Monitoring time‐dependent degradation of phospholipids in sectioned tissues by MALDI imaging mass spectrometry
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.
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 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 | 0.001 |
| 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 | 0.000 | 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".