Enhanced visualization of endocannabinoids spatial distribution in mouse brain via MALDI-2 mass spectrometry imaging
Bibliographic record
Abstract
and together with the enzymes that regulate their biosynthesis and degradation define the endocannabinoid system. The eCB signaling system plays a key role in the central nervous system, and results often altered in neurological disorders. The analysis of eCBs is challenging due to their low concentration in biospecimens, and this is exacerbated in Mass Spectrometry Imaging (MSI) where low sensitivity and tissue dependent ion suppression obscure their spatial visualization. In this work we address this limitation by the application of laser-induced post-ionization (MALDI-2) approach. Herein we demonstrate that MALDI-2 boosts the detection of 2-arachidonylglycerol (2-AG) and N-acylethanolamines (AEA, PEA, OEA) with respect to MALDI, and that eCBs can be visualized in brain at physiological concentration only by MALDI-2-MSI. Root-mean-square (RMS), Total ion count (TIC) and internal standards (I.S.) normalization were evaluated, with I.S. normalization providing improved pixel to pixel variation and more uniform distribution for 2-AG and PEA in specific brain regions. Furthermore, high spatial resolution up to 5 μm pixel size was evaluated, resulting in the detection of all eCBs and confirming the MALDI-2 potential even reducing the ablated tissue amount. As proof of concept, the method was applied to map eCBs in a mouse model of mild traumatic brain injury, the APP-SWE mice, highlighting differences in the modulation of eCBs in Cortex, Hippocampus and Hypothalamus, suggesting the ability to reveal valuable biological insights for neuropharmacology.
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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".