A high-throughput DNA methylation analysis of a single cell
Bibliographic record
Abstract
In recent years, the field of epigenetics has grown dramatically and has become one of the most dynamic and fast-growing branches of molecular biology. The amount of diseases suspected of being influenced by DNA methylation is rising steadily and includes common diseases such as schizophrenia, bipolar disorder, Alzheimer's disease, diabetes, atherosclerosis, cancer, major psychosis, lupus and Parkinson's disease. Due to cellular heterogeneity of methylation patterns, epigenetic analyses of single cells become a necessity. One rationale is that DNA methylation profiles are highly variable across individual cells, even in the same organ, dependent on the function of the gene, disease state, exposure to environmental factors (e.g. radiation, drugs or nutrition), stochastic fluctuations and various other causes. Using a polymerase chain reaction (PCR)-slide microreaction system, we present here a methylation-sensitive PCR analysis, the restriction enzyme-based single-cell methylation assay (RSMA), in the analysis of DNA methylation patterns in single cells. This method addresses the problems of cell heterogeneity in epigenetics research; it is comparably affordable, avoids complicated microfluidic systems and offers the opportunity for high-throughput screening, as many single cells can be screened in parallel. In addition to this study, critical principles and caveats of single cell methylation analyses are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".