bims-metlip 2022-04-24 papers

bims-metlip

Biomed News

on Methods and protocols in metabolomics and lipidomics

Issue of 2022–04–24
twenty-one papers selected by
Sofia Costa, Icahn School of Medicine at Mount Sinai

A Review on Differential Abundance Analysis Methods for Mass Spectrometry-Based Metabolomic Data.
DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data.
Application of a hybrid zwitterionic hydrophilic interaction liquid chromatography column in metabolic profiling studies.
Improvement of analysis throughput for LC/MS assay.
TRACES: A Lightweight Browser for Liquid Chromatography-Multiple Reaction Monitoring-Mass Spectrometry Chromatograms.
Synthesis of Deuterium-Labeled Vitamin D Metabolites as Internal Standards for LC-MS Analysis.
A novel and robust analytical method for the quantification of perospirone in human plasma using an LC-MS/MS system with self-internal standard calibration.
Development of a Metabolite Ratio Rule-Based Method for Automated Metabolite Profiling and Species Differentiation of Four Major Cinnamon Species.
Method for absolute quantification of short chain fatty acids via reverse phase chromatography mass spectrometry.
Development and Application of an LC-MS/MS Untargeted Exposomics Method with a Separated Pooled Quality Control Strategy.
Guide to Metabolomics Analysis: A Bioinformatics Workflow.
Machine Learning-Based Retention Time Prediction of Trimethylsilyl Derivatives of Metabolites.
Rapid determination of 93 banned industrial dyes in beverage, fish, cookie using solid-supported liquid-liquid extraction and ultrahigh-performance liquid chromatography quadrupole orbitrap high-resolution mass spectrometry.
Packed in-tube SPME-LC-MS/MS for fast and straightforward analysis of cannabinoids and metabolites in human urine.
foodMASST a mass spectrometry search tool for foods and beverages.
MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition.
Development and validation of a new liquid chromatography-tandem mass spectrometry assay for the simultaneous quantification of afatinib, dacomitinib, osimertinib, and the active metabolites of osimertinib in human serum.
A simple method for simultaneous determination of organophosphate esters and their diester metabolites in dairy products and human milk by using solid-phase extraction coupled to liquid chromatography-tandem mass spectrometry.
Identification and Spatial Visualization of Dysregulated Bile Acid Metabolism in High-Fat Diet-Fed Mice by Mass Spectral Imaging.
Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite Profiling.
A New Benzyl Phenethanolamine-β-Cyclodextrin Bonded Phase: Chiral Chromatography Performance and Application for LC-MS/MS Analysis of Pesticide Enantiomers in Fruits and Vegetables.

Metabolites. 2022 Mar 30. pii: 305. [Epub ahead of print]12(4):

A Review on Differential Abundance Analysis Methods for Mass Spectrometry-Based Metabolomic Data.

Zhengyan Huang, Chi Wang.

  This review presents an overview of the statistical methods on differential abundance (DA) analysis for mass spectrometry (MS)-based metabolomic data. MS has been widely used for metabolomic abundance profiling in biological samples. The high-throughput data produced by MS often contain a large fraction of zero values caused by the absence of certain metabolites and the technical detection limits of MS. Various statistical methods have been developed to characterize the zero-inflated metabolomic data and perform DA analysis, ranging from simple tests to more complex models including parametric, semi-parametric, and non-parametric approaches. In this article, we discuss and compare DA analysis methods regarding their assumptions and statistical modeling techniques.

Keywords:  differential abundance; mass spectrometry; metabolomics; zero-inflated data

DOI:  https://doi.org/10.3390/metabo12040305
Anal Chem. 2022 Apr 17.

DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data.

Sean M Colby, Christine H Chang, Jessica L Bade, Jamie R Nunez, Madison R Blumer, Daniel J Orton, Kent J Bloodsworth, Ernesto S Nakayasu, Richard D Smith, Yehia M Ibrahim, Ryan S Renslow, Thomas O Metz.

We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface (API) and command-line tool for high-dimensional mass spectrometry data analysis workflows that offers ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, CCS, tandem mass spectra, and isotopic signature. Notably, DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation; algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity, (ii) increase alignment/feature matching confidence among data sets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS metabolomics data to illustrate the advantages of a multidimensional approach in each data processing step.

DOI: https://doi.org/10.1021/acs.analchem.1c05017
J Chromatogr A. 2022 Mar 31. pii: S0021-9673(22)00211-4. [Epub ahead of print]1672 463013

Application of a hybrid zwitterionic hydrophilic interaction liquid chromatography column in metabolic profiling studies.

