In-Source Fragmentation Annotation in Sterol Mass Spectrometry Imaging
Bibliographic record
Abstract
Spatial biology has emerged as a pivotal area in many life science fields, with mass spectrometry imaging (MSI) becoming a cornerstone for molecular imaging. Among recent advancements to increase sensitivity, MALDI-2 technology has significantly expanded the molecular space accessible to MSI, increasing the ion yields of neutral metabolites, such as sterols. Sterols have recently taken center stage in numerous (patho-) physiological processes, including neurodegenerative diseases that have attracted significant scientific interest. However, in-source fragmentation (ISF) poses a substantial challenge for accurate biological interpretation of mass spectrometric data. In this study, we observed and investigated the ISF of cholesterol during MSI under MALDI and MALDI-2 conditions. Using a murine intervention model, we demonstrate how ISF can compromise the accuracy of biological interpretations, potentially leading to significant misinterpretations. Our study underscores the critical need to address ISF to ensure accurate molecular annotation, particularly through tandem mass spectrometry of in-source fragments. This is especially important when using MALDI-2 techniques. Furthermore, we introduce a high-resolution (5 μm) MSI technique, enabling the precise spatial analysis of cholesterol distribution.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".