bims-metlip 2020-11-01 papers

bims-metlip

Biomed News

on Methods and protocols in metabolomics and lipidomics

Issue of 2020–11–01
ten papers selected by
Sofia Costa, Cold Spring Harbor Laboratory

Potential of ion mobility-mass spectrometry for both targeted and non-targeted analysis of phase II steroid metabolites in urine.
Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification.
LiPydomics: A Python Package for Comprehensive Prediction of Lipid Collision Cross Sections and Retention Times and Analysis of Ion Mobility-Mass Spectrometry-Based Lipidomics Data.
A UPLC-MRM-MS method for comprehensive profiling of Amadori compound-modified phosphatidylethanolamines in human plasma.
Quantification of mycophenolic acid in human plasma by liquid chromatography with time-of-flight mass-spectrometry for therapeutic drug monitoring.
Pharmacokinetics and tissue distribution study of obtusifolin in rats by liquid chromatography-tandem mass spectrometry.
Liquid-Chromatographic Methods for Carboxylic Acids in Biological Samples.
MALDI-ToF Mass spectra phenomic analysis for human disease diagnosis enabled by cutting-edge data processing pipelines and bioinformatic tools.
Metabolomics Analysis of Viral Therapeutics.
Automated supervised learning pipeline for non-targeted GC-MS data analysis.

Anal Chim Acta X. 2019 Mar;1 100006

Potential of ion mobility-mass spectrometry for both targeted and non-targeted analysis of phase II steroid metabolites in urine.

Maykel Hernández-Mesa, Fabrice Monteau, Bruno Le Bizec, Gaud Dervilly-Pinel.

  In recent years, the commercialization of hybrid ion mobility-mass spectrometers and their integration in traditional LC-MS workflows provide new opportunities to extend the current boundaries of targeted and non-targeted analyses. When coupled to LC-MS, ion mobility spectrometry (IMS) provides a novel characterization parameter, the so-called averaged collision cross section (CCS, Ω), as well as improves method selectivity and sensitivity by the separation of isobaric and isomeric molecules and the isolation of the analytes of interest from background noise. In this work, we have explored the potential and advantages of this technology for carrying out the determination of phase II steroid metabolites (i.e. androgen and estrogen conjugates, including glucuronide and sulfate compounds; n = 25) in urine samples. These molecules have been selected based on their relevance in the fields of chemical food safety and doping control, as well as in metabolomics studies. The influence of urine matrix on the CCS of steroid metabolites was evaluated in order to give more confidence to current CCS databases and support its use as complementary information to retention time (Rt) and mass spectra for compound identification. Samples were only diluted 10-fold with aqueous formic acid (0.1%, v/v) prior analysis. Only an almost insignificant effect of adult bovine urine matrix on the CCS of certain steroid metabolites was observed in comparison with calve urine matrix, which is a less complex sample. In addition, high accuracy was achieved for CCS measurements carried out over four months (ΔCCS < 1.3% for 99.8% of CCS measurements; n = 1806). Interestingly, it has been observed that signal-to-noise (S/N) ratio could be improved at least 2 or 7-fold when IMS is combined with LC-MS. In addition to the separation of isomeric steroid pairs (i.e. etiocholanolone glucuronide and epiandrosterone glucuronide, as well as 19-noretiocholanolone glucuronide and 19-norandrosterone glucuronide), steroid-based ions were also separated in the IMS dimension from co-eluting matrix compounds that presented similar mass-to-charge ratio (m/z). Finally, based on CCS measurements and as a proof of concept, 17α-boldenone glucuronide has been identified as one of the main metabolites resulted from boldione administration to calves.

Keywords:  Collision cross section; Ion mobility-mass spectrometry; Phase II metabolites; Steroids; Urine

DOI:  https://doi.org/10.1016/j.acax.2019.100006
Anal Bioanal Chem. 2020 Oct 29.

Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification.

Thomas Stricker, Ron Bonner, Frédérique Lisaek, Gérard Hopfgartner.

