Methyl CpG Binding Domain Ultra‐Sequencing: a novel method for identifying inter‐individual and cell‐type‐specific variation in <scp>DNA</scp> methylation
Why this work is in the frame
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Bibliographic record
Abstract
Experience-dependent changes in DNA methylation can exert profound effects on neuronal function and behaviour. A single learning event can induce a variety of DNA modifications within the neuronal genome, some of which may be common to all individuals experiencing the event, whereas others may occur in a subset of individuals. Variations in experience-induced DNA methylation may subsequently confer increased vulnerability or resilience to the development of neuropsychiatric disorders. However, the detection of experience-dependent changes in DNA methylation in the brain has been hindered by the interrogation of heterogeneous cell populations, regional differences in epigenetic states and the use of pooled tissue obtained from multiple individuals. Methyl CpG Binding Domain Ultra-Sequencing (MBD Ultra-Seq) overcomes current limitations on genome-wide epigenetic profiling by incorporating fluorescence-activated cell sorting and sample-specific barcoding to examine cell-type-specific CpG methylation in discrete brain regions of individuals. We demonstrate the value of this method by characterizing differences in 5-methylcytosine (5mC) in neurons and non-neurons of the ventromedial prefrontal cortex of individual adult C57BL/6 mice, using as little as 50 ng of genomic DNA per sample. We find that the neuronal methylome is characterized by greater CpG methylation as well as the enrichment of 5mC within intergenic loci. In conclusion, MBD Ultra-Seq is a robust method for detecting DNA methylation in neurons derived from discrete brain regions of individual animals. This protocol will facilitate the detection of experience-dependent changes in DNA methylation in a variety of behavioural paradigms and help identify aberrant experience-induced DNA methylation that may underlie risk and resiliency to neuropsychiatric disease.
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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 | 0.002 | 0.001 |
| 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 it