Scannotation: A Suspect Screening Tool for the Rapid Pre-Annotation of the Human LC-HRMS-Based Chemical Exposome

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.在日益受到化学污染的环境中，快速在人群尺度上表征人类化学暴露组（chemical exposome，即蓄积于人体内的化学混合物），对于理解其对健康的影响至关重要。对复杂生物基质进行高分辨质谱（High-resolution mass spectrometry, HRMS）分析，理论上可全面呈现化学暴露全貌。然而，对检测到的化学特征尤其是低丰度特征进行注释，仍是大规模应用此类方法的重大阻碍。本研究推出Scannotation（https://github.com/scannotation/Scannotation_software）——一款自动化且易用的可疑物筛查工具，可对HRMS预处理数据集进行快速预注释。该工具整合了多项MS1化学预测指标，包括质荷比（m/z）、实验与预测保留时间、同位素模式及中性丢失模式，对特征与可疑物之间的相似度进行评分，从而高效地对待验证的暂定注释进行优先级排序。研究使用Scannotation与MS-DIAL4对75名布列塔尼青少年的血清样本进行注释。基于MS1化学预测指标的组合应用，Scannotation可高置信度地注释89种化学结构多样的环境化合物（如有MS2数据则通过MS2验证）。其中62%为新兴分子，此类分子的毒理学与人体生物监测数据在已发表文献中尚未见报道。与MS-DIAL4的结果所展现出的互补性，证明了Scannotation在大规模暴露组学（exposomics）数据集高效预注释方面的适用性。