Exploring extra dimensions to capture saliva metabolite fingerprints from metabolically healthy and unhealthy obese patients by comprehensive two-dimensional gas chromatography featuring Tandem Ionization mass spectrometry

Autor: Carlo Bicchi, Erica Liberto, Federico Stilo, Stephen E. Reichenbach, Marta Cialiè Rosso, Paolo Marzullo, Chiara Cordero, Gianluca Aimaretti, Chiara Mele, Massimo Collino, Simone Squara, Stefania Mai
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Male
Analyte
Multivariate statistics
Spectrometry
Mass
Electrospray Ionization
Chromatography
Gas
Metabolite
Glucuronates
Lactose
Mass spectrometry
01 natural sciences
Biochemistry
Gas Chromatography-Mass Spectrometry
Analytical Chemistry
Acetylglucosamine
03 medical and health sciences
chemistry.chemical_compound
Comprehensive two-dimensional gas chromatography-time of flight mass spectrometry
Fused data from multiplexed ionization
Saliva metabolome
Untargeted fingerprinting by template matching
Variable ionization energy
Fuzzy Logic
Cyclohexanes
Reference Values
Partial least squares regression
Humans
Urea
Obesity
Saliva
Chromatography
High Pressure Liquid
030304 developmental biology
0303 health sciences
Chromatography
Chemistry
Deoxyribose
010401 analytical chemistry
Esters
Linear discriminant analysis
N-Acetylneuraminic Acid
0104 chemical sciences
Amino Acids
Neutral
Principal component analysis
Solvents
Gas chromatography
Algorithms
Research Paper
Zdroj: Analytical and Bioanalytical Chemistry
ISSN: 1618-2650
1618-2642
Popis: This study examines the information potential of comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC×GC-TOF MS) and variable ionization energy (i.e., Tandem Ionization™) to study changes in saliva metabolic signatures from a small group of obese individuals. The study presents a proof of concept for an effective exploitation of the complementary nature of tandem ionization data. Samples are taken from two sub-populations of severely obese (BMI > 40 kg/m2) patients, named metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO). Untargeted fingerprinting, based on pattern recognition by template matching, is applied on single data streams and on fused data, obtained by combining raw signals from the two ionization energies (12 and 70 eV). Results indicate that at lower energy (i.e., 12 eV), the total signal intensity is one order of magnitude lower compared to the reference signal at 70 eV, but the ranges of variations for 2D peak responses is larger, extending the dynamic range. Fused data combine benefits from 70 eV and 12 eV resulting in more comprehensive coverage by sample fingerprints. Multivariate statistics, principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA) show quite good patient clustering, with total explained variance by the first two principal components (PCs) that increases from 54% at 70 eV to 59% at 12 eV and up to 71% for fused data. With PLS-DA, discriminant components are highlighted and putatively identified by comparing retention data and 70 eV spectral signatures. Within the most informative analytes, lactose is present in higher relative amount in saliva from MHO patients, whereas N-acetyl-D-glucosamine, urea, glucuronic acid γ-lactone, 2-deoxyribose, N-acetylneuraminic acid methyl ester, and 5-aminovaleric acid are more abundant in MUO patients. Visual feature fingerprinting is combined with pattern recognition algorithms to highlight metabolite variations between composite per-class images obtained by combining raw data from individuals belonging to different classes, i.e., MUO vs. MHO. Graphical abstract Electronic supplementary material The online version of this article (10.1007/s00216-020-03008-6) contains supplementary material, which is available to authorized users.
Databáze: OpenAIRE
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