Development of an NMR microprobe procedure for high‐throughput environmental metabolomics of <scp><i>Daphnia magna</i></scp>
Bibliographic record
Abstract
Nuclear magnetic resonance (NMR) is the primary platform used in high-throughput environmental metabolomics studies because its non-selectivity is well suited for non-targeted approaches. However, standard NMR probes may limit the use of NMR-based metabolomics for tiny organisms because of the sample volumes required for routine metabolic profiling. Because of this, keystone ecological species, such as the water flea Daphnia magna, are not commonly studied because of the analytical challenges associated with NMR-based approaches. Here, the use of a 1.7-mm NMR microprobe in analyzing tissue extracts from D. magna is tested. Three different extraction procedures (D2O-based buffer, Bligh and Dyer, and acetonitrile : methanol : water) were compared in terms of the yields and breadth of polar metabolites. The D2O buffer extraction yielded the most metabolites and resulted in the best reproducibility. Varying amounts of D. magna dry mass were extracted to optimize metabolite isolation from D. magna tissues. A ratio of 1-1.5-mg dry mass to 40 µl of extraction solvent provided excellent signal-to-noise and spectral resolution using (1)H NMR. The metabolite profile of a single daphnid was also investigated (approximately 0.2 mg). However, the signal-to-noise of the (1)H NMR was considerably lower, and while feasible for select applications would likely not be appropriate for high-throughput NMR-based metabolomics. Two-dimensional NMR experiments on D. magna extracts were also performed using the 1.7-mm NMR probe to confirm (1)H NMR metabolite assignments. This study provides an NMR-based analytical framework for future metabolomics studies that use D. magna in ecological and ecotoxicity studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".