Protocol for in vivo chromatin immunoprecipitation on purified chromatin isolated from mouse liver nuclei
Bibliographic record
Abstract
Chromatin immunoprecipitation (ChIP) is used to investigate genome binding by transcription factors, but it can be problematic. We present a protocol to isolate fixed DNA-protein complexes from mouse liver prior to chromatin shearing. We describe steps for liver disaggregation and cross-linking, DNA-protein complex isolation, chromatin shearing, and quality control analysis as well as procedures for ChIP, DNA purification, and ChIP analysis. This protocol yields high-quality samples using commercial antibodies. For complete details on the use and execution of this protocol, please refer to Akl et al. 1 • Steps for conducting ChIP on in vivo mouse liver samples • Protocol for purifying fixed DNA-protein complexes from enriched nuclear fractions • Instructions for chromatin shearing and performing quality control analysis • Detailed procedure for analyzing samples after high-quality ChIP Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Chromatin immunoprecipitation (ChIP) is used to investigate genome binding by transcription factors, but it can be problematic. We present a protocol to isolate fixed DNA-protein complexes from mouse liver prior to chromatin shearing. We describe steps for liver disaggregation and cross-linking, DNA-protein complex isolation, chromatin shearing, and quality control analysis as well as procedures for ChIP, DNA purification, and ChIP analysis. This protocol yields high-quality samples using commercial antibodies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.074 | 0.054 |
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".