Digestion-Free Analysis of Peptides from 30-year-old Formalin-Fixed, Paraffin-Embedded Tissue by Mass Spectrometry Imaging
Bibliographic record
Abstract
Formalin-fixed neuroendocrine tissues from American cockroaches ( Periplaneta americana) embedded in paraffin more than 30 years ago were recently analyzed by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), to reveal the histological localization of more than 20 peptide ions. These represented protonated, and other cationic species of, at least, 14 known neuropeptides. The characterization of peptides in such historical samples was made possible by a novel sample preparation protocol rendering the endogenous peptides readily amenable to MSI analysis. The protocol comprises brief deparaffinization steps involving xylene and ethanol, and is further devoid of conventional aqueous washing, buffer incubations, or antigen retrieval steps. Endogenous secretory peptides that are typically highly soluble are therefore retained in-tissue with this protocol. The method is fully "top-down", that is, without laborious in situ enzymatic digestion that typically disturbs the detection of low-abundance endogenous peptides by MSI. Peptide identifications were supported by accurate mass, on-tissue tandem MS analyses, and by earlier MALDI-MSI results reported for freshly prepared P. americana samples. In contrast to earlier literature accounts stating that MALDI-MSI detection of endogenous peptides is possible only in fresh or freshly frozen tissues, or exceptionally, in formalin-fixed, paraffin-embedded (FFPE) material of less than 1 year old, we demonstrate that MALDI-MSI works for endogenous peptides in FFPE tissue of up to 30 years old. Our findings put forward a useful method for digestion-free, high-throughput analysis of endogenous peptides from FFPE samples and offer the potential for reinvestigating archived and historically interesting FFPE material, such as those stored in hospital biobanks.
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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) | 0.001 | 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".