MALDI Imaging of Formalin-Fixed Paraffin-Embedded Tissues: Application to Model Animals of Parkinson Disease for Biomarker Hunting
Bibliographic record
Abstract
A common technique for the long-term storage of tissues in hospitals and clinical laboratories is preservation in formalin-fixed paraffin-embedded (FFPE) blocks. Such tissues stored for more than five years have not been useful for proteomic studies focused on biomarker discovery. Recently, MS-based proteomic analyses of FFPE showed positive results on blocks stored for less than 2 days. However, most samples are stored for more than one year, and thus our objective was to establish a novel strategy using as a model system 6-hydroxydopamine (6-OHDA) treated rat brain tissues stored in FFPE blocks for more than 9 years. We examined MALDI tissue profiling combining the use of automatic spotting of the MALDI matrix with in situ tissue enzymatic digestion. On adjacent sections, the identification of compounds is carried out by tissue digestion followed by nanoLC/MS-MS analysis. The combination of these approaches provides MALDI direct analysis, MALDI/MS imaging, as well as the localization of a large number of proteins. This method is validated since the analyses confirmed that ubiquitin, trans-elongation factor 1, hexokinase, and the Neurofilament M are down-regulated as previously shown in human or Parkinson animal models. In contrast, peroxidoredoxin 6, F1 ATPase, and alpha-enolase are up-regulated. In addition, we uncovered three novel putative biomarkers, the trans-elongation factor 1 (eEF1) and the collapsin response mediator 1 and 2 from protein libraries. Finally, we validate the CRMP-2 protein using immunocytochemistry and MALDI imaging based on the different ions from trypsic digestion of the protein. The access to archived FFPE tissue using MALDI profiling and imaging opens a whole new area in clinical studies and biomarker discovery from hospital biopsy libraries.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".