Monitoring time‐dependent degradation of phospholipids in sectioned tissues by MALDI imaging mass spectrometry
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Bibliographic record
Abstract
Imaging mass spectrometry (IMS) is useful for visualizing the localization of phospholipids on biological tissue surfaces creating great opportunities for IMS in lipidomic investigations. With advancements in IMS of lipids, there is a demand for large-scale tissue studies necessitating stable, efficient and well-defined sample handling procedures. Our work within this article shows the effects of different storage conditions on the phospholipid composition of sectioned tissues from mouse organs. We have taken serial sections from mouse brain, kidney and liver thaw mounted unto ITO-coated glass slides and stored them under various conditions later analyzing them at fixed time points. A global decrease in phospholipid signal intensity is shown to occur and to be a function of time and temperature. Contrary to the global decrease, oxidized phospholipid and lysophospholipid species are found to increase within 2 h and 24 h, respectively, when mounted sections are kept at ambient room conditions. Imaging experiments reveal that degradation products increase globally across the tissue. Degradation is shown to be inhibited by cold temperatures, with sample integrity maintained up to a week after storage in -80 °C freezer under N2 atmosphere. Overall, the results demonstrate a timeline of the effects of lipid degradation specific to sectioned tissues and provide several lipid species which can serve as markers of degradation. Importantly, the timeline demonstrates oxidative sample degradation begins appearing within the normal timescale of IMS sample preparation of lipids (i.e. 1-2 h) and that long-term degradation is global. Taken together, these results strengthen the notion that standardized procedures are required for phospholipid IMS of large sample sets, or in studies where many serial sections are prepared together but analyzed over time such as in 3-D IMS reconstruction experiments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it