Selecting Normalizers for MicroRNA RT-qPCR Expression Analysis in Murine Preimplantation Embryos and the Associated Conditioned Culture Media
Bibliographic record
Abstract
Normalizing RT-qPCR miRNA datasets that encompass numerous preimplantation embryo stages requires the identification of miRNAs that may be used as stable reference genes. A need has also arisen for the normalization of the accompanying conditioned culture media as extracellular miRNAs may serve as biomarkers of embryo developmental competence. Here, we evaluate the stability of six commonly used miRNA normalization candidates, as well as small nuclear U6, using five different means of evaluation (BestKeeper, NormFinder, geNorm, the comparative Delta Ct method and RefFinder comprehensive analysis) to assess their stability throughout murine preimplantation embryo development from the oocyte to the late blastocyst stages, both in whole embryos and the associated conditioned culture media. In descending order of effectiveness, miR-16, miR-191 and miR-106 were identified as the most stable individual reference miRNAs for developing whole CD1 murine preimplantation embryos, while miR-16, miR-106 and miR-103 were ideal for the conditioned culture media. Notably, the widely used U6 reference was among the least appropriate for normalizing both whole embryo and conditioned media miRNA datasets. Incorporating multiple reference miRNAs into the normalization basis via a geometric mean was deemed beneficial, and combinations of each set of stable miRNAs are further recommended, pending validation on a per experiment basis.
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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 | 0.000 |
| 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 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".