An efficient data-filtering strategy for easy metabolite detection from the direct analysis of a biological fluid using Fourier transform mass spectrometry

An integrated analytical approach based on enhanced fragment ions interrogation and modified Kendrick mass defect filter data mining for in-depth chemical profiling of glucosinolates by ultra-high-pressure liquid chromatography coupled with Orbitrap high resolution mass spectrometry

High resolution mass spectrometry (HRMS)-based analytical technique promotes the discovery and development of new bioactive molecules from natural sources. However, challenges for MS analysis of natural products include their structural diversity, numerous trace components, as well as the interference from complex matrices that limits the rapid detection and identification of all target analytes in the extracts. Herein, we presented an integrated analytical approach to obtain chemical profile of glucosinolates (GLSs) in Eutrema yunnanense, a perennial herb, which is used as a condiment (Wasabi), by ultra-high-pressure liquid chromatography coupled with Orbitrap high resolution mass spectrometry (UHPLC-Orbitrap/HRMS). The intelligent AcquireX deep scan greatly improved the detection efficiency and coverage of data-dependent acquisition (DDA) mode, and enhanced structurally significant product ions interrogation by generating exhaustive MS/MS spectra with more informative fragmentation. Massive HRMS data mining for searching GLSs was then achieved by a modified Kendrick mass defect filter (MKMDF), which enabled the visualization of their homologous characteristics and reduced the complicacy of data post-processing. Ultimately, a total of 175 GLSs were tentatively identified or characterized based on the MS fragmentation patterns, including 52 potentially new compounds among which 37 malonylated GLSs were first discovered. These compounds were then applied to analyse the chemical differentiation between the rhizome and leaf of E. yunnanense. This study provides a feasible approach for screening and confident structure characterization of GLSs and has practical implications for profiling other natural bioactive homologous compounds.

Mass spectrometry-based metabolomics for an in-depth questioning of human health

Today, metabolomics is becoming an indispensable tool to get a more comprehensive analysis of complex living systems, providing insights on multiple aspects of physiology. Although its application in large scale population-based studies is very challenging due to the processing of large sample sets as well as the complexity of data information, its potential to characterize human health is well recognized. Technological advances in metabolomics pave the way for the efficient biomarker discovery of disease etiology, diagnosis and prognosis. Here, different steps of the metabolomics workflow, particularly mass spectrometry-based approaches, are discussed to demonstrate the potential of metabolomics to address biological questioning in human health. First an overview of metabolomics is provided with its interest in human health studies. Analytical development and advances in mass spectrometry instrumentation and computational tools are discussed regarding their application limits. Advancing metabolomics for applicability in human health and large-scale studies is presented and discussed in conclusion.

Comprehensive Screening and Identification of Phillyrin Metabolites in Rats Based on UHPLC-Q-Exactive Mass Spectrometry Combined with Multi-Channel Data Mining

Phillyrin, a well-known bisepoxylignan, has been shown to have many critical pharmacological activities. In this study, a novel strategy for the extensive acquisition and use of data was established based on UHPLC-Q-Exactive mass spectrometry to analyze and identify the in vivo metabolites of phillyrin and to elucidate the in vivo metabolic pathways of phillyrin. Among them, the generation of data sets was mainly due to multichannel data mining methods, such as high extracted ion chromatogram (HEIC), diagnostic product ion (DPI), and neutral loss filtering (NLF). A total of 60 metabolites (including the prototype compound) were identified in positive and negative ion modes based on intuitive and useful data such as the standard's cleavage rule, accurate molecular mass, and chromatographic retention time. The results showed that a series of biological reactions of phillyrin in vivo mainly included methylation, hydroxylation, hydrogenation, sulfonation, glucuronidation, demethylation, and dehydrogenation and their composite reactions. In summary, this study not only comprehensively explained the in vivo metabolism of phillyrin, but also proposed an effective strategy to quickly analyze and identify the metabolites of natural pharmaceutical ingredients in nature.

NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data

Biological processes (like microbial growth & physiological response) are usually dynamic and require the monitoring of metabolic variation at different time-points. Moreover, there is clear shift from case-control (N=2) study to multi-class (N>2) problem in current metabolomics, which is crucial for revealing the mechanisms underlying certain physiological process, disease metastasis, etc. These time-course and multi-class metabolomics have attracted great attention, and data normalization is essential for removing unwanted biological/experimental variations in these studies. However, no tool (including NOREVA 1.0 focusing only on case-control studies) is available for effectively assessing the performance of normalization method on time-course/multi-class metabolomic data. Thus, NOREVA was updated to version 2.0 by (i) realizing normalization and evaluation of both time-course and multi-class metabolomic data, (ii) integrating 144 normalization methods of a recently proposed combination strategy and (iii) identifying the well-performing methods by comprehensively assessing the largest set of normalizations (168 in total, significantly larger than those 24 in NOREVA 1.0). The significance of this update was extensively validated by case studies on benchmark datasets. All in all, NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools. NOREVA can be accessed at https://idrblab.org/noreva/.