EISA-EXPOSOME: One Highly Sensitive and Autonomous Exposomic Platform with Enhanced in-Source...

Area of Science:

• Environmental Chemistry
• Analytical Chemistry
• Toxicology
• Metabolomics
• Proteomics

Background:

• Exposomic research faces challenges due to the lack of sensitive screening techniques for identifying a wide range of chemicals.
• Enhanced in-source fragmentation/annotation (EISA) is a technique developed to improve molecular identification in untargeted metabolomics and proteomics.
• Targeted MS/MS mode (TMM) is a well-established method for chemical detection and identification.

Purpose of the Study:

• To investigate the analytical performance of the EISA technique compared to TMM for detecting and identifying low-level chemicals.
• To develop and validate a chemical annotation informatics algorithm, EISA-EXPOSOME, for high-throughput screening of suspected chemicals.
• To assess the capability of EISA and EISA-EXPOSOME in improving detection and annotation coverage at trace levels.

Main Methods:

• Analytical performance comparison of EISA and TMM using a mixture of 50 pesticides at varying concentrations (20, 4, and 0.8 ppb) on a quadrupole time-of-flight (qTOF) instrument.
• Development of the EISA-EXPOSOME algorithm for automatic searching of precursor ions against an MS/MS library using EISA MS1 spectra.
• Validation of the EISA-EXPOSOME pipeline using spiked pooled dust extracts and urine samples, and proof-of-principle application on a pooled dust sample with a modified T3DB database.

Main Results:

• EISA identified significantly more chemicals than TMM across all tested concentrations, recognizing 1, 20, and 23 additional chemicals at 20, 4, and 0.8 ppb, respectively.
• At 0.8 ppb, EISA showed substantially higher intensities for precursor ions (30-1,154 times) and fragments (3-80 times) compared to TMM, with a higher matched fragment ratio (MFR).
• The EISA-EXPOSOME algorithm demonstrated improved detection and annotation coverage at trace levels, outperforming traditional approaches and facilitating high-throughput screening.

Conclusions:

• The EISA technique offers superior sensitivity and identification capabilities for low-level chemicals compared to TMM in exposomic research.
• The EISA-EXPOSOME algorithm effectively enhances the detection and annotation of chemicals, providing a powerful tool for high-throughput screening.
• This combined approach significantly advances the field of exposomic research by enabling more comprehensive chemical screening at trace levels.