  Annotation and interpretation of full scan electrospray mass spectra of metabolites is complicated by the presence of a wide variety of ions. Not only protonated, deprotonated, and neutral loss ions but also sodium, potassium, and ammonium adducts as well as oligomers are frequently observed. This diversity challenges automatic annotation and is often poorly addressed by current annotation tools. In many cases, annotation is integrated in metabolomics workflows and is based on specific chromatographic peak-picking tools. We introduce mzAdan, a nonchromatography-based multipurpose standalone application that was developed for the annotation and exploration of convolved high-resolution ESI-MS spectra. The tool annotates single or multiple accurate mass spectra using a customizable adduct annotation list and outputs a list of [M+H]+ candidates. MzAdan was first tested with a collection of 408 analytes acquired with flow injection analysis. This resulted in 402 correct [M+H]+ identifications and, with combinations of sodium, ammonium, and potassium adducts and water and ammonia losses within a tolerance of 10 mmu, explained close to 50% of the total ion current. False positives were monitored with mass accuracy and bias as well as chromatographic behavior which led to the identification of adducts with calcium instead of the expected potassium. MzAdan was then integrated in a workflow with XCMS for the untargeted LC-MS data analysis of a 52 metabolite standard mix and a human urine sample. The results were benchmarked against three other annotation tools, CAMERA, findMAIN, and CliqueMS: findMAIN and mzAdan consistently produced higher numbers of [M+H]+ candidates compared with CliqueMS and CAMERA, especially with co-eluting metabolites. Detection of low-intensity ions and correct grouping were found to be essential for annotation performance. Graphical abstract.

Keywords:  Adducts; Electrospray; HRMS; Liquid chromatography; Metabolomics; Software

DOI:  https://doi.org/10.1007/s00216-020-03019-3
Anal Chem. 2020 Oct 29.

LiPydomics: A Python Package for Comprehensive Prediction of Lipid Collision Cross Sections and Retention Times and Analysis of Ion Mobility-Mass Spectrometry-Based Lipidomics Data.

Dylan H Ross, Jang Ho Cho, Rutan Zhang, Kelly M Hines, Libin Xu.

Comprehensive profiling of lipid species in a biological sample, or lipidomics, is a valuable approach to elucidating disease pathogenesis and identifying biomarkers. Currently, a typical lipidomics experiment may track hundreds to thousands of individual lipid species. However, drawing biological conclusions requires multiple steps of data processing to enrich significantly altered features and confident identification of these features. Existing solutions for these data analysis challenges (i.e., multivariate statistics and lipid identification) involve performing various steps using different software applications, which imposes a practical limitation and potentially a negative impact on reproducibility. Hydrophilic interaction liquid chromatography-ion mobility-mass spectrometry (HILIC-IM-MS) has shown advantages in separating lipids through orthogonal dimensions. However, there are still gaps in the coverage of lipid classes in the literature. To enable reproducible and efficient analysis of HILIC-IM-MS lipidomics data, we developed an open-source Python package, LiPydomics, which enables performing statistical and multivariate analyses ("stats" module), generating informative plots ("plotting" module), identifying lipid species at different confidence levels ("identification" module), and carrying out all functions using a user-friendly text-based interface ("interactive" module). To support lipid identification, we assembled a comprehensive experimental database of m/z and CCS of 45 lipid classes with 23 classes containing HILIC retention times. Prediction models for CCS and HILIC retention time for 22 and 23 lipid classes, respectively, were trained using the large experimental data set, which enabled the generation of a large predicted lipid database with 145,388 entries. Finally, we demonstrated the utility of the Python package using Staphylococcus aureus strains that are resistant to various antimicrobials.

DOI: https://doi.org/10.1021/acs.analchem.0c02560
Anal Bioanal Chem. 2020 Oct 27.

A UPLC-MRM-MS method for comprehensive profiling of Amadori compound-modified phosphatidylethanolamines in human plasma.

Xiaobo He, Zhucui Li, Qibin Zhang.

  Phosphatidylethanolamines (PEs) are targets of non-enzymatic glycation, a chemical process that occurs between glucose and primary amine-containing biomolecules. As the early-stage non-enzymatic glycation products of PE, Amadori-PEs are implicated in the pathogenesis of various diseases. However, only a few Amadori-PE molecular species have been identified so far; a comprehensive profiling of these glycated PE species is needed to establish their roles in disease pathology. Herein, based on our previous work using liquid chromatography-coupled neutral loss scanning and product ion scanning tandem mass spectrometry (LC-NLS-MS and LC-PIS-MS) in tandem, we extend identification of Amadori-PE to the low-abundance species, which is facilitated by using plasma lipids glycated in vitro. The confidence of identification is improved by high-resolution tandem mass spectrometry and chromatographic retention time regression. A LC-coupled multiple reaction monitoring mass spectrometry (LC-MRM-MS) assay is further developed for more sensitive quantitation of the Amadori compound-modified lipids. Using synthesized stable isotope-labeled Amadori lipids as internal standards, levels of 142 Amadori-PEs and 33 Amadori-LysoPEs are determined in the NIST human plasma standard reference material. These values may serve as an important reference for future investigations of Amadori-modified lipids in human diseases.