Artemis Lioupi, Christina Virgiliou, Thomas H Walter, Kerri M Smith, Paul Rainville, Ian D Wilson, Georgios Theodoridis, Helen G Gika.

  Metabolic phenotyping studies using mouse liver extracts as a model, performed on a novel zwitterionic HILIC UHPLC column, which is based on ethylene-bridged hybrid organic/inorganic particles bonded with sulfobetaine groups and packed into column hardware modified with hybrid surface technology are reported. Initially the chromatographic performance was evaluated under different mobile phase conditions using selected metabolite standards. Following optimization of the chromatographic conditions for 88 hydrophilic metabolites both targeted and untargeted profiling analyses were performed on tissue extracts using LC-MS/MS and LC-TOF/MS, respectively. Chromatographic efficiency parameters such as peak resolution, peak shapes, selectivity and precision in retention and peak areas as well as characteristics that are critical for metabolic profiling analysis such as metabolite coverage and retention time distribution were assessed. The hybrid zwitterionic column exhibited efficient chromatographic separations providing analysis of ca 80 hydrophilic metabolites from different chemical classes and polarities. Utilizing a one-dimensional separation both targeted and untargeted profiling provided comprehensive metabolic signatures that enabled the acquisition of the metabolic phenotypes of the tissue extracts.

Keywords:  Hydrophilic interaction chromatography; Metabolic phenotyping; Metabolomics; Multitargeted assay; Polar metabolites; Zwitterionic phases

DOI:  https://doi.org/10.1016/j.chroma.2022.463013
Anal Sci. 2022 Apr;38(4): 633-634

Improvement of analysis throughput for LC/MS assay.

Shoujiro Ogawa.



Keywords:  Analysis throughput; Chemical derivatization; LC/MS/MS; Sample multiplexing method

DOI:  https://doi.org/10.1007/s44211-022-00089-4
Metabolites. 2022 Apr 15. pii: 354. [Epub ahead of print]12(4):

TRACES: A Lightweight Browser for Liquid Chromatography-Multiple Reaction Monitoring-Mass Spectrometry Chromatograms.

Yoshihiro Kita, Suzumi M Tokuoka, Yoshiya Oda, Takao Shimizu.

  In targeted metabolomic analysis using liquid chromatography-multiple reaction monitoring-mass spectrometry (LC-MRM-MS), hundreds of MRMs are performed in a single run, yielding a large dataset containing thousands of chromatographic peaks. Automation tools for processing large MRM datasets have been reported, but a visual review of chromatograms is still critical, as real samples with biological matrices often cause complex chromatographic patterns owing to non-specific, insufficiently separated, isomeric, and isotopic components. Herein, we report the development of new software, TRACES, a lightweight chromatogram browser for MRM-based targeted LC-MS analysis. TRACES provides rapid access to all MRM chromatograms in a dataset, allowing users to start ad hoc data browsing without preparations such as loading compound libraries. As a special function of the software, we implemented a chromatogram-level deisotoping function that facilitates the identification of regions potentially affected by isotopic signals. Using MRM libraries containing precursor and product formulae, the algorithm reveals all possible isotopic interferences in the dataset and generates deisotoped chromatograms. To validate the deisotoping function in real applications, we analyzed mouse tissue phospholipids in which isotopic interference by molecules with different fatty-acyl unsaturation levels is known. TRACES successfully removed isotopic signals within the MRM chromatograms, helping users avoid inappropriate regions for integration.

Keywords:  LC-MRM-MS; deisotoping; phospholipids; software; targeted lipidomics; targeted metabolomics

DOI:  https://doi.org/10.3390/metabo12040354
Molecules. 2022 Apr 09. pii: 2427. [Epub ahead of print]27(8):

Synthesis of Deuterium-Labeled Vitamin D Metabolites as Internal Standards for LC-MS Analysis.

Akiko Nagata, Kazuto Iijima, Ryota Sakamoto, Yuka Mizumoto, Miho Iwaki, Masaki Takiwaki, Yoshikuni Kikutani, Seketsu Fukuzawa, Minami Odagi, Masayuki Tera, Kazuo Nagasawa.

