 1-fluoro-2-4-dinitrophenyl-5-l-valine amide (FDVA) and 1-fluoro-2-4-dinitrophenyl-5-l-alanine amide (FDAA). As proof of principle, we compared DiCys and IBLC for detecting chiral metabolites in aqueous extracts of rice. By LC-MS analyses, both methods detected twenty proteinogenic l-amino acids and seven d-amino acids (Ala, Arg, Lys, Phe, Ser, Tyr, and Val), but DiCys showed better analyte separation. We conclude that DiCys/OPA is an excellent amine-derivatization method for enantiomeric metabolite detection in LC-MS analyses.

Keywords:  chiral metabolomics; d-amino acids; dimethyl labeling; enantiomer separation; rice water

DOI:  https://doi.org/10.3390/molecules26247416
Magn Reson Chem. 2021 Dec 20.

SMART-Miner: A Convolutional Neural Network-based Metabolite Identification from 1 H-13 C HSQC spectra.

Hyun Woo Kim, Chen Zhang, Garrison W Cottrell, William H Gerwick.

The identification of metabolites from complex biofluids and extracts of tissues is an essential process for understanding metabolic profiles. Nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomics studies for identification and quantification of metabolites. However, the accurate identification of individual metabolites is still a challenging process with higher peak intensity or similar chemical shifts from different metabolites. In this study, we applied a convolutional neural network (CNN) to 1 H-13 C HSQC NMR spectra to achieve accurate peak identification in complex mixtures. The results reveal that the neural network was successfully trained on metabolite identification from these 2D NMR spectra and achieved very good performance compared with other NMR-based metabolomic tools.

DOI: https://doi.org/10.1002/mrc.5240
JBMR Plus. 2021 Dec;5(12): e10581

Diagnostic Aspects of Vitamin D: Clinical Utility of Vitamin D Metabolite Profiling.

Glenville Jones, Martin Kaufmann.

  The assay of vitamin D that began in the 1970s with the quantification of one or two metabolites, 25-OH-D or 1,25-(OH)2D, continues to evolve with the emergence of liquid chromatography tandem mass spectrometry (LC-MS/MS) as the technique of choice. This highly accurate, specific, and sensitive technique has been adopted by many fields of endocrinology for the measurement of multiple other components of the metabolome, and its advantage is that it not only makes it feasible to assay 25-OH-D or 1,25-(OH)2D but also other circulating vitamin D metabolites in the vitamin D metabolome. In the process, this broadens the spectrum of vitamin D metabolites, which the clinician can use to evaluate the many complex genetic and acquired diseases of calcium and phosphate homeostasis involving vitamin D. Several examples are provided in this review that additional metabolites (eg, 24,25-(OH)2D3, 25-OH-D3-26,23-lactone, and 1,24,25-(OH)3D3) or their ratios with the main forms offer valuable additional diagnostic information. This approach illustrates that biomarkers of disease can also include metabolites devoid of biological activity. Herein, a case is presented that the decision to switch to a LC-MS/MS technology permits the measurement of a larger number of vitamin D metabolites simultaneously and does not need to lead to a dramatic increase in cost or complexity because the technique uses a highly versatile tandem mass spectrometer with plenty of reserve analytical capacity. Physicians are encouraged to consider adding this rapidly evolving technique aimed at evaluating the wider vitamin D metabolome toward streamlining their approach to calcium- and phosphate-related disease states. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Keywords:  1,24,25‐(OH)3D3; 24,25‐(OH)2D3; CHRONIC KIDNEY DISEASE; HYPERCALCEMIA; LC‐MS/MS; RICKETS; VITAMIN D METABOLITE RATIO; VITAMIN D METABOLLITE PROFILING; VITAMIN D METABOLOME

DOI:  https://doi.org/10.1002/jbm4.10581
Metabolites. 2021 Dec 18. pii: 888. [Epub ahead of print]11(12):

Optimized Workflow for On-Line Derivatization for Targeted Metabolomics Approach by Gas Chromatography-Mass Spectrometry.

Raphaela Fritsche-Guenther, Yoann Gloaguen, Anna Bauer, Tobias Opialla, Stefan Kempa, Christina A Fleming, Henry Paul Redmond, Jennifer A Kirwan.

  Using manual derivatization in gas chromatography-mass spectrometry samples have varying equilibration times before analysis which increases technical variability and limits the number of potential samples analyzed. By contrast, automated derivatization methods can derivatize and inject each sample in an identical manner. We present a fully automated (on-line) derivatization method used for targeted analysis of different matrices. We describe method optimization and compare results from using off-line and on-line derivatization protocols, including the robustness and reproducibility of the methods. Our final parameters for the derivatization process were 20 µL of methoxyamine (MeOx) in pyridine for 60 min at 30 °C followed by 80 µL N-Methyl-N-trimethylsilyltrifluoracetamide (MSTFA) for 30 min at 30 °C combined with 4 h of equilibration time. The repeatability test in plasma and liver revealed a median relative standard deviation (RSD) of 16% and 10%, respectively. Serum samples showed a consistent intra-batch median RSD of 20% with an inter-batch variability of 27% across three batches. The direct comparison of on-line versus off-line demonstrated that on-line was fit for purpose and improves repeatability with a measured median RSD of 11% compared to 17% using the same method off-line. In summary, we recommend that optimized on-line methods may improve results for metabolomics and should be used where available.

Keywords:  automated derivatization; gas-chromatography mass spectrometry; metabolomics; on-line derivatization; optimization; quality assurance (QA); quality control (QC); validation

DOI:  https://doi.org/10.3390/metabo11120888
Biomed Chromatogr. 2021 Dec 21. e5302

Analysis of 10 Urinary BTEX Metabolites by Liquid Chromatography Tandem Mass Spectrometry.

Elkana Kohn, Dana Barchel, Ahuva Golik, Marc Lougassi, Tamar Wainstock, Matitiahu Berkovitch, Frieda Schwartsburd.

  Benzene, toluene, ethylbenzene and xylene (BTEX) are a group of volatile organic compounds that are ubiquitous in the environment due to the numerous anthropogenic sources. Exposure to BTEX pose a health risk by increasing probability for damage to multiple organs, neurocognitive impairment and birth defects. Urinary BTEX metabolites are useful biomarkers for evaluation of BTEX exposure, because of easiness of sampling and their longer physiological half-lives compared with parent compounds. A method that utilizes liquid chromatography coupled to electrospray ionization tandem mass spectrometry (LC-MS/MS) was developed and validated for simultaneously monitoring ten urinary BTEX metabolites. During the sample preparation an aliquot of urine was diluted by the equal volume of 1% formic acid, internal standards solution was added, then the sample was centrifuged and analyzed. The analytes were separated on the Kinetex-F5 column by applying a linear gradient, consisting of 0.1 % formic acid and methanol. The method was validated according to the FDA Bioanalytical Method Validation Guidance for Industry. The mean method's accuracies of the spiked matrix were 81-122%; the interday precision ranged from 4% to 20%; limits of quantitation were 0.5-2 μg/L. The method was used for evaluation of baseline levels of urinary BTEX metabolites in 87 firefighters.

Keywords:  BTEX; LC-MS/MS; metabolites; urine; validation

DOI:  https://doi.org/10.1002/bmc.5302
Anal Bioanal Chem. 2021 Dec 20.

Comparative study for analysis of carbohydrates in biological samples.

Martin Meyer, Lidia Montero, Sven W Meckelmann, Oliver J Schmitz.

