Topic Editors

Department of Pharmacy, University of Salerno, Fisciano, SA, Italy
Department of Pharmaceutical Chemistry and Technology, "Sapienza" University of Rome, 00185 Rome, Italy
Dipartimento di Farmacia/DIFARMA, Università di Salerno, Salerno, Italy

Advances in Separation Methods for Metabolomics and Lipidomics

Abstract submission deadline
closed (31 March 2024)
Manuscript submission deadline
31 May 2024
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Topic Information

Dear Colleagues,

The increasing need for novel analytical approaches with higher selectivity, speed and coverage is of utmost importance in metabolomics and lipidomics. The ability to quantify and identify different metabolites and lipids in biospecimes, such as biofluids, tissues and lysates, is crucial for prognostics and diagnostics purposes, and furthermore, high-throughput techniques in the clinical setting are highly desirable. This Topic will be focused on the development, validation and application of original methodologies based on hyphenated methods, such as UHPLC-MS, GC-MS, CE-MS, aimed at the qualitative–quantitative analysis of metabolites and lipids in complex matrices such as cells, plasma, serum, urine and tissue homogenates. Particular attention will be given to method optimization, validation, identification and quantitation of the profiled metabolites and lipids.

Dr. Eduardo Sommella
Dr. Giulia Mazzoccanti
Dr. Emanuela Salviati
Topic Editors

Keywords

  • instrumental methods
  • metabolites
  • method validation
  • identification
  • quantitation
  • chromatography (LC and GC)
  • electrophoresis mass spectrometry (HRMS and MS/MS)

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Analytica
analytica
- - 2020 15.6 Days CHF 1000 Submit
Biomolecules
biomolecules
5.5 8.3 2011 16.9 Days CHF 2700 Submit
Metabolites
metabolites
4.1 5.3 2011 13.2 Days CHF 2700 Submit
Molecules
molecules
4.6 6.7 1996 14.6 Days CHF 2700 Submit
Separations
separations
2.6 2.5 2014 13.6 Days CHF 2600 Submit

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Published Papers (3 papers)

