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 13 C shifts, all preceding studies leveraged 37 Cl or 81 Br 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 13 C atoms. Here, we distinguish the three monoiodoaniline isomers based on the shifts solely for one or two 13 C 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 AI mod ) 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.

Natural organic matter (NOM) components measured with ultrahigh-resolution mass spectrometry (UHRMS) are often assessed by molecular formula-based indices, particularly related to their aromaticity, which are further used as proxies to explain biogeochemical reactivity. An aromaticity index (AI) is calculated mostly with respect to carboxylic groups abundant in NOM. Here, we propose a new constrained AIcon based on the measured distribution of carboxylic groups among individual NOM components obtained by deuteromethylation and UHRMS. Applied to samples from diverse sources (coal, marine, peat, permafrost, blackwater river, and soil), the method revealed that the most probable number of carboxylic groups was two, which enabled to set a reference point n = 2 for carboxyl-accounted AIcon calculation. The examination of the proposed AIcon showed the smallest deviation to the experimentally determined index for all NOM samples under study as well as for individual natural compounds obtained from the Coconut database. In particular, AIcon performed better than AImod for all compound classes in which aromatic moieties are expected: aromatics, condensed aromatics, and unsaturated compounds. Therefore, AIcon referenced with two carboxyl groups is preferred over conventional AI and AImod for biogeochemical studies where the aromaticity of compounds is important to understand the transformations and fate of NOM compounds.

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.

Direct comparison of high-resolution mass spectrometry (HRMS) data acquired with different instrumentation or parameters remains problematic as the derived lists of molecular species via HRMS, even for the same sample, appear distinct. This inconsistency is caused by inherent inaccuracies associated with instrumental limitations and sample conditions. Hence, experimental data may not reflect a corresponding sample. 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. We also demonstrate a web application and a prototype for a uniform database for HRMS data serving as a benchmark for future biogeochemical and environmental applications. FDCEL metric was successfully employed for both spectrum quality control and examination of samples of various nature.