  This work presents a comparative study for the analysis of carbohydrates for four common chromatographic methods, each coupled to mass spectrometry. Supercritical fluid chromatography (SFC), hydrophilic interaction liquid chromatography (HILIC), reversed-phase liquid chromatography (RP-LC) and gas chromatography (GC) with detection by triple quadrupole mass spectrometer (QqQ-MS) are compared. It is shown that gas chromatography and reversed-phase liquid chromatography, each after derivatisation, are superior to the other two methods in terms of separation performance. Furthermore, comparing the different working modes of the mass spectrometer, it can be determined that a targeted analysis, i.e. moving from full scan to single ion monitoring (SIM) and multiple reaction monitoring (MRM), results in an improvement in the sensitivity as well as the repeatability of the method, which has deficiencies especially in the analysis using HILIC. Overall, RP-LC-MS in MRM after derivatisation with 1-phenyl-3-methyl-5-pyrazolone (PMP) proved to be the most suitable method in terms of separation performance, sensitivity and repeatability for the analysis of monosaccharides. Detection limits in the nanomolar range were achieved, which corresponds to a mass concentration in the low µg/L range. The applicability of this method to different biological samples was investigated with various herbal liquors, pectins and a human glycoprotein.

Keywords:  Carbohydrates; Derivatisation; GC–MS; LC–MS; SFC-MS; Saccharides

DOI:  https://doi.org/10.1007/s00216-021-03845-z
Metabolites. 2021 Nov 29. pii: 810. [Epub ahead of print]11(12):

Enhancing Metabolomic Coverage in Positive Ionization Mode Using Dicationic Reagents by Infrared Matrix-Assisted Laser Desorption Electrospray Ionization.

Ying Xi, David C Muddiman.

  Mass spectrometry imaging is a powerful tool to analyze a large number of metabolites with their spatial coordinates collected throughout the sample. However, the significant differences in ionization efficiency pose a big challenge to metabolomic mass spectrometry imaging. To solve the challenge and obtain a complete data profile, researchers typically perform experiments in both positive and negative ionization modes, which is time-consuming. In this work, we evaluated the use of the dicationic reagent, 1,5-pentanediyl-bis(1-butylpyrrolidinium) difluoride (abbreviated to [C5(bpyr)2]F2) to detect a broad range of metabolites in the positive ionization mode by infrared matrix-assisted laser desorption electrospray ionization mass spectrometry imaging (IR-MALDESI MSI). [C5(bpyr)2]F2 at 10 µM was doped in 50% MeOH/H2O (v/v) electrospray solvent to form +1 charged adducted ions with anionic species (-1 charged) through post-electrospray ionization. This method was demonstrated with sectioned rat liver and hen ovary. A total of 73 deprotonated metabolites from rat liver tissue sections were successfully adducted with [C5(bpyr)2]2+ and putatively identified in the adducted positive ionization polarity, along with 164 positively charged metabolite ions commonly seen in positive ionization mode, which resulted in 44% increased molecular coverage. In addition, we were able to generate images of hen ovary sections showing their morphological features. Following-up tandem mass spectrometry (MS/MS) indicated that this dicationic reagent [C5(bpyr)2]2+ could form ionic bonds with the headgroup of glycerophospholipid ions. The addition of the dicationic reagent [C5(bpyr)2]2+ in the electrospray solvent provides a rapid and effective way to enhance the detection of metabolites in positive ionization mode.

Keywords:  IR-MALDESI; ambient ionization; dicationic reagent; mass spectrometry imaging; metabolites

DOI:  https://doi.org/10.3390/metabo11120810
Metabolites. 2021 Dec 02. pii: 832. [Epub ahead of print]11(12):

Untargeted Analysis of Lemna minor Metabolites: Workflow and Prioritization Strategy Comparing Highly Confident Features between Different Mass Spectrometers.

Rofida Wahman, Stefan Moser, Stefan Bieber, Catarina Cruzeiro, Peter Schröder, August Gilg, Frank Lesske, Thomas Letzel.

  Metabolomics approaches provide a vast array of analytical datasets, which require a comprehensive analytical, statistical, and biochemical workflow to reveal changes in metabolic profiles. The biological interpretation of mass spectrometric metabolomics results is still obstructed by the reliable identification of the metabolites as well as annotation and/or classification. In this work, the whole Lemna minor (common duckweed) was extracted using various solvents and analyzed utilizing polarity-extended liquid chromatography (reversed-phase liquid chromatography (RPLC)-hydrophilic interaction liquid chromatography (HILIC)) connected to two time-of-flight (TOF) mass spectrometer types, individually. This study (introduces and) discusses three relevant topics for the untargeted workflow: (1) A comparison study of metabolome samples was performed with an untargeted data handling workflow in two different labs with two different mass spectrometers using the same plant material type. (2) A statistical procedure was observed prioritizing significant detected features (dependent and independent of the mass spectrometer using the predictive methodology Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA). (3) Relevant features were transferred to a prioritization tool (the FOR-IDENT platform (FI)) and were compared with the implemented compound database PLANT-IDENT (PI). This compound database is filled with relevant compounds of the Lemnaceae, Poaceae, Brassicaceae, and Nymphaceae families according to analytical criteria such as retention time (polarity and LogD (pH 7)) and accurate mass (empirical formula). Thus, an untargeted analysis was performed using the new tool as a prioritization and identification source for a hidden-target screening strategy. Consequently, forty-two compounds (amino acids, vitamins, flavonoids) could be recognized and subsequently validated in Lemna metabolic profile using reference standards. The class of flavonoids includes free aglycons and their glycosides. Further, according to our knowledge, the validated flavonoids robinetin and norwogonin were for the first time identified in the Lemna minor extracts.

Keywords:  FOR-IDENT; OPLS-DA; PLANT-IDENT; TOF; metabolomics; polarity-extended chromatography; quadrupole time-of-flight (QTOF)

DOI:  https://doi.org/10.3390/metabo11120832
Molecules. 2021 Dec 14. pii: 7578. [Epub ahead of print]26(24):

High-Throughput Analysis of Amino Acids for Protein Quantification in Plant and Animal-Derived Samples Using High Resolution Mass Spectrometry.

Priyanka Reddy, Aaron Elkins, Joe Panozzo, Simone J Rochfort.

  Current methods for measuring the abundance of proteogenic amino acids in plants require derivatisation, extended run times, very sensitive pH adjustments of the protein hydrolysates, and the use of buffers in the chromatographic phases. Here, we describe a fast liquid chromatography-mass spectrometry (LC-MS) method for the determination of amino acids that requires only three steps: hydrolysis, neutralisation, and sample dilution with a borate buffer solution for pH and retention time stability. The method shows excellent repeatability (repeated consecutive injections) and reproducibility (repeated hydrolysis) in the amino acid content, peak area, and retention time for all the standard amino acids. The chromatographic run time is 20 min with a reproducibility and repeatability of <1% for the retention time and <11% for the peak area of the BSA and quality control (QC) lentil samples. The reproducibility of the total protein levels in the hydrolysis batches 1-4 was <12% for the BSA and the lentil samples. The level of detection on column was below 0.1 µM for most amino acids (mean 0.017 µM).

Keywords:  LC–MS; bovine serum albumin (BSA); hydrolysis; lentil; pulse

DOI:  https://doi.org/10.3390/molecules26247578
Small Methods. 2021 Sep;5(9): e2100206

LINT-Web: A Web-Based Lipidomic Data Mining Tool Using Intra-Omic Integrative Correlation Strategy.

Fengsheng Li, Jia Song, Yingkun Zhang, Shuaikang Wang, Jinhui Wang, Li Lin, Chaoyong Yang, Peng Li, He Huang.

  Lipidomics is a younger member of the "omics" family. It aims to profile lipidome alterations occurring in biological systems. Similar to the other "omics", lipidomic data is highly dimensional and contains a massive amount of information awaiting deciphering and data mining. Currently, the available bioinformatic tools targeting lipidomic data processing and lipid pathway analysis are limited. A few tools designed for lipidomic analysis perform only basic statistical analyses, and lipid pathway analyses rely heavily on public databases (KEGG, Reactome, and HMDB). Due to the inadequate understanding of lipid signaling and metabolism, the use of public databases for lipid pathway analysis can be biased and misleading. Instead of using public databases to interpret lipidomic ontology, the authors introduce an intra-omic integrative correlation strategy for lipidomic data mining. Such an intra-omic strategy allows researchers to unscramble and predict lipid biological functions from correlated genomic ontological results using statistical approaches. To simplify and improve the lipidomic data processing experience, they designed an interactive web-based tool: LINT-web (http://www.lintwebomics.info/) to perform the intra-omic analysis strategy, and validated the functions of LINT-web using two biological systems. Users without sophisticated statistical experience can easily process lipidomic datasets and predict the potential lipid biological functions using LINT-web.

