A Rapid and Efficient Method for the Extraction of Histone Proteins
Bibliographic record
Abstract
Current protocols used to extract and purify histones are notoriously tedious, especially when using yeast cells. Here, we describe the use of a simple filter-aided sample preparation approach enabling histone extraction from yeast and mammalian cells using acidified ethanol, which not only improves extraction but also inactivates histone-modifying enzymes. We show that our improved method prevents N-terminal clipping of H3, an artifact frequently observed in yeast cells using standard histone extraction protocols. Our method is scalable and provides efficient recovery of histones when extracts are prepared from as few as two million yeast cells. We further demonstrate the application of this approach for the analysis of histone modifications in fungal clinical isolates available in a limited quantity. Compared with standard protocols, our method enables the study of histones and their modifications in a faster, simpler, and more robust manner.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".