On-Tissue Localization of Ceramides and Other Sphingolipids by MALDI Mass Spectrometry Imaging
Bibliographic record
Abstract
A novel MALDI-FTICR imaging mass spectrometry (MALDI-IMS) workflow is described for on-tissue detection, spatial localization, and structural confirmation of low abundance bioactive ceramides and other sphingolipids. Increasingly, altered or elevated levels of sphingolipids, sphingolipid metabolites, and sphingolipid metabolizing enzymes have been associated with a variety of disorders such as diabetes, obesity, lysosomal storage disorders, and cancer. Ceramide, which serves as a metabolic hub in sphingolipid metabolism, has been linked to cancer signaling pathways and to metabolic regulation with involvement in autophagy, cell-cycle arrest, senescence, and apoptosis. Using kidney tissues from a new Farber disease mouse model in which ceramides of all acyl chain lengths and other sphingolipid metabolites accumulate in tissues, specific ceramides and sphingomyelins were identified by on-tissue isolation and fragmentation, coupled with an on-tissue digestion by ceramidase or sphingomyelinase. Multiple glycosphingolipid species were also detected. The newly generated library of sphingolipid ions was then applied to MALDI-IMS of human lung cancer tissues. Multiple tumor specific ceramide and sphingomyelin species were detected and confirmed by on-tissue enzyme digests and structural confirmation. High-resolution MALDI-IMS in combination with novel on-tissue ceramidase and sphingomyelinase enzyme digestions makes it now possible to rapidly visualize the distribution of bioactive ceramides and sphingomyelin in 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".