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Collision-induced dissociation (CID) is a common fragmentation strategy in tandem mass spectrometry (MS2) analysis. A conventional understanding is that fragment ions generated in low-energy CID should follow the even-electron rule. As such, (de)protonated ([M+H]+/[M−H]−) or even-electron precursor ions should follow heterolytic cleavages and predominately generate even-electron fragment ions with very few radical fragment ions (RFIs). However, the extent to which RFIs present in MS2 spectra has not been comprehensively investigated. This work uses the annotated high-resolution MS2 spectra from the latest NIST 20 tandem mass spectral library to investigate the occurrence of RFIs in CID MS2 experiments. In particular, RFIs were recognized using integer double bond equivalent (DBE) values calculated from their annotated molecular formulas. Our study shows that 65.4% and 68.8% of MS2 spectra of even-electron precursors contain at least 10% RFIs by ion-count (total number of ions) in positive and negative electrospray ionization modes, respectively. Furthermore, we classified chemicals based on their compound classes and chemical substructures, and calculated the percentages of RFIs in each class. As expected, compounds that can stabilize the radical site via resonance, such as aromatic and conjugated double bond-containing chemicals, are more likely to form RFIs. We also found four possible patterns of change in RFI percentages as a function of CID collision energy. Finally, we demonstrate that the inadequate consideration of RFIs in most conventional bioinformatic tools might be problematic during in silico fragmentation and de novo annotation of MS2 spectra. This work provides a further understanding of CID MS2 mechanisms, and the unexpectedly large percentage of RFIs suggests that the even-electron rule seems to be challenged in numerous cases where it is disobeyed. [Display omitted] • NIST20 MS2 spectra were used to study the percentage of radical fragment ions generated from even-electron precursors. • Radical fragment ions were recognized using their integer double bond equivalent values. • Over 60% of CID MS2 spectra of even-electron precursors contain at least 10% radical fragment ions. • The large percentage of radical fragment ions demonstrates the limited scalability of the even-electron rule. • This work suggests considering radical fragment ions in the development of bioinformatic software for MS2 interpretation. [ABSTRACT FROM AUTHOR]

Advancements in computer science and software engineering have greatly facilitated mass spectrometry (MS)-based untargeted metabolomics. Nowadays, gigabytes of metabolomics data are routinely generated from MS platforms, containing condensed structural and quantitative information from thousands of metabolites. Manual data processing is almost impossible due to the large data size. Therefore, in the "omics" era, we are faced with new challenges, the big data challenges of how to accurately and efficiently process the raw data, extract the biological information, and visualize the results from the gigantic amount of collected data. Although important, proposing solutions to address these big data challenges requires broad interdisciplinary knowledge, which can be challenging for many metabolomics practitioners. Our laboratory in the Department of Chemistry at the University of British Columbia is committed to combining analytical chemistry, computer science, and statistics to develop bioinformatics tools that address these big data challenges. In this Feature Article, we elaborate on the major big data challenges in metabolomics, including data acquisition, feature extraction, quantitative measurements, statistical analysis, and metabolite annotation. We also introduce our recently developed bioinformatics solutions for these challenges. Notably, all of the bioinformatics tools and source codes are freely available on GitHub (https://www.github.com/HuanLab), along with revised and regularly updated content. [ABSTRACT FROM AUTHOR]

• A data processing platform, HDPairFinder, was developed to automatically recognize H/D isotope labeled chemicals in HPLC-HRMS data. • Cross-correlation was proposed to address the RT shift caused by the deuterium isotopic effect. • A suite of bioinformatic algorithms was incorporated to effectively remove false positive features. • AMINES, a library of over 38000 amino-containing chemicals, was constructed to facilitate compound annotation. • Over 1000 highly confident H/D-labeled amino-containing compounds can be identified from the data of environmental water samples. The combination of hydrogen/deuterium (H/D) formaldehyde-based isotopic methyl labeling with solid-phase extraction and high-performance liquid chromatography–high resolution mass spectrometry (HPLC-HRMS) is a powerful analytical solution for nontargeted analysis of trace-level amino-containing chemicals in water samples. Given the huge amount of chemical information generated in HPLC-HRMS analysis, identifying all possible H/D-labeled amino chemicals presents a significant challenge in data processing. To address this, we designed a streamlined data processing pipeline that can automatically extract H/D-labeled amino chemicals from the raw HPLC-HRMS data with high accuracy and efficiency. First, we developed a cross-correlation algorithm to correct the retention time shift resulting from deuterium isotopic effects, which enables reliable pairing of H- and D-labeled peaks. Second, we implemented several bioinformatic solutions to remove false chemical features generated by in-source fragmentation, salt adduction, and natural 13C isotopes. Third, we used a data mining strategy to construct the AMINES library that consists of over 38,000 structure-disjointed primary and secondary amines to facilitate putative compound annotation. Finally, we integrated these modules into a freely available R program, HDPairFinder.R. The rationale of each module was justified and its performance tested using experimental H/D-labeled chemical standards and authentic water samples. We further demonstrated the application of HDPairFinder to effectively extract N-containing contaminants, thus enabling the monitoring of changes of primary and secondary N-compounds in authentic water samples. HDPairFinder is a reliable bioinformatic tool for rapid processing of H/D isotopic methyl labeling-based nontargeted analysis of water samples, and will facilitate a better understanding of N-containing chemical compounds in water. [Display omitted] [ABSTRACT FROM AUTHOR]

The repertoire of modifications to bile acids and related steroidal lipids by host and microbial metabolism remains incompletely characterized. To address this knowledge gap, we created a reusable resource of tandem mass spectrometry (MS/MS) spectra by filtering 1.2 billion publicly available MS/MS spectra for bile-acid-selective ion patterns. Thousands of modifications are distributed throughout animal and human bodies as well as microbial cultures. We employed this MS/MS library to identify polyamine bile amidates, prevalent in carnivores. They are present in humans, and their levels alter with a diet change from a Mediterranean to a typical American diet. This work highlights the existence of many more bile acid modifications than previously recognized and the value of leveraging public large-scale untargeted metabolomics data to discover metabolites. The availability of a modification-centric bile acid MS/MS library will inform future studies investigating bile acid roles in health and disease. [Display omitted] • Mining of public, untargeted LC-MS/MS data reveals a large diversity of bile acids • This work created an MS/MS spectral library resource of modified bile acids • Polyamine biosynthetic pathway-derived metabolites amidated to bile acids discovered • American-to-Mediterranean diet transition altered levels of polyamine bile amidates Using mass spectrometry-based tools to mine public untargeted MS/MS data revealed the vastly underappreciated diversity of bile acids hidden in plain sight and led to the discovery of previously unknown polyamine-conjugated bile acids. [ABSTRACT FROM AUTHOR]