Electromagnetic Field-Assisted Frozen Tissue Planarization Enhances MALDI-MSI in Plant Spatial Omics
Bibliographic record
Abstract
Plant samples with irregular morphology are challenging for longitudinal tissue sectioning. This has restricted the ability to gain insight into some plants using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Herein, we develop a novel technique termed electromagnetic field-assisted frozen tissue planarization (EMFAFTP). This technique involves using a pair of adjustable electromagnets on both sides of a plant tissue. Under an optimized electromagnetic field strength, nondestructive planarization and regularization of the frozen tissue is induced, allowing the longitudinal tissue sectioning that favors subsequent molecular profiling by MALDI-MSI. As a proof of concept, flowers, leaves and roots with irregular morphology from six plant species are chosen to evaluate the performance of EMFAFTP for MALDI-MSI of secondary metabolites, amino acids, lipids, and proteins among others in the plant samples. The significantly enhanced MALDI-MSI capabilities of these endogenous molecules demonstrate the robustness of EMFAFTP and suggest it has the potential to become a standard technique for advancing MALDI-MSI into a new era of plant spatial omics.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".