Towards Improving Embryo Prioritization: Parallel Next Generation Sequencing of DNA and RNA from a Single Trophectoderm Biopsy
Bibliographic record
Abstract
Improved embryo prioritization is crucial in optimizing the results in assisted reproduction, especially in light of increasing utilization of elective single embryo transfers. Embryo prioritization is currently based on morphological criteria and in some cases incorporates preimplantation genetic testing for aneuploidy (PGT-A). Recent technological advances have enabled parallel genomic and transcriptomic assessment of a single cell. Adding transcriptomic analysis to PGT-A holds promise for better understanding early embryonic development and implantation, and for enhancing available embryo prioritization tools. Our aim was to develop a platform for parallel genomic and transcriptomic sequencing of a single trophectoderm (TE) biopsy, that could later be correlated with clinical outcomes. Twenty-five embryos donated for research were utilized; eight for initial development and optimization of our method, and seventeen to demonstrate clinical safety and reproducibility of this method. Our method achieved 100% concordance for ploidy status with that achieved by the classic PGT-A. All sequencing data exceeded quality control metrics. Transcriptomic sequencing data was sufficient for performing differential expression (DE) analysis. All biopsies expressed specific TE markers, further validating the accuracy of our method. Using PCA, samples clustered in euploid and aneuploid aggregates, highlighting the importance of controlling for ploidy in every transcriptomic assessment.
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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 | 0.000 | 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".