Validating MALDI-IMS Feasibility in <i>Ex Vivo</i> Brain Slices
Bibliographic record
Abstract
Matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI-IMS) generates unique mass spectra in X / Y coordinates across a tissue sample, thus allowing for the spatial detection and relative quantification of biologic compounds in situ . The soft ionization of MALDI-IMS makes it an ideal technique for high-resolution imaging of complex lipid species. Lipid-based spatial chemical maps derived from MALDI-IMS provide critical insight into the unique molecular profiles of a variety of neurologic diseases. Ex vivo brain slice preparations are a prominent alternative to in vivo animal models for studying many different neurologic conditions. For the first time, we present a feasible protocol for achieving reproducible lipidomic MALDI-IMS data from ex vivo rat brain slices and provide evidence that ex vivo brain slices maintain spatiochemical lipidomic profiles representative of an intact whole brain. We conducted a methods comparison assessing the lipid profiles within the neocortex, striatum, and corpus callosum between coronal sections taken from ex vivo brain slices and the current gold standard tissue preparation method, fresh frozen whole brains. For the first time we demonstrate a technique by which 400 μm ex vivo brain slices can be extracted from an imaging chamber and prepared for MALDI-IMS in a way that preserves their lipidomic integrity. We demonstrate the feasibility of MALDI-IMS in ex vivo brain slices and provide a roadmap for MALDI-IMS utilization in uncharted neuroscience fields.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| 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".