Keywords:  Amadori compound; Human plasma; LC-MRM-MS; LPE; NIST SRM-1950; PE

DOI:  https://doi.org/10.1007/s00216-020-03012-w
Biomed Chromatogr. 2020 Oct 29. e5011

Quantification of mycophenolic acid in human plasma by liquid chromatography with time-of-flight mass-spectrometry for therapeutic drug monitoring.

Natalicia J Antunes, Nina Ince, Jason Raymond, Karin Kipper, Lewis Couchman, David W Holt, Gilberto De Nucci, Atholl Johnston.

  This study presents, for the first time, the development and validation of a liquid chromatography and time-of-flight mass-spectrometry (LC-TOF-MS) based assay to quantify mycophenolic acid (MPA) in patient samples as part of a routine therapeutic drug monitoring service. MPA was extracted from 50 μL human plasma by protein precipitation, using sulindac as internal standard (IS). Separation was obtained on a LunaTM Omega polar C18 column kept at 40 °C. The mobile phase consisted of a mixture of acetonitrile/deionised water (50/50, v/v) with 0.1 % formic acid at a flow rate of 350 μL/min. Analyte and IS were monitored on a TOF-MS using a Jet-StreamTM (electrospray) interface running in positive mode. Assay performance was evaluated by analysing patient plasma (N=69) and external quality assessment (EQA, N=6) samples. The retention times were 2.66 and 2.18 min for MPA and IS, respectively. The lower limit of quantification of MPA was 0.1 μg/mL. The within- and between-assay reproducibility results ranged from 1.81 % to 10.72 %. Patient and EQA sample results were comparable to those obtained previously by an in-house validated LC-MS/MS method. This method showed satisfactory analytical performance for the determination of MPA in plasma over the calibration range of 0.1 μg/mL to 15.0 μg/mL.

Keywords:  LC-TOF-MS; immunosuppressant, therapeutic drug monitoring; mycophenolic acid

DOI:  https://doi.org/10.1002/bmc.5011
Biomed Chromatogr. 2020 Oct 29. e5009

Pharmacokinetics and tissue distribution study of obtusifolin in rats by liquid chromatography-tandem mass spectrometry.

Zhu Huang, Qiao Sun, Wenwen Hao, Junyu Zhao.

This paper reports the validation of an assay for obtusifolin based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) and its application to a preclinical pharmacokinetic study in rats. After sample preparation of plasma and tissue homogenates by protein precipitation, the analyte and internal standard (IS) were separated by a reversed-phase chromatographic system in a run time of 5.0 min and detected by negative ion electrospray ionization (ESI) followed by selected reaction monitoring of the precursor-to-product ion transitions at m/z 283.0-268.1 for obtusifolin and m/z 329.0-314.1 for IS. The assay was linear in the concentration range 1.0-500 ng/mL with the LLOQ of 1.0 ng/mL. In the pharmacokinetic study of an intragastric administration of 1.3 mg/kg obtusifolin, maximum plasma concentration (Cmax ) of obtusifolin was 152.5±62.3 ng/mL reaching at 0.39±0.17 h (Tmax ). The AUC0-t and AUC0-∞ were 491.8±256.7 ng×h/mL and 501.7±256.7 ng×h/mL, respectively, with the t1/2 of 3.1±0.7 h. Obtusifolin was rapidly distributed into tissues, with the highest distribution in the liver and less in the brain. These results will give some insights into the further pharmacological investigation of obtusifolin.

DOI: https://doi.org/10.1002/bmc.5009
Molecules. 2020 Oct 22. pii: E4883. [Epub ahead of print]25(21):

Liquid-Chromatographic Methods for Carboxylic Acids in Biological Samples.

Takuya Fujiwara, Ryoto Inoue, Takuma Ohtawa, Makoto Tsunoda.

  Carboxyl-bearing low-molecular-weight compounds such as keto acids, fatty acids, and other organic acids are involved in a myriad of metabolic pathways owing to their high polarity and solubility in biological fluids. Various disease areas such as cancer, myeloid leukemia, heart disease, liver disease, and lifestyle diseases (obesity and diabetes) were found to be related to certain metabolic pathways and changes in the concentrations of the compounds involved in those pathways. Therefore, the quantification of such compounds provides useful information pertaining to diagnosis, pathological conditions, and disease mechanisms, spurring the development of numerous analytical methods for this purpose. This review article addresses analytical methods for the quantification of carboxylic acids, which were classified into fatty acids, tricarboxylic acid cycle and glycolysis-related compounds, amino acid metabolites, perfluorinated carboxylic acids, α-keto acids and their metabolites, thiazole-containing carboxylic acids, and miscellaneous, in biological samples from 2000 to date. Methods involving liquid chromatography coupled with ultraviolet, fluorescence, mass spectrometry, and electrochemical detection were summarized.

