Dithranol as a MALDI Matrix for Tissue Imaging of Lipids by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry
Bibliographic record
Abstract
To fill the unmet need for improved matrixes for matrix-assisted laser desorption ionization (MALDI) tissue imaging of small molecules, dithranol (DT)--a matrix mainly used for the analysis of synthetic polymers--was evaluated for detection of lipids in rat liver and bovine calf lens, using MALDI Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS). The use of DT resulted in better detection of endogenous lipids than did two other commonly used matrixes, α-cyano-4-hydroxycinnamic acid (CHCA) and 2,5-dihydroxybenzoic acid (DHB), with >70 lipid entities (including phosphatidylcholines, phosphatidylethanolamines, sphingomyelins, phosphatidylserines, phosphatidylglycerol, phosphatidic acids, ceramide phosphates, sterol lipids, acyl carnitines, and glycerides) being detected in rat liver and bovine lens tissue sections, using positive-ion detection. Using saturated DT in chloroform/methanol (2:1, v/v), with 1% formic acid in the final mixture, 57 lipid entities were successfully imaged from bovine calf lens, with clear and distinct distribution patterns. In a section across the lens equatorial plane, all compounds showed concentric distributions around the lens nucleus and most showed specific abundance changes, which correlated with lens fiber cell age. As a novel finding, palmitoylcarnitine and oleoylcarnitine were found uniquely localized to the younger lens fiber cell cortex region. This work demonstrates the potential of DT as a new matrix for tissue imaging by MALDI-FTICR MS.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".