Cumulative Neutral Loss Model for Fragment Deconvolution in Electrospray Ionization High-Resolution Mass Spectrometry Data

Abstract

Clean high-resolution mass spectra (HRMS) are essential to a successful structural elucidation of an unknown feature during nontarget analysis (NTA) workflows. This is a crucial step, particularly for the spectra generated during dataindependent acquisition or during direct infusion experiments. The most commonly available tools only take advantage of the time domain for spectral cleanup. Here, we present an algorithm that combines the time domain and mass domain information to perform spectral deconvolution. The algorithm employs a probability-based cumulative neutral loss (CNL) model for fragment deconvolution. The optimized model, with a mass tolerance of 0.005 Da and a score CNL threshold of 0.00, was able to achieve a true positive rate (TPr) of 95.0%, a false discovery rate (FDr) of 20.6%, and a reduction rate of 35.4%. Additionally, the CNL model was extensively tested on real samples containing predominantly pesticides at different concentration levels and with matrix effects. Overall, the model was able to obtain a TPr above 88.8% with FD rates between 33 and 79% and reduction rates between 9 and 45%. Finally, the CNL model was compared with the retention time difference method and peak shape correlation analysis, showing that a combination of correlation analysis and the CNL model was the most effective for fragment deconvolution, obtaining a TPr of 84.7%, an FDr of 54.4%, and a reduction rate of 51.0%.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

“…Low-quality MS 2 with unreliable fragment intensities or chimeric peaks (due to missingness or artifacts) might be attributed to instrumental noise (overshading low-abundant analytes) and/or co-eluting ions (due to sample complexity) . Emerging solutions have been proposed, including a Bayesian approach that computes cumulative neutral losses to clean up DIA spectra post hoc with or without the time domain for fragment deconvolution …”

“… 216 Emerging solutions have been proposed, including a Bayesian approach that computes cumulative neutral losses to clean up DIA spectra post hoc with or without the time domain for fragment deconvolution. 217 …”

Critical assessment of the chemical space covered by LC-HRMS non-targeted analysis

“…This typically results in very large and complex datasets (e.g. 5 GB per sample) that must be pre-processed prior to the identification workflow [31][32][33] . The NTA data processing workflows include several steps from data conversion to library search and the confidence assessment of the candidate spectra 2,23,[26][27][28][29] .…”

Critical Assessment of the Chemical Space Covered by LC–HRMS Non-Targeted Analysis

“…Non-targeted analysis (NTA) combined with liquid chromatography–high-resolution mass spectrometry (LC–HRMS) is considered to be one of the most comprehensive methods for the detection and identification of known and unknown unknowns in complex environmental and biological samples. , This approach utilizes a generic and wide-scope strategy for the sample preparation and analysis to maximize the coverage of the chemical space of the sample. ,,,− This typically results in very large and complex data sets (e.g., 5 GB per sample) that must be preprocessed prior to the identification workflow. − NTA data processing workflows include several steps, from data conversion to library searches and the confidence assessment of the candidate spectra. ,,− Because of the complexity of such data sets and the sheer size of the chemical databases, NTA workflows are not very sensitive and, thus, do not result in a high percentage of identified chromatographic features. , A more sensitive but less comprehensive data processing alternative is suspect screening, in which the chemicals of interest are known prior to the data processing workflow. This approach is more sensitive in terms of the limits of detection, but it is unable to detect unknown unknowns. ,, These two strategies are commonly employed together for the screening of complex environmental and biological samples …”

“… 2 , 13 , 21 , 23 − 31 This typically results in very large and complex data sets (e.g., 5 GB per sample) that must be preprocessed prior to the identification workflow. 31 − 33 NTA data processing workflows include several steps, from data conversion to library searches and the confidence assessment of the candidate spectra. 2 , 23 , 26 − 29 Because of the complexity of such data sets and the sheer size of the chemical databases, NTA workflows are not very sensitive and, thus, do not result in a high percentage of identified chromatographic features.…”