  Blood levels of the vitamin D3 (D3) metabolites 25-hydroxyvitamin D3 (25(OH)D3), 24R,25-dihydroxyvitamin D3, and 1α,25-dihydroxyvitamin D3 (1,25(OH)2D3) are recognized indicators for the diagnosis of bone metabolism-related diseases, D3 deficiency-related diseases, and hypercalcemia, and are generally measured by liquid-chromatography tandem mass spectrometry (LC-MS/MS) using an isotope dilution method. However, other D3 metabolites, such as 20-hydroxyvitamin D3 and lactone D3, also show interesting biological activities and stable isotope-labeled derivatives are required for LC-MS/MS analysis of their concentrations in serum. Here, we describe a versatile synthesis of deuterium-labeled D3 metabolites using A-ring synthons containing three deuterium atoms. Deuterium-labeled 25(OH)D3 (2), 25(OH)D3-23,26-lactone (6), and 1,25(OH)2D3-23,26-lactone (7) were synthesized, and successfully applied as internal standards for the measurement of these compounds in pooled human serum. This is the first quantification of 1,25(OH)2D3-23,26-lactone (7) in human serum.

Keywords:  deuterium labeling; liquid-chromatography tandem mass spectrometry; measurement of vitamin D metabolites in blood; vitamin D

DOI:  https://doi.org/10.3390/molecules27082427
Clin Biochem. 2022 Apr 15. pii: S0009-9120(22)00104-7. [Epub ahead of print]

A novel and robust analytical method for the quantification of perospirone in human plasma using an LC-MS/MS system with self-internal standard calibration.

Jing Ding, Yan Zhang, Shanqi Wan, Liu Yang, Xiaohua Cui, Caiping Zhao, Suo Zhang, Zhuocheng Meng.

   OBJECTIVES: This study aims to establish a novel method for measuring perospirone in human plasma for therapeutic drug monitoring (TDM) by liquid chromatography-mass spectrometry (LC-MS) coupled with an automatic liquid chromatograph mass spectrometer coupler 9500 (LC-MS/MS-Mate 9500), which has been equipped with self-internal standard (SIS) calibration technology.
DESIGN & METHODS: A novel and attractive analytical calibration method designed for perospirone, calibration with SIS, was reported. After protein precipitation with acetonitrile-cyclopentanol (9:1, v/v) containing 1% NH3·H2O, LC-MS quantification of perospirone was performed by multiple reaction monitoring in the positive mode with quantitative and qualitative analysis of the ion pairs m/z 427.30 → 177.15 and 427.30 → 166.15 for perospirone and SIS. Chromatographic separation was accomplished in < 2.0 min on an Hypersil GOLDTM C18 column (2.1 mm × 50 mm, 3.0 μm) using a mobile methanol phase and 0.1% formic acid in water.
RESULTS: This method showed good selectivity because no interfering peaks were observed in the plasma samples during the 2.0-min run time. The calibration curve range was 0.05-20 ng/mL, with a correlation coefficient of ≥ 0.9995. Intraday and interday accuracies were 98.3%-107.9%, respectively, with precision relative standard deviation values of < 10%. The matrix effects ranged from 92.7% to 96.1%, and extraction recoveries were between 97.3% and 108.8%. Finally, this method was successfully applied to routine clinical TDM for 142 patients. The perospirone plasma concentrations of the patients ranged between 0.07 and 10.96 ng/mL.
CONCLUSIONS: This bioanalytical method can be used for the quantification of perospirone in human plasma by LC-MS/MS-Mate 9500 using perospirone itself as the SIS.

Keywords:  Liquid chromatography–tandem mass spectrometry; Perospirone; Plasma; Self-internal standard; Therapeutic drug monitoring

DOI:  https://doi.org/10.1016/j.clinbiochem.2022.04.006
J Agric Food Chem. 2022 Apr 19.

Development of a Metabolite Ratio Rule-Based Method for Automated Metabolite Profiling and Species Differentiation of Four Major Cinnamon Species.

Mengliang Zhang, Yifei Wang, Roderick Moore, Roy Upton, Peter de B Harrington, Pei Chen.

  A metabolomic ratio rule-based classification method was developed and programmed for automated metabolite profiling and differentiation of four major cinnamon species using ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS). The computational program identifies key cinnamon metabolites, including proanthocyanidins, cinnamaldehyde, and coumarin, from test samples through LC-MS data processing and assigns cinnamon species by critical metabolite ratios using a stepwise classification strategy. Further, 100% classification accuracy was achieved on the training sample set through critical ratio optimization, and over 95% accuracy was achieved on the validation sample set. The proposed cinnamon classification method exhibited superior accuracy compared to the metabolomic-based PLS-DA modeling method and offered great value for the authentication of cinnamon samples and evaluation of their potential health benefits.

Keywords:  Cinnamon; authentication; classification; mass spectrometry; proanthocyanidins

DOI:  https://doi.org/10.1021/acs.jafc.2c01245
PLoS One. 2022 ;17(4): e0267093

Method for absolute quantification of short chain fatty acids via reverse phase chromatography mass spectrometry.

