The development of pseudo-SIM/MRM and risk-based screening methods for characterization of human exposome

• Main Author: Yang, Junjie
• Other Authors: Zhou Yan
• Format: Thesis-Doctor of Philosophy
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Science::Chemistry::Analytical chemistry
• Online Access: https://hdl.handle.net/10356/164211
• Institution: Nanyang Technological University

Exposome, which studies all the environmental exposure during the lifelong period, is the new era of human health research and a rapidly expanding research area. Tracking chemical exposures in the environment or the human body has been performed by targeted analysis using multiple reactions monitoring or single ion monitoring (MRM/SIM) method and suspect screening in non-targeted analysis. However, the constraints of analytical platforms, such as high application costs, detection of poor throughput, restricted chemical coverage, and reliance on chemical standards for method developments, have hampered the growth of exposome characterization. One of the strategies to face the limitations in the targeted analysis is to develop computational optimization methods and further develop MRM/SIM spectral databases for different instruments. Meanwhile, another strategy is to develop high-throughput workflows with multiple screening filters. Here, this thesis demonstrates the development of several proof-of-concept methodologies in targeted analysis and non-targeted analysis to tackle challenges in the characterization of human exposomes. MRM methods in mass spectrometry coupled with liquid chromatography (LC-MS) can be optimized computationally. The instrument-dependent settings of collision energy can be generalized for applications in different instruments. A reliable and fast screening platform is developed with generalized MRM methods. When coupled with retention time prediction and high-resolution mass spectrometry (HRMS), the conventional targeted analysis workflow can identify environmental exposure without chemical standards. Beyond CE optimization, this thesis replaces the experimental optimization of MRM/SIM transitions with an in-silico optimization strategy. Databases of pseudo-MRM/SIM spectra are developed by a pseudo-spectra algorithm using existing public spectral databases. The databases provide optimized MRM transitions with high selectivity for over 300,000 exogenous chemicals. Furthermore, this thesis develops a novel sensitive and high-throughput exposome analytical platform (CIL-ExPMRM) by isotope labeling urinary biomarkers to increase the detection of chemicals at trace levels. The CIL-pseudo-MRM exposome database consists of environmental pollutants and their transformation products for 110,000 compounds. The platform has been well incorporated with automatic MRM generation, dynamic MRM optimization, and data analysis. Meanwhile, this thesis proposes a new non-target analysis workflow for environmental chemical screening using multiple screening filters and risk-based chemical prioritization. Retention time prediction and spectral matching can provide structural elucidation in identification. Toxicity prediction links the MS fragments to chemical toxicity. Risk-based prioritization highlights the candidates of high threat to human health. Overall, this thesis found the generalized method for optimizing collision energy in MRM methods. The pseudo-MRM/SIM spectral database and CIL-pseudo-MRM spectral databases provided alternatives for MRM transition optimization in the targeted analysis community. The automatic and integrated platform rendered high-throughput suspect screening for non-targeted analysis of human exposomes.