Protocol for antibody-based m6A sequencing of human postmortem brain tissues
Bibliographic record
Abstract
Here, we present an optimized N6-methyladenosine (m6A) immunoprecipitation sequencing for human postmortem brain tissue, building on previously established approaches. We describe steps for RNA extraction, RNA shearing, RNA immunoprecipitation, and library preparation. The protocol includes optimization data for each step, such as m6A profiles from total RNA versus poly(A) RNA as an input, a comparison of different commercial m6A antibodies, and immunoprecipitation with varying RNA/antibody ratios. This protocol allows reliable capture of m6A peaks from human postmortem brain tissue. For complete details on the use and execution of this protocol, please refer to Mitsuhashi et al. 1 • Steps for RNA extraction from human postmortem brain tissues • Instructions for mechanical RNA shearing prior to RNA immunoprecipitation • Procedures on RNA-IP with an m6A antibody followed by library preparation Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Here, we present an optimized N6-methyladenosine (m6A) immunoprecipitation sequencing for human postmortem brain tissue, building on previously established approaches. We describe steps for RNA extraction, RNA shearing, RNA immunoprecipitation, and library preparation. The protocol includes optimization data for each step, such as m6A profiles from total RNA versus poly(A) RNA as an input, a comparison of different commercial m6A antibodies, and immunoprecipitation with varying RNA/antibody ratios. This protocol allows reliable capture of m6A peaks from human postmortem brain tissue.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.033 |
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".