Dominique G Bihan, Thomas Rydzak, Madeleine Wyss, Keir Pittman, Kathy D McCoy, Ian A Lewis.

Short chain fatty acids (SCFAs; including acetate, propionate, and butyrate) are an important class of biological molecules that play a major role in modulating host-microbiome interactions. Despite significant research into SCFA-mediated biological mechanisms, absolute quantification of these molecules in their native form by liquid chromatography mass spectrometry is challenging due to their relatively poor chromatographic properties. Herein, we introduce SQUAD, an isotope-based strategy for absolute quantification of SCFAs in complex biological samples. SQUAD uses aniline derivatization in conjunction with isotope dilution and analysis by reverse-phase liquid chromatography mass spectrometry. We show that SQUAD enables absolute quantification of biologically relevant SCFAs in complex biological samples with a lower limit of detection of 40 nM and a lower limit of quantification ranging from 160 nM to 310 nM. We observed an intra- and inter-day precision under 3% (relative standard deviation) and errors in intra- and inter-day accuracy under 10%. To demonstrate this quantification strategy, we analyzed SCFAs in the caecal contents of germ free versus conventionally raised specific pathogen free (SPF) mice. We showed that acetate was the most abundant SCFA in both types of mice and was present at 200-fold higher concentration in the SPF mice. We also illustrated the use of our quantification strategy in in vitro microbial cultures from five different species of bacteria grown in Mueller Hinton media. This study illustrates the diverse SCFA production rates across microbial taxa with acetate production serving as one of the key differentiating factors across the species. In summary, we introduce an isotope dilution strategy for absolute quantification of aniline-dativized SCFAs and illustrate the utility of this approach for microbiome research.

DOI: https://doi.org/10.1371/journal.pone.0267093
Molecules. 2022 Apr 16. pii: 2580. [Epub ahead of print]27(8):

Development and Application of an LC-MS/MS Untargeted Exposomics Method with a Separated Pooled Quality Control Strategy.

Gianfranco Frigerio, Camilla Moruzzi, Rosa Mercadante, Emma L Schymanski, Silvia Fustinoni.

  Pooled quality controls (QCs) are usually implemented within untargeted methods to improve the quality of datasets by removing features either not detected or not reproducible. However, this approach can be limiting in exposomics studies conducted on groups of exposed and nonexposed subjects, as compounds present at low levels only in exposed subjects can be diluted and thus not detected in the pooled QC. The aim of this work is to develop and apply an untargeted workflow for human biomonitoring in urine samples, implementing a novel separated approach for preparing pooled quality controls. An LC-MS/MS workflow was developed and applied to a case study of smoking and non-smoking subjects. Three different pooled quality controls were prepared: mixing an aliquot from every sample (QC-T), only from non-smokers (QC-NS), and only from smokers (QC-S). The feature tables were filtered using QC-T (T-feature list), QC-S, and QC-NS, separately. The last two feature lists were merged (SNS-feature list). A higher number of features was obtained with the SNS-feature list than the T-feature list, resulting in identification of a higher number of biologically significant compounds. The separated pooled QC strategy implemented can improve the nontargeted human biomonitoring for groups of exposed and nonexposed subjects.

Keywords:  exposomics; liquid chromatography tandem mass spectrometry; pooled quality controls; untargeted metabolomics

DOI:  https://doi.org/10.3390/molecules27082580
Metabolites. 2022 Apr 15. pii: 357. [Epub ahead of print]12(4):

Guide to Metabolomics Analysis: A Bioinformatics Workflow.

Yang Chen, En-Min Li, Li-Yan Xu.

  Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach's ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

Keywords:  metabolic pathways summary; metabolomics; metabolomics analysis tools; multi-omics integration algorithms

DOI:  https://doi.org/10.3390/metabo12040357
Biomedicines. 2022 Apr 11. pii: 879. [Epub ahead of print]10(4):

Machine Learning-Based Retention Time Prediction of Trimethylsilyl Derivatives of Metabolites.

Sara M de Cripan, Adrià Cereto-Massagué, Pol Herrero, Andrei Barcaru, Núria Canela, Xavier Domingo-Almenara.

