Hydroxyflavones as a New Family of Matrices for MALDI Tissue Imaging
Bibliographic record
Abstract
The discovery of new matrices that are suitable for in situ analysis of low molecular-weight compounds by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is an important technological aspect of tissue imaging. In this work, ten natural flavonoid compounds, including flavone and nine of its mono- or polyhydroxyl-substituted analogues (3-hydroxyflavone, 5-hydroxyflavone, 3,7-dihydroxyflavone, chrysin, 7,3',4'-trihydroxyflavone, fisetin, luteolin, quercetin, and morin) were evaluated as potential MALDI matrices for the profiling and imaging of endogenous lipids in mouse liver, using a Fourier transform ion cyclotron resonance (FTICR) mass spectrometer with a 355-nm Nd:YAG UV laser, in the positive ion mode. When an electronic sprayer was used for matrix coating and with a high-pH (0.1-0.5% ammonia hydroxide) matrix solvent, eight of the ten compounds, all of which had at least one OH group at the C3 or C5 position of the flavone structure, enabled the successful detection of 77 to 161 phospholipids and other lipids. The best results were observed with two penta-OH flavones (i.e., quercertin and morin). Taking quercetin as an example, this matrix showed characteristics superior to those of commonly used MALDI matrices, such as DHB (2,5-dihydroxybenzoic acid), CHCA (α-cyano-4-hydroxycinnamic acid), and 2-mercaptobenzothiazole (2-MBT). These characteristics were: μm-sized matrix crystals, uniform matrix coating, low volatility in the high vacuum (~10(-7) mbar) source, good chemical stability, low yield of matrix-related ions, low matrix consumption, low power threshold for laser desorption/ionization, and improved safety of handling. The use of quercetin led to improved lipid imaging, with 212 lipids being successfully imaged from rat brain in a single experiment and with asymmetric distributions of some lipids in left and right brain hippocampus being observed for the first time.
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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 | 0.001 |
| 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".