Insights into the MALDI Process after Matrix Deposition by Sublimation Using 3D ToF-SIMS Imaging
Bibliographic record
Abstract
Imaging mass spectrometry (IMS) has become a powerful tool to characterize the spatial distribution of biomolecules in thin tissue sections. In the case of matrix-assisted laser desorption ionization (MALDI) IMS, homogeneous matrix deposition is critical to produce high-quality ion images, and sublimation in particular has shown to be an excellent matrix deposition method for the imaging of lipids. Matrix deposition by sublimation is, however, a completely solvent-free system, which ought to prevent the mixing of matrix and analytes thought to be necessary for successful MALDI. Using 3D time-of-flight secondary ion imaging mass spectrometry, we have studied the matrix-tissue interface in 3D with high resolution to understand the MALDI process of lipids after matrix deposition by sublimation. There is a strong indication that diffusion is the process by which lipids migrate from the tissue to the matrix layer. We show that triacylglycerols and phospholipids have a delayed migratory trend as compared to diacylglycerols and monoacylglycerols, which is dependent on time and matrix thickness. Additional experiments show that a pure lipid's capacity to migrate into the matrix is dependent on its fluidity at room temperature. Furthermore, it is shown that cholesterol can only migrate in the presence of a (fluid) lipid and appears to fluidize lipids, which could explain its colocalization with the diacylglycerols and monoacylglycerols in the matrix.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".