Metabolomics Data Processing Using OpenMS

Local Asymmetric Gaussian Fitting Algorithm for Enhanced Peak Detection of Liquid Chromatography-High Resolution Mass Spectrometry Data

Feature detection is a crucial step in the data preprocessing workflow of liquid chromatography-mass spectrometry (LC-MS). However, many existing methods are hindered by intricate parameter adjustments and high false positive rates during extracted ion chromatogram (EIC) construction and peak detection, which challenges the identification of spurious and missing compounds. This study introduces a novel algorithm, local asymmetric Gaussian fitting (LAGF), for peak detection. LAGF integrates with the "data points bins" EIC extraction algorithm to enhance the feature detection efficiency. By using a 1 Da data points bin for EIC extraction, computational time is significantly reduced, making the method well-suited for batch metabolomics analysis. LAGF minimizes parameter numbers of generalized two-sided asymmetric Gaussian fitting by automatically determining the peak center (μ) and height (α) while accommodating two-sided standard deviations (σ1 and σ2) to self-adaptively model peak patterns. Features are filtered based on a goodness-of-fit threshold of 0.5. The performance of LAGF was validated using standard mixtures and serum samples at different concentrations in reversed-phase or hydrophilic interaction LC mode. In most cases, LAGF outperformed conventional tools in terms of determination coefficient (R2) and relative standard deviation for automatically detected peak areas. The LAGF algorithm is available as open-source Python code alongside an interactive interface, facilitating implementation in both nontargeted and targeted LC-MS analysis to enhance peak detection and compound identification.

Improved Protein and PTM Characterization with a Practical Electron-Based Fragmentation on Q-TOF Instruments

Electron-based dissociation (ExD) produces uncluttered mass spectra of intact proteins while preserving labile post-translational modifications. However, technical challenges have limited this option to only a few high-end mass spectrometers. We have developed an efficient ExD cell that can be retrofitted in less than an hour into current LC/Q-TOF instruments. Supporting software has been developed to acquire, process, and annotate peptide and protein ExD fragmentation spectra. In addition to producing complementary fragmentation, ExD spectra enable many isobaric leucine/isoleucine and isoaspartate/aspartate pairs to be distinguished by side-chain fragmentation. The ExD cell preserves phosphorylation and glycosylation modifications. It also fragments longer peptides more efficiently to reveal signaling cross-talk between multiple post-translational modifications on the same protein chain and cleaves disulfide bonds in cystine knotted proteins and intact antibodies. The ability of the ExD cell to combine collisional activation with electron fragmentation enables more complete sequence coverage by disrupting intramolecular electrostatic interactions that can hold fragments of large peptides and proteins together. These enhanced capabilities made possible by the ExD cell expand the size of peptides and proteins that can be analyzed as well as the analytical certainty of characterizing their post-translational modifications.

Mass spectrometry based untargeted metabolomics for plant systems biology

Untargeted metabolomics enables the identification of key changes to standard pathways, but also aids in revealing other important and possibly novel metabolites or pathways for further analysis. Much progress has been made in this field over the past decade and yet plant metabolomics seems to still be an emerging approach because of the high complexity of plant metabolites and the number one challenge of untargeted metabolomics, metabolite identification. This final and critical stage remains the focus of current research. The intention of this review is to give a brief current state of LC–MS based untargeted metabolomics approaches for plant specific samples and to review the emerging solutions in mass spectrometer hardware and computational tools that can help predict a compound's molecular structure to improve the identification rate.

Comparison of Three Untargeted Data Processing Workflows for Evaluating LC-HRMS Metabolomics Data

The evaluation of liquid chromatography high-resolution mass spectrometry (LC-HRMS) raw data is a crucial step in untargeted metabolomics studies to minimize false positive findings. A variety of commercial or open source software solutions are available for such data processing. This study aims to compare three different data processing workflows (Compound Discoverer 3.1, XCMS Online combined with MetaboAnalyst 4.0, and a manually programmed tool using R) to investigate LC-HRMS data of an untargeted metabolomics study. Simple but highly standardized datasets for evaluation were prepared by incubating pHLM (pooled human liver microsomes) with the synthetic cannabinoid A-CHMINACA. LC-HRMS analysis was performed using normal- and reversed-phase chromatography followed by full scan MS in positive and negative mode. MS/MS spectra of significant features were subsequently recorded in a separate run. The outcome of each workflow was evaluated by its number of significant features, peak shape quality, and the results of the multivariate statistics. Compound Discoverer as an all-in-one solution is characterized by its ease of use and seems, therefore, suitable for simple and small metabolomic studies. The two open source solutions allowed extensive customization but particularly, in the case of R, made advanced programming skills necessary. Nevertheless, both provided high flexibility and may be suitable for more complex studies and questions.