Droplet digital PCR shows the D-Loop to be an error prone locus for mitochondrial DNA copy number determination
Bibliographic record
Abstract
Absolute quantification of mitochondrial DNA copy number (mCN) provides important insights in many fields of research including cancer, cardiovascular and reproductive health. Droplet digital PCR (ddPCR) natively reports absolute copy number, and we have developed a single-dye, multiplex assay to measure rat mCN that is accurate, precise and affordable. We demonstrate simple methods to optimize this assay and to determine nuclear reference pseudogene copy number to extend the range of mCN that can be measured with this assay. We evaluated two commonly used mitochondrial DNA reference loci to determine mCN, the ND1 gene and the D-Loop. Harnessing the absolute measures of ddPCR, we found that the D-Loop amplifies with a copy number of ~1.0-1.5 relative to other sites on the mitochondrial genome. This anomalous copy number varied significantly between rats and tissues (aorta, brain, heart, liver, soleus muscle). We advocate for avoiding the D-Loop as a mitochondrial reference in future studies of mCN. Further, we report a novel approach to quantifying immunolabelled mitochondrial DNA that provides single-cell estimates of mCN that closely agree with the population analyses by ddPCR. The combination of these assays represents a cost-effective and powerful suite of tools to study mCN.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".