Towards Improving Embryo Selection: Simultaneous Next Generation Sequencing Of DNA And RNA From A Single Trophectoderm Biopsy
Bibliographic record
Abstract
ABSTRACT Improved embryo selection is crucial in optimizing the results from assisted reproduction. Preimplantation genetic screening reduces time to pregnancy and miscarriages. Correlating the transcriptome of an embryo, with fertility treatments and outcomes, holds promise in improving the overall results. We developed a novel method for embryo selection in fertility treatments that integrates embryonic genomic and transcriptomic data and evaluated it in this pilot study. A total of 21 embryos donated for research were included. Three were used for the initial development and optimization of sample processing and sequencing. Thereafter, 18 embryos were used to demonstrate the clinical safety and reproducibility of our method. Two trophectoderm biopsies were taken from each embryo: one was processed as a clinical sample for genomic profiling (control, n=18), while the other biopsy (n=18) was split and utilized for independent, simultaneous genomic and transcriptomic analysis, here termed Preimplantation Genetic and Transcriptomic Testing (PGT 2 ). High quality genomic and transcriptomic data were obtained from all analyzed samples. The concordance between genomic data obtained with PGT 2 and control samples was 100% with clinical grade quality metrics. Euploid embryos showed downregulation of genes involved in anaerobic metabolism, oxidative phosphorylation, and fatty-acid oxidation. This is the first study to provide full genomic and transcriptomic profiles from a single TE biopsy from human embryos in a clinical setting unleashing the potential of improving embryo selection and outcomes in infertility treatments. Clinical trials are needed to correlate transcriptomic data with outcomes. SUMMARY Despite advances in assisted reproductive technologies, the success rate has remained relatively constant. Under the age of 35, there is a 40% chance of delivering a child per embryo transfer, which decreases with increasing maternal age. Prioritizing embryos for transfer is based on morphological assessment and, in some cases, incorporates genetic testing as well. Selection of euploid embryos for transfer shortens the time to pregnancy and reduces the risk for miscarriages. Adding the mRNA analysis to the genomic assessment of an embryo has the potential of improving the outcomes of fertility treatments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".