  In gas chromatography-mass spectrometry-based untargeted metabolomics, metabolites are identified by comparing mass spectra and chromatographic retention time with reference databases or standard materials. In that sense, machine learning has been used to predict the retention time of metabolites lacking reference data. However, the retention time prediction of trimethylsilyl derivatives of metabolites, typically analyzed in untargeted metabolomics using gas chromatography, has been poorly explored. Here, we provide a rationalized framework for machine learning-based retention time prediction of trimethylsilyl derivatives of metabolites in gas chromatography. We compared different machine learning paradigms, in addition to exploring the influence of the computational molecular structure representation to train the prediction models: fingerprint class and fingerprint calculation software. Our study challenged predicted retention time when using chemical ionization and electron impact ionization sources in simulated and real cases, demonstrating a good correct identity ranking capability by machine learning, despite observing a limited false identity filtering power in cases where a spectrum or a monoisotopic mass match to multiple candidates. Specifically, machine learning prediction yielded median absolute and relative retention index (relative retention time) errors of 37.1 retention index units and 2%, respectively. In addition, fingerprint class and fingerprint calculation software, as well as the molecular structural similarity between the training and test or real case sets, showed to be critical modulators of the prediction performance. Finally, we leveraged the structural similarity between the training and test or real case set to determine the probability that the prediction error is below a specific threshold. Overall, our study demonstrates that predicted retention time can provide insights into the true structure of unknown metabolites by ranking from the most to the least plausible molecular identity, and sets the guidelines to assess the confidence in metabolite identification using predicted retention time data.

Keywords:  GC-MS; machine-learning; metabolomics; retention index; retention time

DOI:  https://doi.org/10.3390/biomedicines10040879
Food Chem. 2022 Apr 15. pii: S0308-8146(22)00938-4. [Epub ahead of print]388 132976

Rapid determination of 93 banned industrial dyes in beverage, fish, cookie using solid-supported liquid-liquid extraction and ultrahigh-performance liquid chromatography quadrupole orbitrap high-resolution mass spectrometry.

Shubing Chen, Shuang Li, Keyi Fang, Yongjian Wang, Yan Yang, Chao Han, Yan Shen.

  Banned industrial dyes are composed of a large number of chemicals with diverse physical and chemical properties, making their simultaneous determination a challenging task. A one-step extraction and purification of 93 banned industrial dyes from beverage, fish and cookie sample methods was proposed by using solid supported liquid-liquid extraction (SLE). The extract was analyzed by ultrahigh-performance liquid chromatography quadrupole orbitrap high-resolution mass spectrometry (UPLC-Q-Orbitrap-HRMS). The quantitative and qualitative mode adopts Q-Orbitrap-HRMS full scan MS (full scan MS1) and data-dependent MS/MS (dd-MS2) acquisition mode. The mass resolution was screened under 70,000 FWHM for full-scan MS1 and 35,000 FWHM for dd-MS2. Linearity was observed in the range of 0.01 ∼ 0.5 μg/mL and the limits of quantification were 0.04 ∼ 0.2 mg/kg for 93 dyes. The average recoveries were 70.5-105.8%, with RSD ≤ 10%. The proposed method has the ability to simultaneously screen many banned dyes in foods with high throughput, sensitivity and reliability.

Keywords:  Banned industrial dyes; Foods; SLE; UPLC-Q-Orbitrap-HRMS

DOI:  https://doi.org/10.1016/j.foodchem.2022.132976
Electrophoresis. 2022 Apr 16.

Packed in-tube SPME-LC-MS/MS for fast and straightforward analysis of cannabinoids and metabolites in human urine.

Douglas Morisue Sartore, Jose Luiz Costa, Fernando Mauro Lanças, Álvaro José Dos Santos-Neto.

  Cannabinoids are pharmacologically active compounds present in cannabis plants, which have become important research topics in the modern toxicological and medical research fields. Not only is cannabis the most used drug globally, but also cannabinoids have a growing use to treat a series of diseases. Therefore, new, fast, and efficient analytical methods for analyzing these substances in different matrices are demanded. This study developed a new packed-in-tube solid phase microextraction (IT-SPME) method coupled to LC-MS/MS, for the automated microextraction of 7 cannabinoids from human urine. Packed IT-SPME microcolumns were prepared in (508 μm i.d. x 50 mm) stainless-steel hardware; each one required only 12 mg of sorbent phase. Different sorbents were evaluated; fractional factorial design (24-1 ) and a central composite design were employed for microextraction optimization. Under optimized conditions, the developed method was a fast and straightforward approach. Only 250 μL of urine sample was needed, and no hydrolysis was required. The sample pre-treatment included only dilution and centrifugation steps (8 min), while the complete IT-SPME-LC-MS/MS method took another 12 minutes, with a sample throughput of 3 samples h-1 . The developed method presented adequate precision, accuracy and linearity; R2 values ranged from 0.990 to 0.997, in the range of 10 to 1000 ng mL-1 . The lower limits of quantification (LLOQ) varied from 10 to 25 ng mL-1 . Finally, the method was successfully applied to analyze 20 actual urine samples and the IT-SPME microcolumn was re-used over 150 times. This article is protected by copyright. All rights reserved.

