Challenges and Good Practices in Preprocessing and Normalization of Untargeted DNA Adductomics Data in Exposomics Research

Abstract

DNA adductomics is the study of the whole of DNA adducts in a biological sample and is a valuable asset to exposomics research. To date, a clear view on how to analyze larger sample series is lacking in DNA adductomics, and the preprocessing of untargeted DNA adductomics data is seldom applied. This work aimed to optimize a DNA adductomics data preprocessing workflow (in true untargeted mode). Building upon the xcms R package, we optimized parameters for peak detection, retention time alignment, and peak grouping to reliably detect and integrate putative DNA adduct LC-MS peaks. Next, to ensure reliable downstream data analysis, six sample- and feature-based normalization methods were tested and quantitatively evaluated in two data sets (placental tissue, n = 375, and blood samples, n = 51). As a result, a successful and reproducible procedure for optimization of xcms parameters for DNA adductomics is proposed. Furthermore, evaluation of normalization methods demonstrated the importance and limitations of objective (RSD* and D-ratio) and subjective, i.e., visual (PCA score plot) evaluation. This work supports reproducible and transparent untargeted DNA adductomics data preprocessing to be implemented in large-scale exposomics studies.