Comprehensive Imaging of Porcine Adrenal Gland Lipids by MALDI-FTMS Using Quercetin as a Matrix
Bibliographic record
Abstract
Adrenal glands synthesize and release functional zone-specific steroid and catecholamine hormones to regulate mammalian stress responses. Lipids such as sphingolipids have been shown to control the steroid hormone biosynthesis in adrenal glands, indicating their important roles in endocrine organs. Molecular imaging by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is a well-established analytical technique for determining both the spatial location and the relative abundances of various lipids on tissue. To better understand the overall roles of different lipid classes that play in the mammalian adrenal glands, it is necessary to comprehensively determine the spatial distributions of various lipids in the different functional zones of adrenal glands. However, the potential of this technique has not been fully reached, considering there are thousands of lipid species in a cell or tissue. To achieve this, we used quercetin as a MALDI matrix for negative ion detection of endogenous lipids on tissue sections of porcine adrenal glands by MALDI-Fourier-transform ion cyclotron resonance (FTICR) MS. As a result of these experiments, 409 endogenous compounds were detected in the negative ion mode. Combining both the positive and negative ion detection led to successful determination of the spatial distribution patterns of 555 unique endogenous compounds that were identified as 544 lipid entities and 11 nonlipid metabolites. Many classes of these lipids showed distinct distribution patterns in different functional zones of the adrenal gland. To the best of our knowledge, this work presents the largest group of lipid entities that have been analyzed in a single MS imaging study so far, and comprehensive profiles of the spatial distributions of lipids in porcine adrenal glands are shown here for the first time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.029 | 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 teacher head, 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".