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 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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.063 | 0.038 |
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".