Protocol for unified metabolomics and proteomics analysis of formalin-fixed paraffin-embedded tissue
Bibliographic record
Abstract
The use of archival formalin-fixed paraffin-embedded (FFPE) tissue samples for biochemical analyses is problematic because of the formation of a Schiff base, leading to low protein and metabolite yields during analytical extractions. Here, we overcome this issue using a unified protocol on FFPE tissue for metabolomics and proteomics analyses. Using 20 mg of wet mass tissue, this protocol consistently extracted more than 50 metabolites (across 11 classes of metabolites) and over 900 proteins. • Combined metabolomic and proteomic protocol for FFPE tissue samples • Can identify over 50 metabolites across 11 classes and over 800 proteins • Optimized method that produces reproducible results Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. The use of archival formalin-fixed paraffin-embedded (FFPE) tissue samples for biochemical analyses is problematic because of the formation of a Schiff base, leading to low protein and metabolite yields during analytical extractions. Here, we overcome this issue using a unified protocol on FFPE tissue for metabolomics and proteomics analyses. Using 20 mg of wet mass tissue, this protocol consistently extracted more than 50 metabolites (across 11 classes of metabolites) and over 900 proteins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".