Keywords:  lipidomics; online tools; systems biology; transcriptomics

DOI:  https://doi.org/10.1002/smtd.202100206
J Proteome Res. 2021 Dec 20.

Is Current Practice Adhering to Guidelines Proposed for Metabolite Identification in LC-MS Untargeted Metabolomics? A Meta-Analysis of the Literature.

Dritan Kodra, Petros Pousinis, Panagiotis A Vorkas, Katerina Kademoglou, Theodoros Liapikos, Alexandros Pechlivanis, Christina Virgiliou, Ian D Wilson, Helen Gika, Georgios Theodoridis.

  Metabolite identification remains a bottleneck and a still unregulated area in untargeted LC-MS metabolomics. The metabolomics research community and, in particular, the metabolomics standards initiative (MSI) proposed minimum reporting standards for metabolomics including those for reporting metabolite identification as long ago as 2007. Initially, four levels were proposed ranging from level 1 (unambiguously identified analyte) to level 4 (unidentified analyte). This scheme was expanded in 2014, by independent research groups, to give five levels of confidence. Both schemes provided guidance to the researcher and described the logical steps that had to be made to reach a confident reporting level. These guidelines have been presented and discussed extensively, becoming well-known to authors, editors, and reviewers for academic publications. Despite continuous promotion within the metabolomics community, the application of such guidelines is questionable. The scope of this meta-analysis was to systematically review the current LC-MS-based literature and effectively determine the proportion of papers following the proposed guidelines. Also, within the scope of this meta-analysis was the measurement of the actual identification levels reported in the literature, that is to find how many of the published papers really reached full metabolite identification (level 1) and how many papers did not reach this level.

Keywords:  biomarker discovery; liquid chromatography; mass spectrometry; metabolic profiling; metabolite annotation; metabonomics; unknown metabolites

DOI:  https://doi.org/10.1021/acs.jproteome.1c00841
J Chromatogr A. 2021 Dec 08. pii: S0021-9673(21)00861-X. [Epub ahead of print]1662 462739

A reversed phase ultra-high-performance liquid chromatography-data independent mass spectrometry method for the rapid identification of mycobacterial lipids.

Isin T Sakallioglu, Amith S Maroli, Aline De Lima Leite, Robert Powers.

  A rapid reversed-phase ultra-high-performance liquid chromatography-high resolution mass spectrometry based mycobacterial lipidomics approach is described. This method enables the separation of various lipid classes including lipids specific to mycobacterial, such as methoxy mycolic acid and α-mycolic acid. Lipid separation occurs during a relatively short runtime of 14 min on a charged surface hybrid C18 column. A high-resolution quadrupole-time of flight mass spectrometer and a data independent acquisition mode allowed for the simultaneous acquisition of the full scan and collision induced dissociation fragmentation. The proposed method provides lipid detection results equivalent to or better than existing methods, but with a faster throughput and an overall higher sensitivity. The reversed-phase ultra-high-performance liquid chromatography-high resolution mass spectrometry method was shown to obtain structural information for lipids extracted from Mycobacterium smegmatis, but the method is applicable to the analysis of lipids from various bacterial and mammalian cell lines.

Keywords:  Lipidomics; Liquid chromatography; Mass spectrometry; Mycobacteria; Mycolic acid

DOI:  https://doi.org/10.1016/j.chroma.2021.462739
Antioxidants (Basel). 2021 Dec 19. pii: 2016. [Epub ahead of print]10(12):

LC-MS Identification and Quantification of Phenolic Compounds in Solid Residues from the Essential Oil Industry.

Maria Irakli, Adriana Skendi, Elisavet Bouloumpasi, Paschalina Chatzopoulou, Costas G Biliaderis.

  Plant solid residues obtained from the essential oil industry represent a rich source of phenolic compounds with bioactive properties to be used in the food and pharmaceutical industries. A selective and sensitive liquid chromatography-mass spectrometry (LC-MS) method was developed for the simultaneous determination of phenolic compounds in solid residues of the Lamiaceae family plants. A total of 48 compounds can be separated within 35 min by using the Poroshell-120 EC-C18 column, and a gradient mobile phase of 0.1% formic acid and acetonitrile with flow rate of 0.5 mL/min; salicylic acid was used as internal standard. The calibration curves showed good linearity in the tested concentration range for each analyte (R2 > 0.9921), while recoveries ranged from 70.1% to 115.0% with an intra-day and inter-day precision of less than 6.63% and 15.00%, respectively. Based on the retention behavior, as well as absorption and mass spectra, 17 phenolic acids, 19 flavonoids and 2 phenolic diterpenes were identified and quantified in the solid residues obtained by distillation of six aromatic plants: oregano, rosemary, sage, satureja, lemon balm, and spearmint. The method constitutes an accurate analytical and quality control tool for the simultaneous quantitation of phenolics present in solid waste residues from the essential oil industry.

Keywords:  LC-MS; Lamiaceae; antioxidant activity; essential oil industry; flavonoids; phenolic acids; solid residues

DOI:  https://doi.org/10.3390/antiox10122016
Biomolecules. 2021 Nov 30. pii: 1793. [Epub ahead of print]11(12):

MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra.

Aditya Divyakant Shrivastava, Neil Swainston, Soumitra Samanta, Ivayla Roberts, Marina Wright Muelas, Douglas B Kell.

  The 'inverse problem' of mass spectrometric molecular identification ('given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came') is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem ('calculate a small molecule's likely fragmentation and hence at least some of its mass spectrum from its structure alone') is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the 'translation' a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the 'true' molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are 'similar' to the top hit. In addition to using the 'top hits' directly, we can produce a rank order of these by 'round-tripping' candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower, including those in the last CASMI challenge (for which the results are known), getting 49/93 (53%) precisely correct. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. It seems to act as a Las Vegas algorithm, in that it either gives the correct answer or simply states that it cannot find one. The ability to create and to 'learn' millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.

Keywords:  artificial intelligence; chemical space; deep learning; electrospray; generative methods; mass spectrometry; metabolomics; transformers

DOI:  https://doi.org/10.3390/biom11121793
J Chromatogr Sci. 2021 Dec 21. pii: bmab135. [Epub ahead of print]

A Sensitive Liquid Chromatography-Mass Spectrometry Method for Determination of Toosendanin in Rat Plasma and its Application to Pharmacokinetic Study.

Chuangpeng Shen, Zhisen Pan, Xiaojie Wu, Chong Zhong, Qiao Li, Yuqi Si, Changhui Liu, Haitao Tu, Zhijun Deng, Zhangzhi Zhu, Jiewen Guo, Xiaoyi Xin, Min Liu.

A simple, rapid and sensitive analytical method was developed for the determination of toosendanin in rat plasma using liquid chromatography tandem mass spectrometry (LC-MS/MS). Andrographolide was selected as the internal standard, and the plasma samples were extracted by liquid-liquid extraction with diethyl ether. Chromatographic separation was performed on a Dikma Spursil C18, 3.5 μm (150 × 2.1 mm i.d) analytical column with 85% methanol:water (v/v) containing 0.025% formic acid (pH = 3.9) as mobile phase. The flow rate was 0.25 mL/min, and the total run time was 3 min. Detection was performed with a triple-quadrupole tandem mass spectrometer using negative ion mode electrospray ionization (ESI) in the multiple reaction monitoring (MRM) mode. The MS/MS ion transitions monitored were m/z 573.1 → 531.1 and 349.0 → 287.0 for toosendanin and andrographolide, respectively. Good linearity was observed over the concentration range of 3.125-500 ng/mL in 100 μL of rat plasma with a correlation coefficient ˃0.9997. Intra- and inter-assay variabilities were ˂8.5% in plasma. The recovery and the matrix effect were in the range 71.8-73.5% and 96.4-103.8%, respectively. The analyte was stable under various conditions (at room temperature, during freeze-thaw settings, in the autosampler, and under deep-freeze conditions). The method was successfully applied to a pharmacokinetic study of toosendanin after its oral administration in rats at a dose of 10 mg/kg.

