Dissecting specialized metabolism in space: A MALDI-MSI atlas of Amaryllidaceae alkaloids in <i>Hippeastrum papilio</i> (Ravenna) Van Scheepen
Bibliographic record
Abstract
Abstract Amaryllidoideae produce specific specialized metabolites known as Amaryllidaceae alkaloids (AAs), extensively studied for their significant pharmacological potential. AAs’ spatial distribution and biosynthesis within plant tissues remain poorly understood. This study investigates organ- and tissue-specific localization in Hippeastrum papilio , from precursors to galanthamine and haemanthamine, using matrix-assisted laser desorption/ionization mass spectrometry imaging. Consistent accumulation of AAs was observed in epidermal and vascular tissues, with leaves exhibiting a uniform distribution across all ages and positions. Bulbs exhibited higher concentrations in the outer-scales and basal-plates, while roots displayed compartmentalized patterns, with galanthamine being uniquely abundant in the vascular bundles. Haemanthamine and galanthamine were detected in high quantities in the leaves’ and bulbs’ mucilage, while precursors were scarce. Multivariate analyses revealed that precursors clustered separately from end-products and were specifically enriched in the middle-scales and apical-leaves of the bulbs. Nonetheless, biosynthetic intermediates were observed in all tissues, indicating widespread AA biosynthesis across all organs. These findings suggest a coordinated metabolic network in H . papilio , which challenges existing hypotheses on organ-specific AA biosynthesis and hints at the transport of end-products. This study refines current models of alkaloid biosynthesis and underscore the value of H. papilio as a promising resource for sustainable production of therapeutic AAs. Highlights MALDI-MSI reveals widespread, tissue-specific alkaloid distribution in Hippeastrum papilio, challenging organ-specific biosynthesis and implicating epidermal and vascular tissues in Amaryllidaceae alkaloid production.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".