An untargeted data mining strategy for extracting chemical exposome signatures from LC-HRMS data: Application to meconium for early-life exposure assessment

Background

Exposome research has expanded rapidly in recent years, driven by advances in analytical techniques such as liquid chromatography-high-resolution mass spectrometry (LC-HRMS), which enable broad and sensitive chemical coverage. Targeted methods focus on known compounds, while untargeted metabolomic approaches provide a more holistic view and may reveal exposure biomarkers, but they are not specifically designed to detect exogenous chemicals. Identifying relevant exposure markers within the vast and complex datasets generated by untargeted LC-HRMS data remains a significant analytical and computational challenge, requiring innovative data mining strategies.

Results

We developed a novel untargeted data mining strategy to extract exogenous chemical signatures from complex LC-HRMS datasets. The approach integrates isotopic signature enrichment (ISE), biotransformation-informed feature selection and an “exposure rate” metric. When applied to meconium data from the EDEN cohort, the strategy led to a six-fold reduction in the number of features by retaining only those exhibiting valid carbon isotope patterns. Mass defect plots revealed signatures of suspect monohalogenated species and putative conjugated and non-conjugated metabolites in a specific region. Incorporating ISE results into the chemical formula prediction significantly reduced the number of candidates, improving annotation efficiency. In utero exposure to xenobiotics was supported by the detection of known exposure markers such as acetaminophen, caffeine and nicotine. These results demonstrate the method's potential to uncover exposomic signals in complex biological matrices.

Significance

This study presents a novel data mining strategy that reduces the complexity of untargeted LC-HRMS data by retaining chemically reliable features based on isotopic signatures. As a proof of concept, this strategy enables the detection of specific chemical signatures and exogenous compounds without prior knowledge. Its adaptability to various biological matrices and its compatibility with different high-resolution mass spectrometry platforms make this strategy a valuable tool for exposome research and early-life exposure assessment.