EISA-EXPOSOME: One Highly Sensitive and Autonomous Exposomic Platform with Enhanced in-Source Fragmentation/Annotation

Lacking a highly sensitive exposome screening technique is one of the biggest challenges in moving exposomic research forward. Enhanced in-source fragmentation/annotation (EISA) has been developed to facilitate molecular identification in untargeted metabolomics and proteomics. In this work, with a mixture of 50 pesticides at three concentration levels (20, 4, and 0.8 ppb), we investigated the analytical performance of the EISA technique over the well-accepted targeted MS/MS mode (TMM) in the detection and identification of chemicals at low levels using a quadrupole time-of-flight (qTOF) instrument. Compared with the TMM method, the EISA technique can recognize additional 1, 20, and 23 chemicals, respectively, at the three concentration levels (20, 4, and 0.8 ppb, respectively) investigated. At the 0.8 ppb level, intensities of precursor ions and fragments observed using the EISA technique are 30–1,154 and 3–80 times higher, respectively, than those observed at the TMM mode. A higher matched fragment ratio (MFR) between the EISA technique and the TMM method was recognized for most chemicals. We further developed a chemical annotation informatics algorithm, EISA-EXPOSOME, which can automatically search each precursor ion (m/z) in the MS/MS library against the EISA MS1 spectra. This algorithm then calculated a weighted score to rank the candidate features by comparing the experimental fragment spectra to those in the library. The peak intensity, zigzag index, and retention time prediction model as well as the peak correlation coefficient were further adopted in the algorithm to filter false positives. The performance of EISA-EXPOSOME was demonstrated using a pooled dust extract with a pesticide mixture (n = 200) spiked at 5 ppb. One urine sample spiked with a contaminant mixture (n = 50) at the 5 ppb level was also used for the validation of the pipeline. Proof-of-principal application of EISA-EXPOSOME in the real sample was further evaluated on the pooled dust sample with a modified T3DB database (n = 1650). Our results show that the EISA-EXPOSOME algorithm can remarkably improve the detection and annotation coverage at trace levels beyond the traditional approach as well as facilitate the high throughput screening of suspected chemicals.