Proteomic analysis of human biopsy samples by single two-dimensional electrophoresis: Coomassie, silver, mass spectrometry, and Western blotting
Bibliographic record
Abstract
Proteomic analysis of myocardial tissue from patient populations is critical to our understanding of cardiac disease, but has been limited until now by the small size of biopsies (approximately 20-50 microg), and complicated by the difference in relative abundance of contractile proteins over other cellular components. Here we describe an approach to analysis of myocardial biopsies from patients undergoing coronary artery bypass surgery. First, individual biopsies are selectively extracted, producing subfractions that correspond to the contractile proteins and the cytosolic proteins. Two-dimensional electrophoresis separated proteins are detected by first staining with Coomassie blue then silver, to permit a wider range of accurate quantification. Western blotting of two-dimensional separated samples, to validate peptide mass fingerprinting data, previously required additional gel separations for transfer since staining protocols are not compatible with transfer to membranes or immunoblotting. An existing silver destaining protocol was adapted to allow removal of silver from a whole gel, followed by transfer and Western blotting. An existing Coomassie blue removal protocol was also adapted to permit Western blotting of gels stained with Coomassie blue and silver. Together, these techniques permit peptide mass fingerprinting concurrent with Western blotting of a single protein spot from a single biopsy, eliminating the need for repeated gel separations, and improving spot alignment between immunoblots and stained gels. In the end, this approach may allow a more complete characterization of protein changes in small human biopsies, and also reduce the number of repeated gel separations necessary for a standard proteomic analysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".