Keywords:  Cannabinoids; In-tube solid-phase microextraction (IT-SPME); Liquid chromatography (LC); Tandem mass spectrometry; Urine

DOI:  https://doi.org/10.1002/elps.202100389
NPJ Sci Food. 2022 Apr 20. 6(1): 22

foodMASST a mass spectrometry search tool for foods and beverages.

Kiana A West, Robin Schmid, Julia M Gauglitz, Mingxun Wang, Pieter C Dorrestein.

There is a growing interest in unraveling the chemical complexity of our diets. To help the scientific community gain insight into the molecules present in foods and beverages that we ingest, we created foodMASST, a search tool for MS/MS spectra (of both known and unknown molecules) against a growing metabolomics food and beverage reference database. We envision foodMASST will become valuable for nutrition research and to assess the potential uniqueness of dietary biomarkers to represent specific foods or food classes.

DOI: https://doi.org/10.1038/s41538-022-00137-3
Metabolites. 2022 Mar 22. pii: 276. [Epub ahead of print]12(4):

MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition.

Cheng S Yeung, Tim Beck, Joram M Posma.

  Reviewing the metabolomics literature is becoming increasingly difficult because of the rapid expansion of relevant journal literature. Text-mining technologies are therefore needed to facilitate more efficient literature reviews. Here we contribute a standardised corpus of full-text publications from metabolomics studies and describe the development of two metabolite named entity recognition (NER) methods. These methods are based on Bidirectional Long Short-Term Memory (BiLSTM) networks and each incorporate different transfer learning techniques (for tokenisation and word embedding). Our first model (MetaboListem) follows prior methodology using GloVe word embeddings. Our second model exploits BERT and BioBERT for embedding and is named TABoLiSTM (Transformer-Affixed BiLSTM). The methods are trained on a novel corpus annotated using rule-based methods, and evaluated on manually annotated metabolomics articles. MetaboListem (F1-score 0.890, precision 0.892, recall 0.888) and TABoLiSTM (BioBERT version: F1-score 0.909, precision 0.926, recall 0.893) have achieved state-of-the-art performance on metabolite NER. A training corpus with full-text sentences from >1000 full-text Open Access metabolomics publications with 105,335 annotated metabolites was created, as well as a manually annotated test corpus (19,138 annotations). This work demonstrates that deep learning algorithms are capable of identifying metabolite names accurately and efficiently in text. The proposed corpus and NER algorithms can be used for metabolomics text-mining tasks such as information retrieval, document classification and literature-based discovery and are available from the omicsNLP GitHub repository.

Keywords:  deep learning; named entity recognition; natural language processing

DOI:  https://doi.org/10.3390/metabo12040276
J Chromatogr B Analyt Technol Biomed Life Sci. 2022 Apr 04. pii: S1570-0232(22)00149-0. [Epub ahead of print]1199 123245

Development and validation of a new liquid chromatography-tandem mass spectrometry assay for the simultaneous quantification of afatinib, dacomitinib, osimertinib, and the active metabolites of osimertinib in human serum.

Emi Ishikawa, Yuta Yokoyama, Haruna Chishima, Ouki Kuniyoshi, Itaru Sato, Naoki Nakaya, Hideo Nakajima, Motonori Kimura, Jun Hakamata, Naoya Suehiro, Hideo Nakada, Shinnosuke Ikemura, Aya Jibiki, Hitoshi Kawazoe, Hiroshi Muramatsu, Sayo Suzuki, Tomonori Nakamura.

