Symbolic Aggregate Approximation Improves Gap Filling in High-Resolution Mass Spectrometry Data Processing

Detailed lipid investigation of edible seaweeds by photochemical derivatization and untargeted lipidomics

Seaweeds are macrophytic algae that have been gaining interest as alternative healthy foods, renewable drug sources, and climate change mitigation agents. In terms of their nutritional value, seaweeds are renowned for their high content of biologically active polyunsaturated fatty acids. However, little is known about the regiochemistry-the geometry and position of carbon-carbon double bonds-of free and conjugated fatty acids in seaweeds. In the present work, a detailed characterization of the seaweed lipidome was achieved based on untargeted HRMS-based analysis and lipid derivatization with a photochemical aza-Paternò-Büchi reaction. A triple-data processing strategy was carried out to achieve high structural detail on the seaweed lipidome, i.e., (i) a first data processing workflow with all samples for aligning peak and statistical analysis that led to the definition of lipid sum compositions (e.g., phosphatidylglycerol (PG) 34:1), (ii) a second data processing workflow in which the samples of each seaweed were processed separately to annotate molecular lipids with known fatty acyl isomerism (e.g., PG 16:0_18:1), and (iii) the annotation of lipid regioisomers following MS/MS annotation of the lipid derivatives obtained following the aza-Paternò-Büchi reaction (e.g., PG 16:0_18:1 ω-9). Once the platform was set up, the lipid extracts from 8 seaweed species from different seaweed families were characterized, describing over 900 different lipid species, and information on the regiochemistry of carbon-carbon double bonds uncovered unknown peculiarities of seaweeds belonging to different families. The overall analytical approach helped to fill a gap in the knowledge of the nutritional composition of seaweeds.

Handling of problematic ion chromatograms with the Automated Target Screening (ATS) workflow for unsupervised analysis of high-resolution mass spectrometry data

Liquid chromatography (LC) or gas chromatography (GC) coupled to high-resolution mass spectrometry (HRMS) is a versatile analytical method for the analysis of thousands of chemical pollutants that can be found in environmental and biological samples. While the tools for handling such complex datasets have improved, there are still no fully automated workflows for targeted screening analysis. Here we present an R-based workflow that is able to cope with challenging data like noisy ion chromatograms, retention time shifts, and multiple peak patterns. The workflow can be applied to batches of HRMS data recorded after GC with electron ionization (GC-EI) and LC coupled to electrospray ionization in both negative and positive mode (LC-ESIneg/LC-ESIpos) to perform peak annotation and quantitation fully unsupervised. We used Orbitrap HRMS data of surface water extracts to compare the Automated Target Screening (ATS) workflow with data evaluations performed with the vendor software TraceFinder and the established semi-automated analysis workflow in the MZmine software. The ATS approach increased the overall evaluation performance of the peak annotation compared to the established MZmine module without the need for any post-hoc corrections. The overall accuracy increased from 0.80 to 0.86 (LC-ESIpos), from 0.77 to 0.83 (LC-ESIneg), and from 0.67 to 0.76 (GC-EI). The mean average percentage errors for quantification of ATS were around 30% compared to the manual quantification with TraceFinder. The ATS workflow enables time-efficient analysis of GC- and LC-HRMS data and accelerates and improves the applicability of target screening in studies with a large number of analytes and sample sizes without the need for manual intervention.

Assessing background contamination of sample tubes used in human biomonitoring by non-targeted liquid chromatography-high resolution mass spectrometry