Keywords:  fatty acids; fluorescence; mass spectrometry; perfluorinated carboxylic acids; α-keto acids

DOI:  https://doi.org/10.3390/molecules25214883
Curr Med Chem. 2020 Oct 27.

MALDI-ToF Mass spectra phenomic analysis for human disease diagnosis enabled by cutting-edge data processing pipelines and bioinformatic tools.

Ricardo J Pais, Ray K Iles, Raminta Zmuidinaite.

  Currently, methodologies of human disease diagnosis are still far from capable of rapidly and accurately screening for multiple diseases, simultaneously in a large population, at affordable costs. MALDI-ToF mass spectrometry is an ultra-sensitive, ultra-fast and low-cost high-throughput technology which has a huge potential in clinical laboratory medicine to achieve this goal. Such clinical analysis is starting the first steps towards human phenotype detection and hence phenomic screening for multiple disease states. In this review, we will discuss the main advances achieved so far; putting forward targeted applications of MALDI-ToF mass spectrometry in the service of human disease detection. Here, we will focus mostly on methodological workflow, namely MALDI-ToF data processing for phenomic analysis, using state-of-the-art bioinformatic pipelines and software tools. We will further focus on the role of mathematical modelling, machine learning and artificial intelligence algorithms for disease screening. Moreover, we will present some already developed tools for disease diagnostics and screening based on MALDI-ToF analysis. We will discuss the remaining challenges that are ahead when implementing MALDI-ToF into clinical laboratories. The move from identifying a normal to a single disease phenotype is challenging, but the step towards simultaneously running multiple algorithms screens for multiple different disease phenotype may only be limited by computing power once the first hurdle is passed. The road map to reaching the full potential of human clinical phenomics may be clearer than imagined and give an insight as to the huge benefits this technology may bring for the future of diagnostics.

Keywords:  MALDI ToF; Mass Spectrometry; Predictive Modelling ; bioinformatics; machine learning; phenomics

DOI:  https://doi.org/10.2174/0929867327666201027154257
Methods Mol Biol. 2021 ;2225 179-197

Metabolomics Analysis of Viral Therapeutics.

Haiwei Gu, Xiaojian Shi, Paniz Jasbi, Jeffrey Patterson.

  Virotherapy, enabled by recent advances in the transdisciplinary field of biotechnology, has emerged as a powerful tool for use in anticancer treatment, gene therapy, immunotherapy, etc. Examining the effects of viruses and virus-derived immune-modulating therapeutics is of great fundamental and clinical interest. Here we describe a sample preparation protocol for metabolite extraction from virus-infected tissue, in addition to liquid chromatography-mass spectrometry conditions essential for subsequent analysis. This metabolomics approach delivers highly sensitive and specific metabolite information on various biospecimens. Such an approach may be adopted to monitor biological changes in over 30 relevant metabolic pathways in response to viral infection and also viral therapeutics.

Keywords:  LC-MS/MS; Metabolomics; Sample preparation; Viral vector; Virotherapy

DOI:  https://doi.org/10.1007/978-1-0716-1012-1_10
Anal Chim Acta X. 2019 Mar;1 100005

Automated supervised learning pipeline for non-targeted GC-MS data analysis.

Kimmo Sirén, Ulrich Fischer, Jochen Vestner.

  Non-targeted analysis is nowadays applied in many different domains of analytical chemistry such as metabolomics, environmental and food analysis. Conventional processing strategies for GC-MS data include baseline correction, feature detection, and retention time alignment before multivariate modeling. These techniques can be prone to errors and therefore time-consuming manual corrections are generally necessary. We introduce here a novel fully automated approach to non-targeted GC-MS data processing. This new approach avoids feature extraction and retention time alignment. Supervised machine learning on decomposed tensors of segmented chromatographic raw data signal is used to rank regions in the chromatograms contributing to differentiation between sample classes. The performance of this novel data analysis approach is demonstrated on three published datasets.

Keywords:  Chemometrics; Classification; Exploratory data analysis; Machine learning; Metabolomics; Tensor decomposition

DOI:  https://doi.org/10.1016/j.acax.2019.100005