MALDI-2 Mass Spectrometry Imaging enables spatial visualization of endocannabinoids in brain
Bibliographic record
Abstract
Endocannabinoids (eCBs) are endogenous lipid messengers that primarily bind cannabinoid receptors CB1/CB2 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, resulting altered in most neurological disorders. The analysis of eCBs is challenging for 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). 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 endogenous concentration only by MALDI-2-MSI. Both root-mean-square (RMS) and deuterated internal standards (I.S.) normalization were evaluated, with I.S. normalization providing improved pixel to pixel variation and more uniform distribution in specific brain regions, especially for 2-AG and PEA. Furthermore, high lateral 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. Lastly the method was applied as proof of concept in a mouse model of mild traumatic brain injury demonstrating the ability to reveal valuable biological insights for neuropharmacology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".