HR-MAS DREAMTIME NMR for Slow Spinning <i>ex Vivo</i> and <i>in Vivo</i> Samples
Bibliographic record
Abstract
HR-MAS NMR is a powerful tool, capable of monitoring molecular changes in intact heterogeneous samples. However, one of the biggest limitations of 1 H NMR is its narrow spectral width which leads to considerable overlap in complex natural samples. DREAMTIME NMR is a highly selective technique that allows users to isolate suites of metabolites from congested spectra. This permits targeted metabolomics by NMR and is ideal for monitoring specific processes. To date, DREAMTIME has only been employed in solution-state NMR, here it is adapted for HR-MAS applications. At high spinning speeds (>5 kHz), DREAMTIME works with minimal modifications. However, spinning over 3–4 kHz leads to cell lysis, and if maintaining sample integrity is necessary, slower spinning (<2.5 kHz) is required. Very slow spinning (≤500 Hz) is advantageous for in vivo analysis to increase organism survival; however, sidebands from water pose a problem. To address this, a version of DREAMTIME, termed DREAMTIME-SLOWMAS, is introduced. Both techniques are compared at 2500, 500, and 50 Hz, using ex vivo worm tissue. Following this, DREAMTIME-SLOWMAS is applied to monitor key metabolites of anoxic stress in living shrimp at 500 Hz. Thus, standard DREAMTIME works well under MAS conditions and is recommended for samples reswollen in D 2 O or spun >2500 Hz. For slow spinning in vivo or intact tissue samples, DREAMTIME-SLOWMAS provides an excellent way to target process-specific metabolites while maintaining sample integrity. Overall, DREAMTIME should find widespread application wherever targeted molecular information is required from complex samples with a high degree of spectral overlap.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".