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Scannotation: A Suspect Screening Tool for the Rapid Pre-Annotation of the Human LC-HRMS-Based Chemical Exposome.
- Source :
-
Environmental science & technology [Environ Sci Technol] 2023 Dec 05; Vol. 57 (48), pp. 19253-19262. Date of Electronic Publication: 2023 Nov 15. - Publication Year :
- 2023
-
Abstract
- In an increasingly chemically polluted environment, rapidly characterizing the human chemical exposome (i.e., chemical mixtures accumulating in humans) at the population scale is critical to understand its impact on health. High-resolution mass spectrometry (HRMS) profiling of complex biological matrices can theoretically provide a comprehensive picture of chemical exposures. However, annotating the detected chemical features, particularly low-abundant ones, remains a significant obstacle to implementing such approaches at a large scale. We present Scannotation (https://github.com/scannotation/Scannotation_software), an automated and user-friendly suspect screening tool for the rapid pre-annotation of HRMS preprocessed data sets. This software tool combines several MS1 chemical predictors, i.e., m / z , experimental and predicted retention times, isotopic patterns, and neutral loss patterns, to score the proximity between features and suspects, thus efficiently prioritizing tentative annotations to verify. Scannotation and MS-DIAL4 were used to annotate blood serum samples of 75 Breton adolescents. Scannotation's combination of MS1-based chemical predictors allowed us to annotate 89 chemically diverse environmental compounds with high confidence (confirmed by MS2 when available). These compounds included 62% of emerging molecules, for which no toxicological or human biomonitoring data are reported in the literature. The complementarity observed with MS-DIAL4 results demonstrates the relevance of Scannotation for the efficient pre-annotation of large-scale exposomics data sets.
- Subjects :
- Humans
Adolescent
Mass Spectrometry methods
Exposome
Subjects
Details
- Language :
- English
- ISSN :
- 1520-5851
- Volume :
- 57
- Issue :
- 48
- Database :
- MEDLINE
- Journal :
- Environmental science & technology
- Publication Type :
- Academic Journal
- Accession number :
- 37968235
- Full Text :
- https://doi.org/10.1021/acs.est.3c04764