Appropriate degree of trust: deriving confidence metrics for automatic peak assignment in high-resolution mass spectrometry

Detection and Exclusion of False-Positive Molecular Formula Assignments via Mass Error Distributions in UHR Mass Spectra of Natural Organic Matter

Ultrahigh resolution mass spectrometry (UHRMS) routinely detects and identifies thousands of mass peaks in complex mixtures, such as natural organic matter (NOM) and petroleum. The assignment of several chemically plausible molecular formulas (MFs) for a single accurate mass still poses a major problem for the reliable interpretation of NOM composition in a biogeochemical context. Applying sensible chemical rules for MF validation is often insufficient to eliminate multiple assignments (MultiAs)─especially for mass peaks with low abundance or if ample heteroatoms or isotopes are included - and requires manual inspection or expert judgment. Here, we present a new approach based on mass error distributions for the identification of true and false assignments among MultiAs. To this end, we used the mass error in millidalton (mDa), which was superior to the commonly used relative mass error in ppm. We developed an automatic workflow to group MultiAs based on their shared formula units and Kendrick mass defect values and to evaluate the mass error distribution. In this way, the number of valid assignments of chlorinated disinfection byproducts was increased by 8-fold as compared to only applying 37Cl/35Cl isotope ratio filters. Likewise, phosphorus-containing MFs can be differentiated against chlorine-containing MFs with high confidence. Further, false assignments of highly aromatic sulfur-containing MFs ("black sulfur") to sodium adducts in negative ionization mode can be excluded by applying our approach. Overall, MFs for mass peaks that are close to the detection limit or where naturally occurring isotopes are rare (e.g., 15N) or absent (e.g., P and F) can now be validated, substantially increasing the reliability of MF assignments and broadening the applicability of UHRMS analysis to even more complex samples and processes.

Nontargeted homologue series extraction from hyphenated high resolution mass spectrometry data

BACKGROUND

A large proportion of polar anthropogenic compounds routinely released into the environment comprises homologue series, i.e., sets of chemicals differing in a repeating chemical unit. Using analytical techniques such as liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS), these compounds are readily measurable as signal sets with characteristic differences in mass and typically retention time. However, and despite such distinct characteristics, no computational approach for the direct, simultaneous and untargeted detection of all such signal sets comprising both LC and HRMS information has to date been presented.

RESULTS

A fast two-staged approach has been developed to extract LC-HRMS signal patterns which can be indicative of homologous analytes. In a first stage, a k-d tree representation of picked LC-HRMS peaks is used to extract all feasible 3-tuples of peaks with restrictions in, e.g., mass defect differences. A second stage then recombines these 3-tuples to larger series tuples while ensuring smooth changes in their retention time characteristics. This unsupervised approach was evaluated for ten effluent samples from Swiss sewage treatment plants (STPs), in both positive and negative electrospray-ionization.

CONCLUSIONS

Beside recovering all continuous series of previously identified homologues, substantial fractions of nontargeted peaks could subsequently be assigned into very diverse peak series, although assignments were often not unique. The latter ambiguities were resolved by a self-organizing map technique and revealed both distinctive series meshing and rivaling combinatorial solutions in the presence of isobaric or gapped series peaks. When comparing STPs, several ubiquitous yet partially low-frequent series mass differences emerged and may prioritize future identification efforts. The presented algorithm is freely available as part of the R package nontarget and as a user-friendly web-interface at www.envihomolog.eawag.ch.Graphical AbstractSearch for systematic series indicative of homologous compounds is based on a partitioned representation of LC-HRMS signal characteristics. This nontargeted search first extracts series triplets in a nearest-neighbour walk and then recombines them to larger ones. For illustration, the two dimensions involving mass defect characteristics are represented by one only.

Two-dimensional mass defect matrix plots for mapping genealogical links in mixtures of lignin depolymerisation products

Lignin is the second most abundant natural biopolymer, and lignin wastes are therefore potentially significant sources for renewable chemicals such as fuel compounds, as alternatives to fossil fuels. Waste valorisation of lignin is currently limited to a few applications such as in the pulp industry, however, because of the lack of effective extraction and characterisation methods for the chemically highly complex mixtures after decomposition. Here, we have implemented high resolution mass spectrometry and developed two-dimensional mass defect matrix plots as a data visualisation tool, similar to the Kendrick mass defect plots implemented in fields such as petroleomics. These 2D matrix plots greatly simplified the highly convoluted lignin mass spectral data acquired from Fourier transform ion cyclotron resonance (FTICR)–mass spectrometry, and the derived metrics provided confident peak assignments and strongly improved structural mapping of lignin decomposition product series from the various linkages within the lignin polymer after electrochemical decomposition.

Graphical Abstract

A Novel and Intuitive Method of Displaying and Interacting with Mass Difference Information: Application to Oligonucleotide Drug Impurities

A new method is presented for determining relationships between components in complex analytical systems. The method uses the mass differences between peaks in high resolution electrospray ionization (ESI) mass spectra. It relates peaks that share common mass differences. The method is based on the fundamental assumption that peaks in the spectra having the same exact mass difference are related by the same chemical moiety/substructure. Moreover, the presence (or absence/loss) of the same chemical moiety from a series of molecules may reflect similarities in the mechanisms of formation of each molecule. The determined mass differences in the spectra are used to automatically differentiate the types of components in the samples. Contour plots and summary plots of the summed total ion signal as a function of the mass difference are generated, which form powerful tools for the rapid and automated determination of the components in the samples and for comparisons with other samples. For the first time, in this work a unique profile contour plot has been developed that permits the interactive interrogation of the mass range by mass difference data matrix to obtain valuable information about components that share a common mechanism of formation, and all possible mechanisms of formation linked to a selected precursor molecule. The method can be used as an additional and complementary method to the existing analytical methods to determine relationships between components in complex chemical systems.

Distinguishing analyte from noise components in mass spectra of complex samples: where to cut the noise?

Fourier transform mass spectrometry (FTMS) enables comprehensive analysis of complex molecular mixtures. Given the broad intensity ranges of components in the mass spectra, it is imperative to accurately determine a noise threshold level above which peak assignments will be made. Conventionally, to find the threshold level, the "N sigma" approach or an equivalent rule is used. However, the "N sigma" approach cannot be applied to mass spectra stored with partially removed noise (reduced-profile mode). It is also not directly applicable to mass spectra acquired in the absorption mode with removed negative spectral amplitudes. Moreover, N value selection is normally made based on a rule of thumb, meaning that the calculated threshold level may be biased. Here, we present a noise thresholding method which addresses these limitations for analysis of mass spectra of complex molecular mixtures. The introduced data-dependent thresholding method involves analysis of the distribution of logarithmic intensity of all peaks, including noise and analyte, for a given mass spectrum. Selected method applications include FTMS analysis of crude oil fractions as well as tandem MS analysis of intact proteins.

Autophaser: an algorithm for automated generation of absorption mode spectra for FT-ICR MS

Phase correction of Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry data allows the spectra to be presented in absorption mode. Absorption mode spectra offer superior mass resolving power (up to a factor of 2), mass accuracy, and sensitivity over the conventional magnitude mode. Hitherto, the use of absorption mode in FT-ICR mass spectrometry has required either specially adapted instrumentation or a manually intensive process of phase correction or has ignored the potentially significant effects of image charge and the associated frequency shifts. Here we present an algorithm that allows spectra recorded on unadapted FT-ICR mass spectrometers to be phase corrected, their baseline deviations removed, and then an absorption mode spectrum presented in an automated manner that requires little user interaction.