High Resolution Mass Spectrometry Methods for High Throughput and Untargeted Analysis of Anatoxins in Cyanobacteria
Bibliographic record
Abstract
Anatoxins (ATXs) are increasingly linked to animal fatalities worldwide, but their analysis is challenging due to their polarity, instability, sample complexity and the scarcity of standards for most analogues. Conventional methods often don’t detect or differentiate analogues or are prone to significant matrix interferences in quantitation or detection. Here, we describe two recently developed methods for ATX analysis and demonstrate their application to cyanobacterial field samples from Atlantic Canada. A direct analysis in real time-high resolution tandem mass spectrometry (DART-HRMS/MS) method with limits of detection (LOD) of approximately 5 μg/kg and a total run time of under 2 min per triplicate analysis was suitable for rapid quantitation of anatoxin-a, homoanatoxin-a and dihydroanatoxin-a. Sample preparation was simplified to only require cell lysis, homogenization and centrifugation. An untargeted LC-HRMS/MS is also presented that offered lower LODs (0.1 μg/kg) and the opportunity to differentiate isomeric species and detect unknown ATX conjugates. Both methods used isotope dilution calibration with 13C4-anatoxin-a to correct for the significant and variable matrix effects and showed excellent quantitative performance. They are broadly applicable to other quantitative or screening applications and will allow for more comprehensive study of ATX occurrence including the typically undetected fraction of conjugated ATXs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".