Evaluation of Nontargeted Mass Spectral Data Acquisition Strategies for Water Analysis and Toxicity-Based Feature Prioritization by MS2Tox

The machine-learning tool MS2Tox can prioritize hazardous
nontargeted
molecular features in environmental waters, by predicting acute fish
lethality of unknown molecules based on their MS2 spectra,
prior to structural annotation. It has yet to be investigated how
the extent of molecular coverage, MS2 spectra quality,
and toxicity prediction confidence depend on sample complexity and
MS2 data acquisition strategies. We compared two common
nontargeted MS2 acquisition strategies with liquid chromatography
high-resolution mass spectrometry for structural annotation accuracy
by SIRIUS+CSI:FingerID and MS2Tox toxicity prediction of 191 reference
chemicals spiked to LC-MS water, groundwater, surface water, and wastewater.
Data-dependent acquisition (DDA) resulted in higher rates (19–62%)
of correct structural annotations among reference chemicals in all
matrices except wastewaters, compared to data-independent acquisition
(DIA, 19–50%). However, DIA resulted in higher MS2 detection rates (59–84% DIA, 37–82% DDA), leading
to higher true positive rates for spectral library matching, 40–73%
compared to 34–72%. DDA resulted in higher MS2Tox toxicity
prediction accuracy than DIA, with root-mean-square errors of 0.62
and 0.71 log-mM, respectively. Given the importance of MS2 spectral quality, we introduce a “CombinedConfidence”
score to convey relative confidence in MS2Tox predictions and apply
this approach to prioritize potentially ecotoxic nontargeted features
in environmental waters.