The exposome is a concept used in epidemiology and environmental health. It represents the totality of exposures an individual experiences throughout their life. Among these, exposure to chemical molecules—whether exogenous (pollutants, metals, but also everyday consumer products) or endogenous (produced by the body in response to exposure)—constitutes a subcategory known as the chemical exposome.
Research on the chemical exposome has expanded rapidly in recent years, thanks to advances in analytical techniques such as liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS), which enable broad and sensitive chemical coverage. However, detecting exposure markers among the hundreds or thousands of constituents present in human biological matrices remains a major analytical challenge. Targeted methods, which aim to detect known contaminants, do not allow for the assessment of unexpected exposure diversity. Similarly, non-targeted metabolomic approaches provide a more comprehensive view of the metabolic fingerprint but are not specifically designed to detect exogenous substances. They also produce large and complex LC-HRMS datasets that are difficult to exploit for identifying exposure markers.
Teams from DMTS (LIAA and LI-MS) collaborated to develop a data exploration strategy that extracts potential xenobiotic exposure signatures from non-targeted LC-HRMS datasets without prior assumptions. This approach combines precise measured mass filters and chemical characteristics. It includes the elimination of artifact and background signals, enrichment of relevant data through isotopic signatures (ISE), extraction of suspected halogenated compounds and potential biotransformation products (conjugated and non-conjugated metabolite pairs), and consideration of potential exposure frequency.
As a proof of concept, the method was applied to the analysis of data from meconium samples collected in the EDEN mother-child cohort in France, which aims to better understand the importance of early determinants on individual health, in collaboration with the EAROH team at the Center for Research in Epidemiology and Statistics (CRESS). It revealed signatures of exogenous compounds, reflecting in utero exposure to xenobiotics such as paracetamol, caffeine, nicotine, and monohalogenated compounds. These results demonstrate the method's potential for identifying "exposomic" signals in complex biological matrices.
The method's adaptability to various biological matrices and its compatibility with different high-resolution mass spectrometry platforms make it a valuable tool for exposome research and the assessment of early exposures.
Contact at Frédéric-Joliot Institute for Life sciences:
Estelle Rathahao-Paris