  Reports on the therapeutic drug monitoring (TDM) of second- and third-generation epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) in non-small cell lung cancer patients are limited and are required to improve the safety of EGFR-TKI therapy. Some EGFR-TKIs have active metabolites with similar or higher potency compared with the parent compounds; thus, monitoring the parent compound as well as its active metabolites is essential for truly effective TDM. In this study, we developed and validated a method that simultaneously quantifies second- and third-generation EGFR-TKIs (afatinib, dacomitinib, and osimertinib) and the active metabolites of osimertinib, AZ5104 and AZ7550, in the human serum using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The clinical application of the method was also evaluated. The analytes were extracted from a 100 μL serum sample using a simple protein precipitation method and analyzed using LC-MS/MS. Excellent linearity of calibration curves was observed at ranges of 2.5-125.0 ng/mL for afatinib, 2.5-125.0 ng/mL for dacomitinib, 4.0-800.0 ng/mL for osimertinib, 1.0-125.0 ng/mL for AZ5104, and 2.5-125.0 ng/mL for AZ7550. The precision and accuracy were below 14.9% and within ± 14.9% of the nominal concentrations, respectively. The mean recovery was higher than 94.7% and the coefficient of variation (CV) was lower than 8.3%. The mean internal-standard normalized matrix factors ranged from 94.6 to 111.9%, and the CVs were lower than 9.7%. This analytical method met the acceptance criteria of the U.S. Food and Drug Administration guidelines. The method was also successfully applied to the analysis of 45 clinical samples; it supports the efficient and valuable analysis for TDM investigations of EGFR-TKIs.

Keywords:  Epidermal growth factor receptor-tyrosine kinase inhibitors; Human serum; LC-MS/MS; Non-small cell lung cancer; Protein precipitation

DOI:  https://doi.org/10.1016/j.jchromb.2022.123245
Anal Bioanal Chem. 2022 Apr 21.

A simple method for simultaneous determination of organophosphate esters and their diester metabolites in dairy products and human milk by using solid-phase extraction coupled to liquid chromatography-tandem mass spectrometry.

Xuelei Chen, Nan Zhang, Liping Li, Rong Zhao, Ning Chen, Sai Fan, Zhixiong Shi.

  Organophosphate esters (OPEs) and their diester metabolites have been frequently found in various environmental matrices and regarded as emerging environmental pollutants, whereas data on their occurrence in foods and human matrices are still limited. In this study, a novel and simple procedure was developed to simultaneously determine 14 OPEs and 6 diester metabolites in dairy products and human milk. After enzymatic hydrolysis by β-glucuronidase/arylsulfatase, a freeze-dried milk sample was extracted with acetonitrile and purified by solid-phase extraction. Subsequently, all target compounds were determined by HPLC-ESI-MS/MS. Linearity, limits of detection (LODs), recovery, precision, and matrix effects of the proposed methodology were validated, and the parameters of HPLC-ESI-MS/MS were optimized. LODs for OPEs and their diester metabolites were from 0.001 to 0.02 ng/mL, and limits of quantification (LOQs) were 0.01-0.3 ng/mL. Average recoveries at two spiked levels ranged between 67.3 and 121%, with relative standard deviation lower than 20.7%. A test for matrix effects showed that most analytes presented signal suppression, and isotopically labeled ISs were essential for compensating for the matrix effects. Finally, OPEs and their metabolites both showed high detecting frequencies in real samples, which indicated that these emerging pollutants were ubiquitous in foods and the human body, and the impact of the diester metabolites on population exposure must be included in exposure assessment.

Keywords:  Dairy products; Diester metabolites; HPLC-ESI-MS/MS; Human milk; Organophosphate esters; Solid-phase extraction

DOI:  https://doi.org/10.1007/s00216-022-04079-3
Front Nutr. 2022 ;9 858603

Identification and Spatial Visualization of Dysregulated Bile Acid Metabolism in High-Fat Diet-Fed Mice by Mass Spectral Imaging.

Qi Zhang, Zhen-Hua Wu, Shan-Shan Zhao, Jing Yang, Lei Chen, Xiao-Yu Wang, Zhan-You Wang, Hui-Xin Liu.

  Changes in overall bile acid (BA) levels and specific BA metabolites are involved in metabolic diseases, gastrointestinal, and liver cancer. BAs have become established as important signaling molecules that enable fine-tuned inter-tissue communication within the enterohepatic circulation. The liver, BAs site of production, displayed physiological and functional zonal differences in the periportal zone versus the centrilobular zone. In addition, BA metabolism shows regional differences in the intestinal tract. However, there is no available method to detect the spatial distribution and molecular profiling of BAs within the enterohepatic circulation. Herein, we demonstrated the application in mass spectrometry imaging (MSI) with a high spatial resolution (3 μm) plus mass accuracy matrix-assisted laser desorption ionization (MALDI) to imaging BAs and N-1-naphthylphthalamic acid (NPA). Our results could clearly determine the zonation patterns and regional difference characteristics of BAs on mouse liver, ileum, and colon tissue sections, and the relative content of BAs based on NPA could also be ascertained. In conclusion, our method promoted the accessibility of spatial localization and quantitative study of BAs on gastrointestinal tissue sections and demonstrated that MALDI-MSI was a valuable tool to investigate and locate several BA molecules in different tissue types leading to a better understanding of the role of BAs behind the gastrointestinal diseases.