DOI: https://doi.org/10.1093/chromsci/bmab135
J Mass Spectrom Adv Clin Lab. 2021 Nov;22 43-49

Automated data-driven mass spectrometry for improved analysis of lipids with dual dissociation techniques.

Seul Kee Byeon, Anil K Madugundu, Akhilesh Pandey.

Lipidomics is an important component of most multi-Omics systems biology studies and is largely driven by mass spectrometry (MS). Because lipids are tight regulators of multiple cellular functions, including energy homeostasis, membrane structures and cell signaling, lipidomics can provide a deeper understanding of variations underlying disease states and can become an even more powerful platform when combined with other omics, including genomics or proteomics. However, data analysis, especially in lipid annotation, poses challenges due to the heterogeneity of functional head groups and fatty acyl chains of varying hydrocarbon lengths and degrees of unsaturation. As there are various MS/MS fragmentation sites in lipids that are class-dependent, obtaining MS/MS data that includes as many fragment ions as possible is critical for structural characterization of lipids in lipidomics workflow. Here, we report an improved lipidomics methodology that resulted in increased coverage of lipidome using: 1) An automated data-driven MS/MS acquisition scheme in which inclusion and exclusion lists were automatically generated from the full scan MS of sample injections, followed by creation of updated lists over iterative analyses; and, 2) Incorporation of dual dissociation techniques of higher-energy collision dissociation and collision-induced dissociation for more accurate characterization of phosphatidylcholine species. Inclusion lists were created automatically based on full scan MS signals from samples and through iterative analyses, ions in the inclusion list that were fragmented were automatically moved to the exclusion list in subsequent runs. We confirmed that analytes with low MS response that did not undergo MS/MS events in conventional data-dependent analysis were successfully fragmented using this approach. Overall, this automated data-driven data acquisition approach resulted in a higher coverage of lipidome and the use of dual dissociation techniques provided additional information that was critical in characterizing the side chains of phosphatidylcholine species.

DOI: https://doi.org/10.1016/j.jmsacl.2021.10.003
EBioMedicine. 2021 Dec 20. pii: S2352-3964(21)00558-2. [Epub ahead of print]75 103764

1H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints.

D Bizzarri, M J T Reinders, M Beekman, P E Slagboom, Bbmri-Nl, E B van den Akker.

  BACKGROUND: Missing or incomplete phenotypic information can severely deteriorate the statistical power in epidemiological studies. High-throughput quantification of small-molecules in bio-samples, i.e. 'metabolomics', is steadily gaining popularity, as it is highly informative for various phenotypical characteristics. Here we aim to leverage metabolomics to impute missing data in clinical variables routinely assessed in large epidemiological and clinical studies.METHODS: To this end, we have employed ∼26,000 1H-NMR metabolomics samples from 28 Dutch cohorts collected within the BBMRI-NL consortium, to create 19 metabolomics-based predictors for clinical variables, including diabetes status (AUC5-Fold CV = 0·94) and lipid medication usage (AUC5-Fold CV = 0·90).
FINDINGS: Subsequent application in independent cohorts confirmed that our metabolomics-based predictors can indeed be used to impute a wide array of missing clinical variables from a single metabolomics data resource. In addition, application highlighted the potential use of our predictors to explore the effects of totally unobserved confounders in omics association studies. Finally, we show that our predictors can be used to explore risk factor profiles contributing to mortality in older participants.
INTERPRETATION: To conclude, we provide 1H-NMR metabolomics-based models to impute clinical variables routinely assessed in epidemiological studies and illustrate their merit in scenarios when phenotypic variables are partially incomplete or totally unobserved.
FUNDING: BBMRI-NL, X-omics, VOILA, Medical Delta and the Dutch Research Council (NWO-VENI).

Keywords:  (1)H-NMR metabolomics; Association studies; Epidemiology; Missing values; Regression models; Surrogate clinical variables

DOI:  https://doi.org/10.1016/j.ebiom.2021.103764
Int J Mol Sci. 2021 Dec 20. pii: 13643. [Epub ahead of print]22(24):

Advances in Lipid Extraction Methods-A Review.

Ramesh Kumar Saini, Parchuri Prasad, Xiaomin Shang, Young-Soo Keum.

  Extraction of lipids from biological tissues is a crucial step in lipid analysis. The selection of appropriate solvent is the most critical factor in the efficient extraction of lipids. A mixture of polar (to disrupt the protein-lipid complexes) and nonpolar (to dissolve the neutral lipids) solvents are precisely selected to extract lipids efficiently. In addition, the disintegration of complex and rigid cell-wall of plants, fungi, and microalgal cells by various mechanical, chemical, and enzymatic treatments facilitate the solvent penetration and extraction of lipids. This review discusses the chloroform/methanol-based classical lipid extraction methods and modern modifications of these methods in terms of using healthy and environmentally safe solvents and rapid single-step extraction. At the same time, some adaptations were made to recover the specific lipids. In addition, the high throughput lipid extraction methodologies used for liquid chromatography-mass spectrometry (LC-MS)-based plant and animal lipidomics were discussed. The advantages and disadvantages of various pretreatments and extraction methods were also illustrated. Moreover, the emerging green solvents-based lipid extraction method, including supercritical CO2 extraction (SCE), is also discussed.

Keywords:  Bligh and Dyer method; Folch method; Soxhlet extraction; green solvents; lipidomics; pre-treatments; supercritical CO2 extraction

DOI:  https://doi.org/10.3390/ijms222413643
Environ Int. 2021 Dec 21. pii: S0160-4120(21)00674-7. [Epub ahead of print]159 107049

Organophosphate ester metabolites in human breast milk determined by online solid phase extraction coupled to high pressure liquid chromatography tandem mass spectrometry.

Mengqin Chen, Jacco Koekkoek, Marja Lamoree.

  The analysis of metabolites of organophosphate esters (OPEs) in human breast milk is essential to evaluate OPE and OPE metabolite exposure of newborns. In the current study, an analytical method which only needs a small amount of breast milk (100 μl) was developed and validated for six diester metabolites and three hydroxylated metabolites applying salt-induced liquid-liquid extraction (SI-LLE) and dispersive solid phase extraction (d-SPE) for sample preparation and online solid phase extraction coupled to high pressure chromatography tandem mass spectrometry (online-SPE-HPLC-MS/MS) for quantitative measurement. The final method consisted of an extraction with formic acid (FA)/acetonitrile (1:200, v/v) and a cleanup with C18 d-SPE. The final extracts were trapped on a C18 cartridge with application of a wash step of 2 ml 0.1% FA milli-Q/methanol (98:2, v/v). Method detection limits (MDLs) ranging from 21.7 ng/l for BBOEHEP to 500 ng/l for BCIPP and average recoveries ranging from 58% for 5-OH-EHDPHP to 120% for BCIPP were achieved. Thirty-three breast milk samples from the LINC (Linking EDCs in maternal Nutrition to Child health) cohort collected in three distinct areas in The Netherlands were analyzed using the validated method. BCEP, BCIPP, BCIPHPP, BDCIPP, and 5-OH-EHDPHP were not detected in any of the samples, while BBOEP was the most frequently detected metabolite with a concentration range of <MDL to l.47 ng/ml, followed by DPhP and BBOEHEP, detected in ranges of <MDL to 0.09 and <MDL to 0.027 ng/ml. The results indicated that OPEs entering the human body are only to a limited extent excreted via breast milk.

Keywords:  Breast milk; LINC; Metabolite; OPEs; Online-SPE

DOI:  https://doi.org/10.1016/j.envint.2021.107049
Diagnostics (Basel). 2021 Nov 25. pii: 2195. [Epub ahead of print]11(12):

Fully Automated Quantitative Measurement of Serum Organic Acids via LC-MS/MS for the Diagnosis of Organic Acidemias: Establishment of an Automation System and a Proof-of-Concept Validation.

