Monitoring the degree of in-source phospholipid fragmentation during MALDI mass spectrometry imaging
Bibliographic record
Abstract
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a powerful tool for tissue lipid analysis. Yet, the validity of fatty acid (FA) signals is often questioned due to the potential of in-source lipid fragmentation. This study investigates the extent of in-source phospholipid fragmentation by examining different phospholipid headgroups, specifically phosphatidylethanolamine (PE), phosphatidylglycerol (PG), phosphatidylserine (PS) and phosphatidylcholine (PC), containing a mix of saturated and unsaturated tails, comparing in-source fragmentation of FA tails. These standards evaluated phospholipid fragmentation during laser intensity optimization for MALDI-MS and MSI. Fragmentation was assessed in negative ion mode using four MALDI matrices: norharmane (NRM), 9-aminoacridine (9AA), 1,5-diaminonaphthalene (DAN), and 1,6-diphenyl-1,3,5-hexatriene (DPH). By examining the extent of in-source fragmentation during MALDI MSI, an exogenous standard can be chosen to monitor in-source fragmentation during experiments. Various techniques for standard deposition were evaluated, including manual spotting versus automated spraying and pre-depositing standards beneath tissue sections compared to applying them directly onto tissue surfaces. The results demonstrate the ability of these phospholipid standards to detect in-source fragmentation using different matrices, as demonstrated by the detection of the exogenous FA 17:0 tail signal. Furthermore, the study highlights the importance of selecting a standard that closely matches the lipid of interest to optimize laser energy for MSI, enhancing endogenous FA signals while minimizing in-source fragmentation. This work provides a workflow for mitigating in-source fragmentation in MALDI-MSI experiments, enabling more reliable lipid analysis in complex biological tissues.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".