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Over the years, there has been notable progress in understanding the pathogenesis and treatment modalities of diabetes and its complications, including the application of metabolomics in the study of diabetes, capturing attention from researchers worldwide. Advanced mass spectrometry, including gas chromatography–tandem mass spectrometry (GC-MS/MS), liquid chromatography–tandem mass spectrometry (LC-MS/MS), and ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS), etc., has significantly broadened the spectrum of detectable metabolites, even at lower concentrations. Advanced mass spectrometry has emerged as a powerful tool in diabetes research, particularly in the context of metabolomics. By leveraging the precision and sensitivity of advanced mass spectrometry techniques, researchers have unlocked a wealth of information within the metabolome. This technology has enabled the identification and quantification of potential biomarkers associated with diabetes and its complications, providing new ideas and methods for clinical diagnostics and metabolic studies. Moreover, it offers a less invasive, or even non-invasive, means of tracking disease progression, evaluating treatment efficacy, and understanding the underlying metabolic alterations in diabetes. This paper summarizes advanced mass spectrometry for the application of metabolomics in diabetes mellitus, gestational diabetes mellitus, diabetic peripheral neuropathy, diabetic retinopathy, diabetic nephropathy, diabetic encephalopathy, diabetic cardiomyopathy, and diabetic foot ulcers and organizes some of the potential biomarkers of the different complications with the aim of providing ideas and methods for subsequent in-depth metabolic research and searching for new ways of treating the disease. [ABSTRACT FROM AUTHOR]

Trained sensory panels are regularly used to rate food products but do not allow for data-driven approaches to steer food product development. This study evaluated the potential of a molecular-based strategy by analyzing 27 tomato soups that were enhanced with yeast-derived flavor products using a sensory panel as well as LC-MS and GC-MS profiling. These data sets were used to build prediction models for 26 different sensory attributes using partial least squares analysis. We found driving separation factors between the tomato soups and metabolites predicting different flavors. Many metabolites were putatively identified as dipeptides and sulfur-containing modified amino acids, which are scientifically described as related to umami or having "garlic-like" and "onion-like" attributes. Proposed identities of high-impact sensory markers (methionyl-proline and asparagine-leucine) were verified using MS/MS. The overall results highlighted the strength of combining sensory data and metabolomics platforms to find new information related to flavor perception in a complex food matrix. [ABSTRACT FROM AUTHOR]

The effect of sample injection amount on metabolome analysis in a chemical isotope labeling (CIL) liquid chromatography–mass spectrometry (LC–MS) platform was investigated. The performance of time-of-flight (TOF) mass spectrometers with and without a high-dynamic-range (HD) detection system was compared in the analysis of 12 C 2 / 13 C 2 -dansyl labeled human urine samples. An average of 1635 ± 21 (n = 3) peak pairs or putative metabolites was detected using the HD-TOF-MS, compared to 1429 ± 37 peak pairs from a conventional or non-HD TOF-MS. In both instruments, signal saturation was observed. However, in the HD-TOF-MS, signal saturation was mainly caused by the ionization process, while in the non-HD TOF-MS, it was caused by the detection process. To extend the MS detection range in the non-HD TOF-MS, an automated switching from using 12 C to 13 C-natural abundance peaks for peak ratio calculation when the 12 C peaks are saturated has been implemented in IsoMS, a software tool for processing CIL LC–MS data. This work illustrates that injecting an optimal sample amount is important to maximize the metabolome coverage while avoiding the sample carryover problem often associated with over-injection. A TOF mass spectrometer with an enhanced detection dynamic range can also significantly increase the number of peak pairs detected. Biological significance In chemical isotope labeling (CIL) LC–MS, relative metabolite quantification is done by measuring the peak ratio of a 13 C 2 -/ 12 C 2 -labeled peak pair for a given metabolite present in two comparative samples. The dynamic range of peak ratio measurement does not need to be very large, as only subtle changes of metabolite concentrations are encountered in most metabolomic studies where relative metabolome quantification of different groups of samples is performed. However, the absolute concentrations of different metabolites can be very different, requiring a technique to provide a wide detection dynamic range to allow the detection of as many peak pairs as possible. In this work, we demonstrated that controlling the sample injection amount into LC–MS was critical to achieve the optimal detectability while avoiding sample carry-over problem. In addition, the use of a high-dynamic-range TOF system increased the number of peak pairs detected, compared to a conventional TOF system. We also investigated the ionization and detection saturation factors limiting the dynamic range of detection. This article is part of a Special Issue entitled: Protein dynamics in health and disease. Guest Editors: Pierre Thibault and Anne-Claude Gingras. [ABSTRACT FROM AUTHOR]

Fermented aqueous extracts of Viscum album L. are widely used for cancer treatment in complementary medicine. The high molecular weight compounds viscotoxins and lectins are considered to be the main active substances in the extracts. However, a vast number of small molecules (≤1500 Da) is also expected to be present, and few studies have investigated their identities. In this study, a comprehensive metabolome analysis of samples of fermented aqueous extracts of V. album from two host tree species (Malus domestica and Pinus sylvestris), both prepared by two pharmaceutical manufacturing processes, was performed by liquid chromatography−high resolution tandem mass spectrometry (LC-HRMS/MS). A total of 212 metabolites were putatively annotated, including primary metabolites (e.g., amino acids, organic acids, etc.) and secondary metabolites (mostly phenolic compounds). A clear separation between V. album samples according to the host tree species, but not due to manufacturing processes, was observed by principal component analysis. The biomarkers responsible for this discrimination were assessed by partial least squares−discriminant analysis. Because V. album extracts from different host trees have different clinical applications, the present work highlights the possibility of characterizing the metabolome for identification and traceability of V. album fermented aqueous extracts. [ABSTRACT FROM AUTHOR]