High-Spatial Resolution Matrix-Assisted Laser Desorption Ionization Imaging Analysis of Glucosylceramide in Spleen Sections from a Mouse Model of Gaucher Disease
Bibliographic record
Abstract
MALDI mass spectrometric imaging (MSI) enables spatially resolved mass and intensity information to be obtained directly from tissue sections, thereby illustrating how analytes are distributed within these sections. Here we have used an oversampling technique on a commercially available MALDI orthogonal acceleration TOF mass spectrometer with ion mobility separation capability to produce high spatial resolution images of the glycosphingolipid, glucosylceramide (GC). To exemplify the biological application of our approach, GC was imaged in spleen sections from a conditional knockout mouse model of type 1 Gaucher disease in which the catabolism of this glycosphingolipid is impaired. The laser was continually fired at one position until no more ions were observed and then the sample was moved by 15 microm (laser diameter approximately 150 microm). Ions were generated from only the unirradiated surface at each of these positions achieving an effective spacing of 15 microm. At 15 microm laser step-size, it was possible to visualize macrophages enriched in GC, which could be distinguished from other cell types in the spleen. Current MALDI MSI spatial resolution is typically limited by the diameter of the laser spot-size, which is usually between 50 and 100 microm, covering an area equivalent to tens of mammalian cells.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".