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25 pages, 5194 KiB  
Article
Multi-Dimensional Liquid Chromatography of Pulse Triacylglycerols with Triple Parallel Mass Spectrometry
by William C. Byrdwell and Hari Kiran Kotapati
Separations 2023, 10(12), 594; https://doi.org/10.3390/separations10120594 - 05 Dec 2023
Viewed by 1385
Abstract
We analyzed ten pulses (the dried seeds of legumes), i.e., baby lima beans, black beans, black-eyed peas, butter beans, cranberry beans, garbanzo beans, green split peas, lentils, navy beans, and pinto beans, using three-dimensional liquid chromatography (3D-LC) with parallel second dimensions, LC × [...] Read more.
We analyzed ten pulses (the dried seeds of legumes), i.e., baby lima beans, black beans, black-eyed peas, butter beans, cranberry beans, garbanzo beans, green split peas, lentils, navy beans, and pinto beans, using three-dimensional liquid chromatography (3D-LC) with parallel second dimensions, LC × (LC + LC). We combined non-aqueous reversed-phase (NARP) chromatography as the first dimension separation, 1D, with argentation UHPLC for separation based on degree and location of unsaturation in the first second dimension, 2D(1), and multi-cycle NARP-UHPLC in the second second dimension, 2D(2). Pulses contained 1.9% to 2.7% lipids, except garbanzo beans, which contained 6.2% lipids. High-resolution, accurate-mass (HRAM) orbitrap mass spectrometry (MS) was used to perform lipidomic analysis of the 2D(2) and percent relative quantification, showing that the most abundant average triacylglycerol (TAG) molecular species across all pulses were PLL at 10.67% and PLLn at 10.45%. Common beans (Phaseolus vulgaris) were clustered together using principal component analysis (PCA), showing the highest levels of linolenic acid, C18:3, in molecular species such as PLnLn, LLnLn, and OLLn, with palmitic (P), C16:0, linoleic (L), 18:2, linolenic (Ln), 18:3, and oleic (O), 18:1, FAs. Calibration curves derived from interweaved sets of regioisomer standards allowed the absolute quantification of 1,2- and 1,3-regioisomers for a subset of TAGs. Full article
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17 pages, 1287 KiB  
Article
Urinary Free Cortisol Determination and Interferences Studies Using Liquid Chromatography Coupled to Tandem Mass Spectrometry after On-Line Solid Phase Extraction Based on TurboflowTM Chromatography
by Fidéline Bonnet-Serrano, Samir Nakib, Corinne Zientek, Laurence Guignat, Jean Guibourdenche, Jerôme Bertherat and Marie-Claude Menet
Metabolites 2023, 13(10), 1063; https://doi.org/10.3390/metabo13101063 - 09 Oct 2023
Viewed by 817
Abstract
(1) A 24 h urinary free cortisol (UFF) is one of the first-line exams recommended for the diagnosis of Cushing’s syndrome. In a hospital hormonology department, this activity can exceed several hundred dosages per week. The UFF is generally determined via an immunoassay [...] Read more.
(1) A 24 h urinary free cortisol (UFF) is one of the first-line exams recommended for the diagnosis of Cushing’s syndrome. In a hospital hormonology department, this activity can exceed several hundred dosages per week. The UFF is generally determined via an immunoassay with an automate using a chemiluminescence or electrochemiluminescence detection system. To increase the cortisol concentration in the analyzed sample, the automated analysis is preceded by urine extraction, which does not prevent there from being some interferences due to other steroids with close structures. (2) This paper describes the development of on-line solid phase extraction coupled to liquid chromatography and mass spectrometry for the analysis of urinary free cortisol. The on-line extraction was based on the TurboflowTM chromatography coupled to the analytical column by two valves, easily available for the laboratories. (3) The choice of the Accucore Polar Premium® analytical column made it possible to avoid analytical interferences with exogenous or endogenous molecules having the same SRM transition (363 → 121) as cortisol. (4) The method was fully validated in the range of clinically relevant concentrations from the lower limit of quantification (LLOQ) to 411.75 nmol·L−1. Full article
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12 pages, 496 KiB  
Protocol
Miniaturization and Automation Protocol of a Urinary Organic Acid Liquid-Liquid Extraction Method on GC-MS
by Masauso Moses Phiri, Elmarie Davoren and Barend Christiaan Vorster
Molecules 2023, 28(15), 5927; https://doi.org/10.3390/molecules28155927 - 07 Aug 2023
Viewed by 821
Abstract
The aim of this study was to improve the extraction method for urinary organic acids by miniaturizing and automating the process. Currently, manual extraction methods are commonly used, which can be time-consuming and lead to variations in test results. To address these issues, [...] Read more.
The aim of this study was to improve the extraction method for urinary organic acids by miniaturizing and automating the process. Currently, manual extraction methods are commonly used, which can be time-consuming and lead to variations in test results. To address these issues, we reassessed and miniaturized the in-house extraction method, reducing the number of steps and the sample-to-solvent volumes required. The evaluated miniaturized method was translated into an automated extraction procedure on a MicroLab (ML) Star (Hamilton Technologies) liquid handler. This was then validated using samples obtained from the ERNDIM External Quality Assurance program. The organic acid extraction method was successfully miniaturized and automated using the Autosampler robot. The linear range for most of the thirteen standard analytes fell between 0 to 300 mg/L in spiked synthetic urine, with low (50 mg/L), medium (100 mg/L), and high (500 mg/L) levels. The correlation coefficient (r) for most analytes was >0.99, indicating a strong relationship between the measured values. Furthermore, the automated extraction method demonstrated acceptable precision, as most organic acids had coefficients of variation (CVs) below 20%. In conclusion, the automated extraction method provided comparable or even superior results compared to the current in-house method. It has the potential to reduce solvent volumes used during extraction, increase sample throughput, and minimize variability and random errors in routine diagnostic settings. Full article
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