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Chronic liver diseases affect more than 1 billion people worldwide and represent one of the main public health issues. Nonalcoholic fatty liver disease (NAFLD) accounts for the majority of mortal cases, while there is no currently approved therapeutics for its treatment. One of the prospective approaches to NAFLD therapy is to use a mixture of natural compounds. They showed effectiveness in alleviating NAFLD-related conditions including steatosis, fibrosis, etc. However, understanding the mechanism of action of such mixtures is important for their rational application. In this work, we propose a new dereplication workflow for deciphering the mechanism of action of the lignin-derived natural compound mixture. The workflow combines the analysis of molecular components with high-resolution mass spectrometry, selective chemical tagging and deuterium labeling, liver tissue penetration examination, assessment of biological activity in vitro, and computational chemistry tools used to generate putative structural candidates. Molecular docking was used to propose the potential mechanism of action of these structures, which was assessed by a proteomic experiment.

Direct comparison of high-resolution mass spectrometry (HRMS) data acquired with different instrumentation or parameters remains difficult as the derived lists of molecular species via HRMS, even for the same sample, appear distinct. This inconsistency is the result of inherent inaccuracies caused by instrumental limitations and sample conditions. We propose a method that classifies HRMS data based on the differences in the number of elements between each pair of molecular formulae within the formulae list to preserve the essence of the given sample. The novel metric, Formulae Difference Chains Expected Length (FDCEL), allowed for comparing and classifying samples measured by different instruments. FDCEL metric was implemented for both spectrum quality control and for examination of samples of various nature. We also demonstrate a web application and a prototype for a uniform database for HRMS data serving as a benchmark for future biogeochemical applications.

Mass spectrometry (MS) and isotopes were intertwined for a century, with stable isotopes central to many MS identification and quantification protocols. In contrast, the analytical separations including ion mobility spectrometry (IMS) largely ignored isotopes, partly because of insufficient resolution. We recently delineated various halogenated aniline isomers by structurally specific splitting in FAIMS spectra. While this capability hinges on the 13C shifts, all preceding studies leveraged 37Cl or 81Br to enhance the differentiation. However, such abundant heavy isotopes are absent from typical organic compounds. With single I isotope, iodinated organics generate similar isotopic envelopes dominated by the 13C atoms. Here, we distinguish the three monoiodoaniline isomers based on the shifts solely for one or two 13C atoms. The differentiation may be somewhat improved using multipoint peak position descriptions for more reproducible shifts. The interisomer order of shifts differs from those for chlorinated or brominated analogues, showcasing the specificity of approach. We also investigated the mass scaling of isotopic shifts, encountering divergent trends for different structural families.

Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) provides a unique opportunity for molecular analysis of natural complex mixtures. In many geochemical and environmental studies structure-propertry relations are based solely on the elemental compositional information. Several calculated parameters were proposed to increase reliability of structural attribution, among which aromaticity indices (AI and AImod) are widely used. Herein, we applied a combination of selective labeling reactions in order to obtain direct structural information on the individual components of lignin-derived polyphenolic material. Carboxylic (COOH), carbonyl (C═O), and hydroxyl (OH) groups were enumerated by esterification, reducing, and acetylation reactions, respectively, followed by FTICR MS analyses. Obtained information was enabled to constrain aromaticity accounting for the carbon skeleton only. We found that actual aromaticity of components may be both higher or lower than approximated values depending on the abundance of COOH, C═O, and OH groups. The results are of importance for the geochemical community studying terrestrial NOM with structural gradients.