Yasushi Ueyanagi, Daiki Setoyama, Daisuke Kawakami, Yuichi Mushimoto, Shinya Matsumoto, Taeko Hotta, Dongchon Kang.

  Gas chromatography-mass spectrometry has been widely used to analyze hundreds of organic acids in urine to provide a diagnostic basis for organic acidemia. However, it is difficult to operate in clinical laboratories on a daily basis due to sample pretreatment processing. Therefore, we aimed to develop a fully automated system for quantifying serum organic acids using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The pretreatment CLAM-2030 device was connected to an LC-MS/MS system for processing serum under optimized conditions, which included derivatizing serum organic acids using 3-Nitrophenylhydrazine. The derivatized organic acids were separated on a reverse-phase Sceptor HD-C column and detected using negative-ion electrospray ionization multiple reaction monitoring MS. The automated pretreatment-LC-MS/MS system processed serum in less than 1 h and analyzed 19 serum organic acids, which are used to detect organic acidemias. The system exhibited high quantitative sensitivity ranging from approximately 2 to 100 µM with a measurement reproducibility of 10.4% CV. Moreover, a proof-of-concept validation of the system was performed using sera from patients with propionic acidemia (n = 5), methylmalonic acidemia (n = 2), and 3-methylcrotonylglycinuria (n = 1). The levels of marker organic acids specific to each disease were significantly elevated in the sera of the patients compared to those in control samples. The automated pretreatment-LC-MS/MS system can be used as a rapid in-hospital system to measure organic acid levels in serum for the diagnosis of organic acidemias.

Keywords:  3-Nitrophenylhydrazine; automated sample preparation; liquid chromatography-mass spectrometry; organic acid analysis; organic acid disorders

DOI:  https://doi.org/10.3390/diagnostics11122195
J Mass Spectrom Adv Clin Lab. 2021 Nov;22 26-33

Rapid identification of plasmalogen molecular species using targeted multiplexed selected reaction monitoring mass spectrometry.

Abul Kalam Azad, Hironori Kobayashi, Abdullah Md Sheikh, Harumi Osago, Hiromichi Sakai, Md Ahsanul Haque, Shozo Yano, Atsushi Nagai.

  Plasmalogens (Pls) levels are reported to be altered in several neurological and metabolic diseases. Identification of sn-1 fatty alcohols and sn-2 fatty acids of different Pls species is necessary to determine the roles and mechanisms of action of Pls in different diseases. Previously, full-scan tandem mass spectrometry (MS/MS) was used for this purpose but is not effective for low-abundance Pls species. Recently, multiplexed selected reaction monitoring MS (SRM/MS) was found to be more selective and sensitive than conventional full-scan MS/MS for the identification of low-abundance compounds. In the present study, we developed a liquid chromatography (LC)-targeted multiplexed SRM/MS system for the identification and quantification of different Pls choline (Pls-PC) and Pls ethanolamine (Pls-PE) species. We determined five precursor-product ion transitions to identify sn-1 and sn-2 fragments of each Pls species. Consequently, sn-1 and sn-2 fatty acyl chains of 22 Pls-PC and 55 Pls-PE species were identified in mouse brain samples. Among them, some species had C20:0 and C20:1 fatty alcohols at the sn-1 position. For quantification of Pls species in mouse brain samples, a single SRM transition was employed. Thus, our results suggest that the LC-targeted multiplexed SRM/MS system is very sensitive for the identification and quantification of low-abundance lipids such as Pls, and is thus expected to make a significant contribution to basic and clinical research in this field in the future.

Keywords:  CS, commercial standard; IS, internal standard; Identification; LC, liquid chromatography; LC-MS/MS; MS/MS, tandem mass spectrometry; MTBE, methyl tert-butyl ether; PLs, glycerophospholipids; PUFAs, polyunsaturated fatty acids; Phospholipids; Plasmalogens; Pls, plasmalogens; Pls-PC, plasmalogens choline; Pls-PE, plasmalogens ethanolamine; Quantification; RT, retention time; SRM, selected reaction monitoring; Targeted multiplexed SRM/MS‘

DOI:  https://doi.org/10.1016/j.jmsacl.2021.09.004
Molecules. 2021 Dec 07. pii: 7427. [Epub ahead of print]26(24):

Magnetic Solid-Phase Extraction Based on Magnetic Sulfonated Reduced Graphene Oxide for HPLC-MS/MS Analysis of Illegal Basic Dyes in Foods.

Shibo Cui, Xinwu Mao, Haijing Zhang, Haowei Zeng, Zihao Lin, Xuewu Zhang, Ping Qi.

  In this study, a magnetic solid-phase extraction (MSPE) method coupled with High-Performance Liquid Chromatography Mass Spectrometry (HPLC-MS/MS) for the determination of illegal basic dyes in food samples was developed and validated. This method was based on Magnetic sulfonated reduced graphene oxide (M-S-RGO), which was sensitive and selective to analytes with structure of multiaromatic rings and negatively charged ions. Several factors affecting MSPE efficiency such as pH and adsorption time were optimized. Under the optimum conditions, the calibration curves exhibited good linearity, ranging from 5 to 60 µg/g with correlation coefficients >0.9950. The limits of detection of 16 basic dyes were in the range of 0.01-0.2 µg/L. The recoveries ranged from 70% to 110% with RSD% < 10%. The results indicate that M-S-RGO is an efficient and selective adsorbent for the extraction and cleanup of basic dyes. Due to the MSPE procedures, matrix effect and interference were eliminated in the analysis of HPLC-MS/MS without the matrix-matched standards. Thus, validation data showed that the proposed MSPE-HPLC-MS/MS method was rapid, efficient, selective, and sensitive for the determination of illegal basic dyes in foods.

Keywords:  HPLC–MS/MS; MSPE method; functionalized graphene oxide; illegal food dyes

DOI:  https://doi.org/10.3390/molecules26247427
Metabolites. 2021 Nov 30. pii: 827. [Epub ahead of print]11(12):

Grape Lipidomics: An Extensive Profiling thorough UHPLC-MS/MS Method.

Domenico Masuero, Domen Škrab, Giulia Chitarrini, Mar Garcia-Aloy, Pietro Franceschi, Paolo Sivilotti, Graziano Guella, Urska Vrhovsek.

  Lipids play many essential roles in living organisms, which accounts for the great diversity of these amphiphilic molecules within the individual lipid classes, while their composition depends on intrinsic and extrinsic factors. Recent developments in mass spectrometric methods have significantly contributed to the widespread application of the liquid chromatography-mass spectrometry (LC-MS) approach to the analysis of plant lipids. However, only a few investigators have studied the extensive composition of grape lipids. The present work describes the development of an ultrahigh performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) method that includes 8098 MRM; the method has been validated using a reference sample of grapes at maturity with a successful analysis and semi-quantification of 412 compounds. The aforementioned method was subsequently applied also to the analysis of the lipid profile variation during the Ribolla Gialla cv. grape maturation process. The partial least squares (PLS) regression model fitted to our experimental data showed that a higher proportion of certain glycerophospholipids (i.e., glycerophosphoethanolamines, PE and glycerophosphoglycerols, PG) and of some hydrolysates from those groups (i.e., lyso-glycerophosphocholines, LPC and lyso-glycerophosphoethanolamines, LPE) can be positively associated with the increasing °Brix rate, while a negative association was found for ceramides (CER) and galactolipids digalactosyldiacylglycerols (DGDG). The validated method has proven to be robust and informative for profiling grape lipids, with the possibility of application to other studies and matrices.

Keywords:  grape; lipidome; lipidomics; liquid chromatography; mass spectrometry

DOI:  https://doi.org/10.3390/metabo11120827
Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 2021 Dec 23. 1-13

Evaluation of rapid and standard tandem mass spectrometric methods to analyse veterinary drugs and their metabolites in antemortem bodily fluids from food animals.

Weilin L Shelver, Shubhashis Chakrabarty, Jennifer M Young, Christopher J Byrd, David J Smith.

