Subcellular fractionation of frozen skeletal muscle samples
Bibliographic record
Abstract
Cell fractionation can be used to determine the localization and trafficking of proteins between cellular compartments such as the cytosol, mitochondria, and nuclei. Subcellular fractionation is usually performed immediately after tissue dissection because freezing may fragment cell membranes and induce organellar cross-contamination. Mitochondria are especially sensitive to freezing/thawing and mechanical homogenization. We proposed a protocol to improve the retention of soluble proteins in the mitochondrial fraction obtained from small amounts of frozen skeletal muscle. Fifty milligrams of the red portion of gastrocnemius muscle from Wistar rats were immediately processed or frozen in liquid nitrogen and stored at -80 °C for further processing. We compared the enrichment of subcellular fractions from frozen/fresh samples obtained with the modified protocol with those obtained by standard fractionation. Western blot analyses of marker proteins for cytosolic (alpha-tubulin), mitochondrial (VDAC1), and nuclear (histone-H3) fractions indicated that all of the procedures resulted in enriched subcellular fractions with minimal organellar cross-contamination. Notably, the activity of the soluble protein citrate synthase was higher in the mitochondrial fractions obtained with the modified protocol from frozen/fresh samples compared with the standard protocol. Therefore, our protocol improved the retention of soluble proteins in the mitochondrial fraction and may be suitable for subcellular fractionation of small amounts of frozen skeletal muscle samples.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".