Mass spectrometry imaging (MSI) has become an important tool for 2D profiling of biological tissues, allowing for the visualization of individual compound distributions in the sample. Based on this information, it is possible to investigate the molecular organization within any particular tissue and detect abnormal regions (such as tumor regions) and many other biologically relevant phenomena. However, the large number of compounds present in the spectra hinders the productive analysis of large MSI datasets when utilizing standard tools. The heterogeneity of samples makes exploratory visualization (a presentation of the general idea of the molecular and structural organization of the inspected tissues) challenging. Here, we explore the application of various dimensionality reduction techniques that have been used extensively in the visualization of hyperspectral images and the MSI data specifically, such as principal component analysis, independent component analysis, non-negative matrix factorization, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection. Further, we propose a new approach based on a combination of structure preserving visualization with nonlinear manifold embedding of normalized spectral data. This way, we aim to preserve as much spatially overlapping signals as possible while augmenting them with information on compositional (spectral) variation. The proposed approach can be used for exploratory visualization of MSI datasets without prior deep chemical or histological knowledge of the sample. Thus, different datasets can be visually compared employing the proposed method. The proposed approach allowed for the clear visualization of the molecular layer, granular layer, and white matter in chimpanzee and macaque cerebellum slices.

Mass spectrometry (MS) and isotopes were intertwined for a century, with stable isotopes central to many MS identification and quantification protocols. In contrast, the analytical separations including ion mobility spectrometry (IMS) largely ignored isotopes, partly because of insufficient resolution. We recently delineated various halogenated aniline isomers by structurally specific splitting in FAIMS spectra. While this capability hinges on the

In Hadamard transform ion mobility spectrometry (HT IMS), the signal-to-noise ratio is always lower for non-modified pseudorandom sequences than for modified sequences. Since the use of non-modified modulating pseudorandom sequences is strategically preferable from a duty cycle standpoint, we investigated the change in the interference signal when transitioning from non-modified modulating sequences to sequences modified by the addition of 1,3,5 and 7 zeros. The interfering signal in HT IMS with modified pseudorandom sequences was shown to be mainly random noise for all the cases except for modifying by incorporation of 1 zero. For standard samples of tetraalkylammonium halides, modulation by non-modified pseudorandom sequences is beneficial in the case of small numbers of averaged spectra (below ∼40 averaged spectra compared to any modified pseudorandom sequences except for 1 zero modified and below ∼200 averaged spectra compared to signal averaging ion mobility spectrometry) and worsens the signal-to-noise ratio in the case of large numbers of averaged spectra. Contrarily, modulation by modified pseudorandom sequences is beneficial for any number of averaged spectra, except for very small ones (below 15 averaged spectra compared to modulation by non-modified sequences). Pseudorandom sequence modified with 1 zero incorporation is beneficial in the case of below ∼400 averaged spectra compared to any modified and non-modified pseudorandom sequences. The signal-to-noise ratio in conventional signal averaging mode ion mobility spectrometry is affected by random noise, whereas the HT IMS with non-modified pseudorandom sequences was demonstrated to be primarily affected by a systematic noise-like artefact signal. Because noise-like artefact signals were found to be reproducible, predicting models for interference signals could be generated to improve signal-to-noise ratio. This is significant because non-modified modulating sequences are limited by their poor signal-to-noise ratio. This improvement would increase the viability of non-modified modulating sequences which are preferred because of their higher sample utilization efficiency.

Background:The aim of this study was to evaluate internal customer satisfaction with the Medical Information Service of Novartis Farmaceutica after changing to a new model of response by email to scientific information requests (the e-Answers initiative).Methods:A survey, conducted 3 months after the e-Answers initiative was implemented, was designed to determine the profile of each respondent and included 10 multiple-choice questions on user satisfaction. A 7-point Likert-type scale was used whenever possible.Results:A total of 97 responses (50.5%) were analyzed. Overall satisfaction with the change was favorable for 94% of respondents, and 94% and 97% of respondents evaluated favorably the elimination of paper load and the decrease in environmental impact, respectively. Satisfaction with response time since implementing the new model was positive for 91% of the respondents.Conclusion:In this anonymous survey with high participation of service users, the e-Answers initiative was evaluated positively.

Mass spectrometry imaging (MSI) with high resolution in mass and space is an analytical method that produces distributions of ions on a sample surface. The algorithms for preprocessing and analysis of the raw data acquired from a mass spectrometer should be evaluated. To do that, the ion composition at every point of the sample should be known. This is possible via the employment of a simulated MSI dataset. In this work, we suggest a pipeline for a robust simulation of MSI datasets that resemble real data with an option to simulate the spectra acquired from any mass spectrometry instrument through the use of the experimental MSI datasets to extract simulation parameters.