  Antemortem bodily fluids can serve as an indicator of veterinary medicine exposure prior to food animal slaughter. A multi-residue, rapid screen electrospray ionisation mass spectrometric (RS-ESI-MS) method was developed to analyse 10 veterinary drugs or metabolites (clenbuterol, erythromycin, flunixin, 5-hydroxyflunixin, meloxicam, ractopamine, ractopamine-glucuronide, salbutamol, tylosin, and zilpaterol) in hog oral fluid and bovine urine. Simple acetonitrile extraction with salting-out was employed to remove the analytes from matrices in less than 30 minutes. Instrumental analysis time was < 1 min/injection. Regression coefficients of matrix-matched calibration curves ranged 0.9743-0.9999 across all compounds with limits of detection ranging from 0.46-108 ng mL-1 for cattle urine and 0.19-64.4 ng mL-1 for hog oral fluid across all analytes. Except for ractopamine-glucuronide, analyte recoveries ranged from 92.7-106% for oral fluid and urine fortified at 30, 100, and 300 ng mL-1, with inter-day variations of < 25%. Ractopamine-glucuronide recovery was 93.3% for oral fluid fortified at 300 ng mL-1. The RS-ESI-MS method accurately identified ractopamine and/or ractopamine-glucuronide in incurred cattle urine with results correlating well with traditional LC-MS/MS and HPLC fluorescence methods. As far as we are aware, this is the first report of the direct quantification of ractopamine-glucuronide from biological matrices without lengthy hydrolysis and cleanup steps.

Keywords:  ESI-MS; LC-MS/MS; cattle urine; drug residue; hog oral fluid; multi-residue method; rapid analysis; veterinary drugs

DOI:  https://doi.org/10.1080/19440049.2021.2006801
J Mass Spectrom Adv Clin Lab. 2021 Nov;22 17-25

A pilot study optimizing metabolomic and lipidomic acquisition in serum for biomarker discovery in nonalcoholic fatty liver disease.

Dandan He, Yang Su, Duanyue Meng, Xinmiao Wang, Jun Wang, Hui Ye.

  Background: The worldwide prevalence of non-alcoholic fatty liver disease (NAFLD) has stimulated work to identify biomarkers and develop effective treatments. Metabolomics is an emerging tool that has been widely applied to discover biomarkers and simultaneously uncover pathological mechanisms. Here, we aim to optimize metabolomic acquisition with the goal of obtaining a systemic metabolic profile to unravel the potential link between dysregulated metabolism and NAFLD.Methods: We analyzed serum samples collected from healthy subjects (n = 8) and NAFLD patients (n = 8) via an integrative analytical workflow using two orthogonal separation modes with T3 and amide columns and two ionization polarity modes on a UPLC-ESI-Q/TOF. Data dependent acquisition was employed for data acquisition. Differentially expressed metabolites and lipids were identified by comparing the collected metabolic and lipidomic profiles between the healthy subjects and NAFLD patients.
Results: The integrative LC-MS/MS analytical workflow employed here features an improved coverage of metabolites and lipids, which leads to the identification of 20 potential biomarkers of NAFLD, including lipids, acylcarnitines, and organic acids.
Conclusions: This pilot study has identified potential biomarkers for NAFLD and revealed corresponding dysregulated metabolic pathways related to NAFLD's occurrence and progression, establishing a molecular basis for NAFLD diagnosis and therapeutic intervention.

Keywords:  ACN, acetonitrile; Acylcarnitines; DGs, diacylglycerols; EICs, extracted ion chromatograms; ESI− and ESI+, ionization polarity modes; FA, Formic acid; FC, fold change; HCC, hepatocellular carcinoma; HFD, high-fat diet; HILIC, hydrophilic interaction chromatography; LE, Leucine enkephalin; LPC, lysophosphatidylcholine; Lipids; MCD, methionine-choline-deficient; MGs, monoacylglycerols; MS, mass spectrometry; Metabolic biomarkers; Metabolomics; NAFLD; NAFLD, non-alcoholic fatty liver disease; NASH, non-alcoholic steatohepatitis; OPLS-DA, orthogonal partial least square discriminant analysis; PCs, phosphorylcholines; PEs, phosphatidylethanolamines; PKC∊, protein kinase C∊; ROC, receiver operating characteristic; RPLC, reversed-phase liquid chromatography; T3-neg, T3 column-based reverse phase separation plus the negative ion mode; T3-pos, T3 column-based reverse phase separation plus the positive ion mode; TIC, total ion chromatogram; VIP, variable importance; amide-neg, amide column-based HILIC separation plus the negative ion mode; amide-pos, amide column-based HILIC separation plus the positive ion mode

DOI:  https://doi.org/10.1016/j.jmsacl.2021.10.001
J Chromatogr Sci. 2021 Dec 22. pii: bmab137. [Epub ahead of print]

Forced Degradation Study of Zanubrutinib: An LC-PDA and LC-MS Approach.

Gowri Gollu, Sowjanya Gummadi, Venkata Deepthi Vemuri, Swathi Nalla, Nalini Mathala, Sasikala Allam.

Zanubrutinib is an unfamiliar second generation selective Brutson's Tyrosine Kinase inhibitor used to treat mantle cell lymphoma. In the present analysis, a new, stability indicating reverse-phase, high performance liquid chromatography method was developed and validated for the determination of Zanubrutinib succeeding degradation studies as pert the International Conference on Harmonization guidelines. The chromatographic separation of Zanubrutinib was achieved in a C18 column (250 × 4.6 mm, 5-μm particle size) using a mobile phase of Acetonitrile: 0.1% Tri Ethyl Amine (65:35 v/v) monitored at 219 nm. The forced degradation studies were conducted by exposing the analyte to acidic, alkaline and neutral hydrolysis, oxidative, reductive, photolytic, and thermal stress conditions and the degradation behavior was studied. The analyte showed degradation under acidic, alkaline, oxidative and reductive stress conditions with additional peaks but, it was stable under neutral, photolytic and thermal stress conditions. The developed method was extended to triple quadruple mass spectrometry to characterize degradation products and to study the fragmentation pattern. Total four degradants were characterized including DP1 in acid &base hydrolysis, DP2 in oxidative and DP3, DP4 in reductive stress condition. As no substantial method was available for quantification of Zanubrutinib and to characterize zanubrutinib degradants, this method can be used for regular analysis in quality control labs.

DOI: https://doi.org/10.1093/chromsci/bmab137
J Pers Med. 2021 Dec 03. pii: 1288. [Epub ahead of print]11(12):

Managing of Unassigned Mass Spectrometric Data by Neural Network for Cancer Phenotypes Classification.

Denis V Petrovsky, Arthur T Kopylov, Vladimir R Rudnev, Alexander A Stepanov, Liudmila I Kulikova, Kristina A Malsagova, Anna L Kaysheva.

  Mass spectrometric profiling provides information on the protein and metabolic composition of biological samples. However, the weak efficiency of computational algorithms in correlating tandem spectra to molecular components (proteins and metabolites) dramatically limits the use of "omics" profiling for the classification of nosologies. The development of machine learning methods for the intelligent analysis of raw mass spectrometric (HPLC-MS/MS) measurements without involving the stages of preprocessing and data identification seems promising. In our study, we tested the application of neural networks of two types, a 1D residual convolutional neural network (CNN) and a 3D CNN, for the classification of three cancers by analyzing metabolomic-proteomic HPLC-MS/MS data. In this work, we showed that both neural networks could classify the phenotypes of gender-mixed oncology, kidney cancer, gender-specific oncology, ovarian cancer, and the phenotype of a healthy person by analyzing 'omics' data in 'mgf' data format. The created models effectively recognized oncopathologies with a model accuracy of 0.95. Information was obtained on the remoteness of the studied phenotypes. The closest in the experiment were ovarian cancer, kidney cancer, and prostate cancer/kidney cancer. In contrast, the healthy phenotype was the most distant from cancer phenotypes and ovarian and prostate cancers. The neural network makes it possible to not only classify the studied phenotypes, but also to determine their similarity (distance matrix), thus overcoming algorithmic barriers in identifying HPLC-MS/MS spectra. Neural networks are versatile and can be applied to standard experimental data formats obtained using different analytical platforms.

