Relationship of quantitative reverse transcription polymerase chain reaction (RT-PCR) to RNA Sequencing (RNAseq) transcriptome identifies mouse preimplantation embryo reference genes
Bibliographic record
Abstract
Numerous reference genes for use with quantitative reverse transcription polymerase chain reaction (RT-qPCR) have been used for oocytes, eggs, and preimplantation embryos. However, none are actually suitable because of their large variations in expression between developmental stages. To address this, we produced a standardized and merged RNA sequencing (RNAseq) data set by combining multiple publicly available RNAseq data sets that spanned mouse GV oocytes, MII eggs, and 1-cell, 2-cell, 4-cell, 8-cell, morula, and blastocyst stage embryos to identify transcripts with essentially constant expression across all stages. Their expression was then measured using RT-qPCR, with which they did not exhibit constant expression but instead revealed a fixed quantitative relationship between measurements by the two techniques. From this, the relative amounts of total messenger RNA at each stage from the GV oocyte through blastocyst stages were calculated. The quantitative relationship between measurements by RNAseq and RT-qPCR was then used to find genes predicted to have constant expression across stages in RT-qPCR. Candidates were assessed by RT-qPCR to confirm constant expression, identifying Hmgb3 and Rb1cc1 or the geometric mean of those plus either Taf1d or Cd320 as suitable reference genes. This work not only identified transcripts with constant expression from mouse GV oocytes to blastocysts, but also determined a general quantitative relationship between expression measured by RNAseq and RT-qPCR across stages that revealed the relative levels of total mRNA at each stage. The standardized and merged RNA data set should also prove useful in determining transcript expression in mouse oocytes, eggs, and embryos.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".