Keywords:  MALDI; bile acid; enterohepatic circulation; mass spectrometry imaging (MSI); metabolic disease; zonation pattern

DOI:  https://doi.org/10.3389/fnut.2022.858603
Metabolites. 2022 Mar 24. pii: 283. [Epub ahead of print]12(4):

Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite Profiling.

Daniel Cañueto, Reza M Salek, Mònica Bulló, Xavier Correig, Nicolau Cañellas.

  The quality of automatic metabolite profiling in NMR datasets from complex matrices can be affected by the numerous sources of variability. These sources, as well as the presence of multiple low-intensity signals, cause uncertainty in the metabolite signal parameters. Lineshape fitting approaches often produce suboptimal resolutions to adapt them in a complex spectrum lineshape. As a result, the use of software tools for automatic profiling tends to be restricted to specific biological matrices and/or sample preparation protocols to obtain reliable results. However, the analysis and modelling of the signal parameters collected during initial iteration can be further optimized to reduce uncertainty by generating narrow and accurate predictions of the expected signal parameters. In this study, we show that, thanks to the predictions generated, better profiling quality indicators can be outputted, and the performance of automatic profiling can be maximized. Our proposed workflow can learn and model the sample properties; therefore, restrictions in the biological matrix, or sample preparation protocol, and limitations of lineshape fitting approaches can be overcome.

Keywords:  NMR; automatic profiling; machine learning

DOI:  https://doi.org/10.3390/metabo12040283
J AOAC Int. 2022 Apr 22. pii: qsac047. [Epub ahead of print]

A New Benzyl Phenethanolamine-β-Cyclodextrin Bonded Phase: Chiral Chromatography Performance and Application for LC-MS/MS Analysis of Pesticide Enantiomers in Fruits and Vegetables.

Liang Li, Yongjiu Jin, Yanhong Jin, Zhi Zhang, Zhou Zhu, Haiqun Kou.

   BACKGROUND: At present, the research on achiral drug and pesticide residue detection methods is still the mainstay at home and abroad, and there is still a lack of systematic research on the enantiomeric analysis of chiral drugs and pesticides.
OBJECTIVE: In order to prepare a novel chiral stationary phase, whose "multi-mode" chiral separation chromatographic performance and its utility was verified.
METHODS: An S-(-)-2-benzylamino-1-phenylethanol mono-derivative β-cyclodextrin bonded stationary phase (BzCSP) was prepared based on the "thiol-ene" addition reaction. The chiral compounds including four types of chiral compounds were used as "probes", and their chiral chromatographic properties were evaluated. Furthermore, a new LC-MS/MS method for the determination of the enantiomeric residues of three chiral pesticides in five kinds of fruits and vegetables was established.
RESULTS: The study found that the novel stationary phase was suitable for a variety of chromatographic modes (normal phase mode, reversed-phase mode, polar organic mode). The resolutions of hexaconazole (Hex), tebuconazole (Teb) and triticonazole (Trit) enantiomers could be up to 2.31, 1.68, and 1.48, respectively, within 30 min under reversed-phase chromatography. Based on the optimal chromatographic and mass spectrum conditions, a new LC-MS/MS quantitative method for the Hex, Teb and Trit enantiomers was established by multi-reaction positive ion monitoring (MRM). The detection limits (LODs) of enantiomers were less than 0.89 µg kg-1 for Hex, 0.93 µg kg-1 for Teb and 0.93 µg/kg for Trit, the averaged recoveries of enantiomers were in the range of 75.8-106.3% for Hex, 77.4-116.3% for Teb, and 78.7-113.4% for Trit. The method had good reproducibilities with the RSDs (<5%) for intra-day and (<7%) for inter-day.
CONCLUSION: The established method had the characteristics of good selectivity, high sensitivity, strong resistance to matrix interference, and good reproducibility. It is indicated that the stationary phase prepared by the "thiol-ene" addition reaction is a new type of multi-mode stationary phase, which has a good development value.
HIGHLIGHTS: The study reported a new method for the rapid preparation of a rare "multi-mode" chiral stationary phase BzCSP based on the "thiol-ene" addition reaction and verified the practicability of BzCSP including good selectivity, high sensitivity, strong resistance to matrix interference, and good reproducibility.

Keywords:  LC-MS/MS; chiral pesticides and drugs; enantiomeric separation analysis; thiol-ene addition “Click Chemistry” reaction; “multi-mode” chiral chromatography

DOI:  https://doi.org/10.1093/jaoacint/qsac047