JPA: Joint Metabolic Feature Extraction Increases the Depth of Chemical Coverage for LC-MS-Based Metabolomics and Exposomics

Abstract

Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data has been a long-standing bioinformatic challenge in untargeted metabolomics. Conventional feature extraction algorithms fail to recognize features with low signal intensities, poor chromatographic peak shapes, or those that do not fit the parameter settings. This problem also poses a challenge for MS-based exposome studies, as low-abundant metabolic or exposomic features cannot be automatically recognized from raw data. To address this data processing challenge, we developed an R package, JPA (short for Joint Metabolomic Data Processing and Annotation), to comprehensively extract metabolic features from raw LC-MS data. JPA performs feature extraction by combining a conventional peak picking algorithm and strategies for (1) recognizing features with bad peak shapes but that have tandem mass spectra (MS2) and (2) picking up features from a user-defined targeted list. The performance of JPA in global metabolomics was demonstrated using serial diluted urine samples, in which JPA was able to rescue an average of 25% of metabolic features that were missed by the conventional peak picking algorithm due to dilution. More importantly, the chromatographic peak shapes, analytical accuracy, and precision of the rescued metabolic features were all evaluated. Furthermore, owing to its sensitive feature extraction, JPA was able to achieve a limit of detection (LOD) that was up to thousands of folds lower when automatically processing metabolomics data of a serial diluted metabolite standard mixture analyzed in HILIC(−) and RP(+) modes. Finally, the performance of JPA in exposome research was validated using a mixture of 250 drugs and 255 pesticides at environmentally relevant levels. JPA detected an average of 2.3-fold more exposure compounds than conventional peak picking only.

Mechanistic Understanding of the Discrepancies between Common Peak Picking Algorithms in Liquid Chromatography–Mass Spectrometry-Based Metabolomics

“…Last, given the uniqueness of each peak picking algorithm, no single peak picking algorithm can extract all true positive features from untargeted metabolomics data. At the current stage, a good strategy is to combine multiple peak picking algorithms to process the same dataset. − In this work, we demonstrated this integration concept by combining pairs of peak picking algorithms and extracted features from the 10 datasets. The number of true metabolic features detected by two combined algorithms is significantly higher than that by an individual algorithm (Figure C).…”

Addressing big data challenges in mass spectrometry-based metabolomics

“…Moreover, JPA also surpassed the conventional centWave algorithm by detecting 2.3-fold more exposure chemicals from a standard mixture containing 505 drugs and pesticides. 16…”

“…4c). 16 Using various biological sample types, we systematically investigated how sample concentration, LC separation conditions, and data processing software contribute to computational variation. Our results suggest that the computational variation is largely determined by the data processing software.…”

“…Moreover, JPA also surpassed the conventional centWave algorithm by detecting 2.3-fold more exposure chemicals from a standard mixture containing 505 drugs and pesticides. 16 On the other hand, enhancing feature extraction sensitivity usually comes with an increase in false positive features. False positive metabolic features can reduce the confidence of downstream statistical and biological interpretations.…”

ChloroDBPFinder: Machine Learning-Guided Recognition of Chlorinated Disinfection Byproducts from Nontargeted LC-HRMS Analysis

“…Currently, there are two primary strategies for the nontargeted discovery of Cl-DBPs. The first strategy leverages the distinct MS patterns of the Cl element, including (1) the negative mass defect, which represents the mass difference between the exact mass and nominal mass for Cl-containing compounds, 20,21 and (2) the unique isotope intensity pattern of 35 Cl/ 37 Cl (Table S1). 22−27 However, given the diverse DBP chemical structures, relying solely on mass defect or a single isotope ratio cutoff is inaccurate and not sufficient for capturing all Cl-DBPs.…”