Targeted proteomic approach for quantification of collagen type I and type III in formalin-fixed paraffin-embedded tissue
Bibliographic record
Abstract
Collagen is the most abundant protein in mammals and a major structural component of the extracellular matrix (ECM). Changes to ECM composition occur as a result of numerous physiological and pathophysiological causes, and a common means to evaluate these changes is the collagen 3 (Col3) to collagen 1 (Col1) ratio. Current methods to measure the Col3/1 ratio suffer from a lack of specificity and often under- or over-estimate collagen composition and quantity. This manuscript presents a targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) method for quantification of Col3 and Col1 in FFPE tissues. Using surrogate peptides to generate calibration curves, Col3 and Col1 are readily quantified in FFPE tissue sections with high accuracy and precision. The method is applied to several tissue types from both human and reindeer sources, demonstrating its generalizability. In addition, the targeted LC-MS/MS method permits quantitation of the hydroxyprolinated form of Col3, which has significant implications for understanding not only the quantity of Col3 in tissue, but also understanding of the pathophysiology underlying many causes of ECM changes. This manuscript presents a straightforward, accurate, precise, and generalizable method for quantifying the Col3/1 ratio in a variety of tissue types and organisms.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".