Keywords:  bioinformatics; cancer; metabolomics; multiomics data; neural network; proteomics; system biology

DOI:  https://doi.org/10.3390/jpm11121288
J Environ Sci Health A Tox Hazard Subst Environ Eng. 2021 Dec 24. 1-5

Development and validation of a LC-PDA method for methylphenidate analysis in sewage.

Maiara C S Paixão, Jessica Nardi, Charise D Bertol, Natália Freddo, Bruna F Vieira, Vitoria A Rosano, Maria T Friedrich, Luciana G Rossato-Grando.

  Methylphenidate (MPH) is an important emerging pollutant found in effluents and wastewater. Thus, we aimed to develop and validate a method for detection and quantitation of MPH residues in sewage through high performance liquid chromatography coupled with photodiode array detector (LC-PDA). Here we describe a selective, accurate, precise, and valid method for determination of MPH in sewage with a total running time of 10 min, with limits of detection and quantification of 0.27 and 0.92 µg/mL, respectively. MPH retention peak was observed at 5 min. The method was applied to MPH analysis in a sewage sample pretreated with solid phase extraction, obtaining a result of 2.8 µg/L of MPH. Thus, the developed method can be considered feasible to be applied to MPH residual contamination analysis in sewage using a widely available apparatus.

Keywords:  HPLC; Methylphenidate; PDA; emerging pollutants; environmental contamination; method validation; wastewater

DOI:  https://doi.org/10.1080/10934529.2021.2020513
Front Microbiol. 2021 ;12 735878

A Novel Approach to Characterize the Lipidome of Marine Archaeon Nitrosopumilus maritimus by Ion Mobility Mass Spectrometry.

Kai P Law, Wei He, Jianchang Tao, Chuanlun Zhang.

  Archaea are differentiated from the other two domains of life by their biomolecular characteristics. One such characteristic is the unique structure and composition of their lipids. Characterization of the whole set of lipids in a biological system (the lipidome) remains technologically challenging. This is because the lipidome is innately complex, and not all lipid species are extractable, separable, or ionizable by a single analytical method. Furthermore, lipids are structurally and chemically diverse. Many lipids are isobaric or isomeric and often indistinguishable by the measurement of mass or even their fragmentation spectra. Here we developed a novel analytical protocol based on liquid chromatography ion mobility mass spectrometry to enhance the coverage of the lipidome and characterize the conformations of archaeal lipids by their collision cross-sections (CCSs). The measurements of ion mobility revealed the gas-phase ion chemistry of representative archaeal lipids and provided further insights into their attributions to the adaptability of archaea to environmental stresses. A comprehensive characterization of the lipidome of mesophilic marine thaumarchaeon, Nitrosopumilus maritimus (strain SCM1) revealed potentially an unreported phosphate- and sulfate-containing lipid candidate by negative ionization analysis. It was the first time that experimentally derived CCS values of archaeal lipids were reported. Discrimination of crenarchaeol and its proposed stereoisomer was, however, not achieved with the resolving power of the SYNAPT G2 ion mobility system, and a high-resolution ion mobility system may be required for future work. Structural and spectral libraries of archaeal lipids were constructed in non-vendor-specific formats and are being made available to the community to promote research of Archaea by lipidomics.

Keywords:  Nitrosopumilus maritimus; archaea; ion mobility mass spectrometry; lipidomics; thaumarchaeota

DOI:  https://doi.org/10.3389/fmicb.2021.735878
BMC Bioinformatics. 2021 Dec 18. 22(1): 603

GraphOmics: an interactive platform to explore and integrate multi-omics data.

Joe Wandy, Rónán Daly.

  BACKGROUND: An increasing number of studies now produce multiple omics measurements that require using sophisticated computational methods for analysis. While each omics data can be examined separately, jointly integrating multiple omics data allows for deeper understanding and insights to be gained from the study. In particular, data integration can be performed horizontally, where biological entities from multiple omics measurements are mapped to common reactions and pathways. However, data integration remains a challenge due to the complexity of the data and the difficulty in interpreting analysis results.RESULTS: Here we present GraphOmics, a user-friendly platform to explore and integrate multiple omics datasets and support hypothesis generation. Users can upload transcriptomics, proteomics and metabolomics data to GraphOmics. Relevant entities are connected based on their biochemical relationships, and mapped to reactions and pathways from Reactome. From the Data Browser in GraphOmics, mapped entities and pathways can be ranked, sorted and filtered according to their statistical significance (p values) and fold changes. Context-sensitive panels provide information on the currently selected entities, while interactive heatmaps and clustering functionalities are also available. As a case study, we demonstrated how GraphOmics was used to interactively explore multi-omics data and support hypothesis generation using two complex datasets from existing Zebrafish regeneration and Covid-19 human studies.
CONCLUSIONS: GraphOmics is fully open-sourced and freely accessible from https://graphomics.glasgowcompbio.org/ . It can be used to integrate multiple omics data horizontally by mapping entities across omics to reactions and pathways. Our demonstration showed that by using interactive explorations from GraphOmics, interesting insights and biological hypotheses could be rapidly revealed.

Keywords:  Data exploration; Omics integration; Pathway analysis; Reactome; Visualisation

DOI:  https://doi.org/10.1186/s12859-021-04500-1
J Clin Med. 2021 Dec 12. pii: 5813. [Epub ahead of print]10(24):

Rapid Targeted Method of Detecting Abused Piperazine Designer Drugs.

Anna Welz, Marcin Koba, Piotr Kośliński, Joanna Siódmiak.

  Piperazine derivatives belong to the popular psychostimulating compounds from the group of designer drugs. They are an alternative to illegal drugs such as ecstasy and amphetamines. They are being searched by consumers for recreational use due to their stimulating and hallucinogenic effects. Many NPS-related poisonings and deaths have been reported where piperazines have been found. However, a major problem is the potential lack of laboratory confirmation of the involvement of piperazine derivatives in the occurrence of poisoning. Although many methods have been published, piperazine derivatives are not always included in a routine analytical approach or targeted toxicological analysis. There is an increasing need to provide qualitative evidence for the presence of piperazine derivatives and to ensure reproducible quantification. This article describes a new rapid method of detecting piperazine derivatives in biological material, using LC-MS. All target analytes were separated in a 15 min run time and identified based on the precursor ion, at least two product ions, and the retention time. Stable isotopically labeled (SIL) internal standards: BZP-D7, mCPP-D8 and TFMPP-D4 were used for analysis, obtaining the highest level of confidence in the results. The proposed detection method provides the analytical confirmation of poisoning with piperazine designer drugs.

Keywords:  LC-MS; benzylpiperazine derivatives; designer drugs; hallucinogenic effects; phenylpiperazine derivatives; piperazine derivatives; stimulants

DOI:  https://doi.org/10.3390/jcm10245813
Comput Struct Biotechnol J. 2021 ;19 6157-6168

Bacterial species identification using MALDI-TOF mass spectrometry and machine learning techniques: A large-scale benchmarking study.

Thomas Mortier, Anneleen D Wieme, Peter Vandamme, Willem Waegeman.

  Today machine learning methods are commonly deployed for bacterial species identification using MALDI-TOF mass spectrometry data. However, most of the studies reported in literature only consider very traditional machine learning methods on small datasets that contain a limited number of species. In this paper we present benchmarking results on an unprecedented scale for a wide range of machine learning methods, using datasets that contain almost 100,000 spectra and more than 1000 different species. The size and the diversity of the data allow to compare three important identification scenarios that are often not distinguished in literature, i.e., identification for novel biological replicates, novel strains and novel species that are not present in the training data. The results demonstrate that in all three scenarios acceptable identification rates are obtained, but the numbers are typically lower than those reported in studies with a more limited analysis. Using hierarchical classification methods, we also demonstrate that taxonomic information is in general not well preserved in MALDI-TOF mass spectrometry data. For the novel species scenario, we apply for the first time neural networks with Monte Carlo dropout, which have shown to be successful in other domains, such as computer vision, for the detection of novel species.

