Extending the breadth of saliva metabolome fingerprinting by smart template strategies and effective pattern realignment on comprehensive two-dimensional gas chromatographic data

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOFMS) is one the most powerful analytical platforms for chemical investigations of complex biological samples. It produces large datasets that are rich in information, but highly complex, and its consist...

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Vydáno v:Analytical and bioanalytical chemistry Ročník 415; číslo 13; s. 2493 - 2509
Hlavní autoři: Squara, Simone, Manig, Friederike, Henle, Thomas, Hellwig, Michael, Caratti, Andrea, Bicchi, Carlo, Reichenbach, Stephen E., Tao, Qingping, Collino, Massimo, Cordero, Chiara
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2023
Springer
Springer Nature B.V
Témata:
Algorithms
Analysis
Analytical Chemistry
Biobanks
Biochemistry
Biological properties
Biological samples
Centroids
Characterization and Evaluation of Materials
Chemistry
Chemistry and Materials Science
Chromatography
Comparative analysis
Comprehensive 2D Chromatography
comprehensive two-dimensional gas chromatography
data collection
Data integration
Data processing
Datasets
Domains
Fingerprinting
Fluctuations
Food Science
Gas chromatography
Gas Chromatography-Mass Spectrometry - methods
Humans
Laboratory Medicine
Maillard reaction
Maillard reaction products
Mass spectrometry
Mass spectroscopy
Mathematical analysis
Metabolism
Metabolites
Metabolome
Monitoring/Environmental Analysis
nutritional intervention
Optimization
Pattern recognition
Polynomials
Process parameters
Realignment
Research Paper
Retention
Saliva
Scientific imaging
Signal to noise ratio
Similarity
2D data normalization
UT fingerprinting
Smart template functions
Comprehensive two-dimensional gas chromatography
2D pattern realignment
Saliva metabolomics
ISSN:1618-2642, 1618-2650, 1618-2650
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Shrnutí:Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOFMS) is one the most powerful analytical platforms for chemical investigations of complex biological samples. It produces large datasets that are rich in information, but highly complex, and its consistency may be affected by random systemic fluctuations and/or changes in the experimental parameters. This study details the optimization of a data processing strategy that compensates for severe 2D pattern misalignments and detector response fluctuations for saliva samples analyzed across 2 years. The strategy was trained on two batches: one with samples from healthy subjects who had undergone dietary intervention with high/low-Maillard reaction products (dataset A), and the second from healthy/unhealthy obese individuals (dataset B). The combined untargeted and targeted pattern recognition algorithm (i.e., UT fingerprinting) was tuned for key process parameters, the signal-to-noise ratio (S/N), and MS spectrum similarity thresholds, and then tested for the best transform function (global or local, affine or low-degree polynomial) for pattern realignment in the temporal domain. Reliable peak detection achieved its best performance, computed as % of false negative/positive matches, with a S/N threshold of 50 and spectral similarity direct match factor (DMF) of 700. Cross-alignment of bi-dimensional (2D) peaks in the temporal domain was fully effective with a supervised operation including multiple centroids (reference peaks) and a match-and-transform strategy using affine functions. Regarding the performance-derived response fluctuations, the most promising strategy for cross-comparative analysis and data fusion included the mass spectral total useful signal (MSTUS) approach followed by Z- score normalization on the resulting matrix.
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ISSN:1618-2642
1618-2650
1618-2650
DOI:10.1007/s00216-023-04516-x