Controlling and minimising background contamination is crucial for maintaining a high quality of samples in human biomonitoring targeting organic chemicals. We assessed the contamination of three previous types and one newly introduced medical-grade type of sample tubes used for storing human body fluids at the German Environmental Specimen Bank. Aqueous extracts from these tubes were analysed by non-targeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) before and after a dedicated cleaning procedure. After peak detection using MZmine, Bayesian hypothesis testing was used to group peaks into those originating either from instrumental and laboratory background contamination, or actual tube contaminants, based on if their peak height was reduced, increased or not affected by the cleaning procedure. For all four tube types 80-90% of the 2475 peaks (1549 in positive and 926 in negative mode) were assigned to laboratory/instrumental background, which we have to consider as potential sample tube contaminants. Among the tube contaminants, results suggest a considerable difference in the contaminant peak inventory and the absolute level of contamination among the different sample tube types. The cleaning procedure did not affect the largest fraction of peaks (50-70%). For the medical grade tubes, the removal of contaminants by the cleaning procedure was strongest compared to the previous tubes, but in all cases a small fraction increased in intensity after cleaning, probably due to a release of oligomers or additives. The identified laboratory background contaminants were mainly semi-volatile polymer additives such as phthalates and phosphate esters. A few compounds could be assigned solely as tube-specific contaminants, such as N,N-dibutylformamide and several constituents of the oligomeric light stabiliser Tinuvin-622. A cleaning procedure before use is an effective way to standardise the used sample tubes and minimises the background contamination, and therefore increases sample quality and therewith analytical results.

Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software

BACKGROUND

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS).

AIM OF REVIEW

This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.

Augmentation of field fluorescence measures for improved in situ contaminant detection

This research proposes a new method that fuses data from the field and lab-based optical measures coupled with machine learning algorithms to quantify the concentrations of toxic contaminants found in fuels and oil sands process-affected water. Selected pairs of excitation/emission intensities at key wavelengths are inputs to an augmentation neural network (NN), trained using lab-based measurements, that generates synthetic high-resolution spectra. Then, an image processing NN is used to estimate the contaminant concentrations from the spectra generated from a few key wavelengths. The presented approach is tested using naphthenic acids, phenol, fluoranthene and pyrene spiked into natural waters. The spills or loss of containment of these contaminants represent a significant risk to the environment and public health, requiring accurate and rapid detection methods to protect the surrounding aquatic environment. Results were compared with models based on only the corresponding peak intensities of each contaminant and with an image processing NN using the original spectra. Naphthenic acids, fluoranthene and pyrene were easy to detect by all methods; however, performance for more challenging signals to identify, such as phenol, was optimized by the proposed method (peak picking with mean absolute error (MAE) of 30.48 µg/L, generated excitation-emission matrix with MAE of 8.30 µg/L). Results suggested that data fusion and machine learning techniques can improve the detection of contaminants in the aquatic environment at environmentally relevant concentrations.

An analytical pipeline to support robust research on the ecology, evolution, and function of floral volatiles

Research on floral volatiles has grown substantially in the last 20 years, which has generated insights into their diversity and prevalence. These studies have paved the way for new research that explores the evolutionary origins and ecological consequences of different types of variation in floral scent, including community-level, functional, and environmentally induced variation. However, to address these types of questions, novel approaches are needed that can handle large sample sizes, provide quality control measures, and make volatile research more transparent and accessible, particularly for scientists without prior experience in this field. Drawing upon a literature review and our own experiences, we present a set of best practices for next-generation research in floral scent. We outline methods for data collection (experimental designs, methods for conducting field collections, analytical chemistry, compound identification) and data analysis (statistical analysis, database integration) that will facilitate the generation and interpretation of quality data. For the intermediate step of data processing, we created the R package bouquet, which provides a data analysis pipeline. The package contains functions that enable users to convert chromatographic peak integrations to a filtered data table that can be used in subsequent statistical analyses. This package includes default settings for filtering out non-floral compounds, including background contamination, based on our best-practice guidelines, but functions and workflows can be easily customized as necessary. Next-generation research into the ecology and evolution of floral scent has the potential to generate broadly relevant insights into how complex traits evolve, their genomic architecture, and their consequences for ecological interactions. In order to fulfill this potential, the methodology of floral scent studies needs to become more transparent and reproducible. By outlining best practices throughout the lifecycle of a project, from experimental design to statistical analysis, and providing an R package that standardizes the data processing pipeline, we provide a resource for new and seasoned researchers in this field and in adjacent fields, where high-throughput and multi-dimensional datasets are common.