Sodium-Doped Gold-Assisted Laser Desorption Ionization for Enhanced Imaging Mass Spectrometry of Triacylglycerols from Thin Tissue Sections
Bibliographic record
Abstract
The deposition of sodium salts followed by a sputtered layer of gold has been demonstrated to be a power combination for the analysis of triacylglycerols (TAGs) from tissue sections by laser desorption ionization (LDI) imaging mass spectrometry (IMS). Various sodium salts were tested for their capability to ionize TAGs and their ability to produce fast drying, small crystals (≤3 μm). The spray deposition of a sodium acetate and carbonate buffer mixture at pH 10.3 on which a 28 ± 3 nm sputtered layer of gold (Au-CBS) is subsequently deposited was found to provide the most effective combination for TAG analysis by high imaging resolution IMS. Under these conditions, a 30-fold increase in TAG signal intensity was observed when compared to matrix-assisted LDI (MALDI) methods using 2,5-dihydrobenzoic acid as matrix. Furthermore, Au-CBS led to an increase in the number of detected TAG species from ∼7 with DHB to more than 25 with the novel method, while few phospholipid signals were observed. These results were derived from the IMS investigation of fresh frozen mouse liver and rabbit adrenal gland tissue sections with a range of higher spatial resolutions between 35 and 10 μm. Au-CBS-LDI MS presents a highly sensitive and specific alternative to MALDI MS for imaging of TAGs from tissue sections. This novel approach has the potential to provide new biological insights on the role of TAGs in both health and disease.
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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 | 0.000 | 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) | 0.001 | 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".