Keywords:  Bacterial species identification; Extreme classification; Hierarchical classification; MALDI-TOF MS; Machine learning; Neural networks

DOI:  https://doi.org/10.1016/j.csbj.2021.11.004
Molecules. 2021 Dec 11. pii: 7522. [Epub ahead of print]26(24):

Mapping the Chemistry of Hair Strands by Mass Spectrometry Imaging-A Review.

Mai H Philipsen, Emma R Haxen, Auraya Manaprasertsak, Per Malmberg, Emma U Hammarlund.

  Hair can record chemical information reflecting our living conditions, and, therefore, strands of hair have become a potent analytical target within the biological and forensic sciences. While early efforts focused on analyzing complete hair strands in bulk, high spatial resolution mass spectrometry imaging (MSI) has recently come to the forefront of chemical hair-strand analysis. MSI techniques offer a localized analysis, requiring fewer de-contamination procedures per default and making it possible to map the distribution of analytes on and within individual hair strands. Applying the techniques to hair samples has proven particularly useful in investigations quantifying the exposure to, and uptake of, toxins or drugs. Overall, MSI, combined with optimized sample preparation protocols, has improved precision and accuracy for identifying several elemental and molecular species in single strands of hair. Here, we review different sample preparation protocols and use cases with a view to make the methodology more accessible to researchers outside of the field of forensic science. We conclude that-although some challenges remain, including contamination issues and matrix effects-MSI offers unique opportunities for obtaining highly resolved spatial information of several compounds simultaneously across hair surfaces.

Keywords:  hair analysis; mass spectrometry imaging; sample preparation

DOI:  https://doi.org/10.3390/molecules26247522
Bioinformatics. 2021 Dec 24. pii: btab854. [Epub ahead of print]

BATL: Bayesian annotations for targeted lipidomics.

Justin G Chitpin, Anuradha Surendra, Thao T Nguyen, Graeme P Taylor, Hongbin Xu, Irina Alecu, Roberto Ortega, Julianna J Tomlinson, Angela M Crawley, Michaeline McGuinty, Michael G Schlossmacher, Rachel Saunders-Pullman, Miroslava Cuperlovic-Culf, Steffany A L Bennett, Theodore J Perkins.

MOTIVATION: Bioinformatic tools capable of annotating, rapidly and reproducibly, large, targeted lipidomic datasets are limited. Specifically, few programs enable high-throughput peak assessment of liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) data acquired in either selected or multiple reaction monitoring (SRM and MRM) modes.RESULTS: We present here Bayesian Annotations for Targeted Lipidomics (BATL), a Gaussian naïve Bayes classifier for targeted lipidomics that annotates peak identities according to eight features related to retention time, intensity, and peak shape. Lipid identification is achieved by modelling distributions of these eight input features across biological conditions and maximizing the joint posterior probabilities of all peak identities at a given transition. When applied to sphingolipid and glycerophosphocholine SRM datasets, we demonstrate over 95% of all peaks are rapidly and correctly identified.
AVAILABILITY AND IMPLEMENTATION: BATL software is freely accessible online at https://complimet.ca/batl/ and is compatible with Safari, Firefox, Chrome and Edge.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI: https://doi.org/10.1093/bioinformatics/btab854
J Mass Spectrom Adv Clin Lab. 2021 Nov;22 34-42

Stability of lipids in plasma and serum: Effects of temperature-related storage conditions on the human lipidome.

Gregory B Reis, Jon C Rees, Anna A Ivanova, Zsuzsanna Kuklenyik, Nathan M Drew, James L Pirkle, John R Barr.

  Large epidemiological studies often require sample transportation and storage, presenting unique considerations when applying advanced lipidomics techniques. The goal of this study was to acquire lipidomics data on plasma and serum samples stored at potential preanalytical conditions (e.g., thawing, extracting, evaporating), systematically monitoring lipid species for a period of one month. Split aliquots of 10 plasma samples and 10 serum samples from healthy individuals were kept in three temperature-related environments: refrigerator, laboratory benchtop, or heated incubator. Samples were analyzed at six different time points over 28 days using a Bligh & Dyer lipid extraction protocol followed by direct infusion into a lipidomics platform using differential mobility with tandem mass spectrometry. The observed concentration changes over time were evaluated relative to method and inter-individual biological variability. In addition, to evaluate the effect of lipase enzyme levels on concentration changes during storage, we compared corresponding fasting and post-prandial plasma samples collected from 5 individuals. Based on our data, a series of low abundance free fatty acid (FFA), diacylglycerol (DAG), and cholesteryl ester (CE) species were identified as potential analytical markers for degradation. These FFA and DAG species are typically produced by endogenous lipases from numerous triacylglycerols (TAGs), and certain high abundance phosphatidylcholines (PCs). The low concentration CEs, which appeared to increase several fold, were likely mass-isobars from oxidation of other high concentration CEs. Although the concentration changes of the high abundant TAG, PC, and CE precursors remained within method variability, the concentration trends of FFA, DAG, and oxidized CE products should be systematically monitored over time to inform analysts about possible pre-analytical biases due to degradation in the study sample sets.

Keywords:  15-Hp-PGD2, 15-hydroperoxy-prostaglandin D2; CE, Cholesteryl ester; CER, Ceramide; Cholesteryl Ester; DAG, Diacylglycerol; Degradation; FFA, Free Fatty Acid; Fatty Acids; HpETE, hydroperoxyeicosatetraenoic acid; HpODE, hydroperoxyoctadecadienoic acid; Hydrolysis; LPC, Lysophosphatidylcholine; LPE, Lysophosphatidylethanolamine; Lipidomics; LysoPL, Lysophospholipid; Oxidation; PC, Phosphatidylcholine; PE, Phosphatidylethanolamine; PGD2, prostaglandin D2; PL, Phospholipid; PLA1, phospholipase A1; PLA2, phospholipase A2; SM, Sphingomyelin; Stability; TAG, Triacylglycerol; Triglycerides

DOI:  https://doi.org/10.1016/j.jmsacl.2021.10.002
J Mass Spectrom Adv Clin Lab. 2021 Nov;22 50-55

The effect of sample age on the metabolic information extracted from formalin-fixed and paraffin embedded tissue samples using desorption electrospray ionization mass spectrometry imaging.

Olof Gerdur Isberg, Yuchen Xiang, Sigridur Klara Bodvarsdottir, Jon Gunnlaugur Jonasson, Margret Thorsteinsdottir, Zoltan Takats.

  Background: Metabolites, especially lipids, have been shown to be promising therapeutic targets. In conjugation with genes and proteins they can be used to identify phenotypes of disease and support the development of targeted treatments. The majority of clinically collected tissue samples are stored in formalin-fixed and paraffin embedded (FFPE) blocks due to their tissue conservation ability and indefinite storage capacity. For metabolic analysis, however, fresh frozen (FF) samples are currently preferred over FFPE samples due to concerns of metabolic information being lost when preparing the samples. With little or no sample preparation, desorption electrospray ionisation mass spectrometry imaging (DESI-MSI) allows for the study of spatial as well as spectral information. Methods: DESI-MSI analysis was performed on FFPE breast cancer tissue microarray samples from 213 patients collected between the years 1935-2013. Logistic regression (LR) models were built to classify samples based on age and FF samples were used for feature validation. Results: LR models developed on the FFPE samples achieved an average classification accuracy of 96% when predicting their age with a 10-year grouping. Closer examination of the metabolic change over time revealed that the mean signal intensities for the lower mass range (100 - 500 m/z) linearly decrease over time, while the mean intensities for the higher mass range (500 - 900 m/z), remained relatively constant. Conclusions: In our samples, which span over 70 years, sample age has a weak yet quantifiable impact on metabolite content in FFPE samples, while the higher mass range is seemingly unaffected. FFPE samples thus provide an alternative avenue for metabolic analysis of lipids.

Keywords:  DESI-MSI; FFPE; Lipids; Mass spectrometry imaging; Sample age

DOI:  https://doi.org/10.1016/j.jmsacl.2021.10.004