Mosaicism is more than meets the eye: transcriptional consequences of blastocyst mosaicism
Bibliographic record
Abstract
Blastocyst mosaicism is increasingly detected due to advances in preimplantation genetic testing for aneuploidy (PGT-A). While some mosaic embryos result in a live birth, there are reports of altered implantation potential. Various approaches are needed to better understand their developmental uniqueness, including their transcriptomic profile. This study aimed to profile the transcriptomic signatures of mosaic embryos and investigate how mosaicism influences global gene expression. Utilizing a novel method enabling simultaneous sequencing of both genomic (g)DNA, for PGT-A, and mRNA, we analyzed 76 blastocysts from 60 IVF-ICSI patients, including 29 euploid, 23 mosaic, and 24 aneuploid embryos. Transcriptomic analysis revealed that 14% of annotated genes are differentially expressed (DE) between mosaic and euploid embryos, with the majority of genes being upregulated in mosaic embryos. We further divided the mosaic cohort into mosaic-gain (mosaic trisomy) and mosaic-loss (mosaic monosomy) embryos and identified a core set of 902 DE genes that are shared regardless of the direction of the mosaicism (gain or loss). Pathway analysis revealed significant upregulation in pathways associated with cell cycle regulation, mitochondrial respiration, DNA repair, and vesicle transport. Following leading-edge analysis, genes previously annotated to be involved in embryo implantation were downregulated in mosaic embryos. Separately, in a subset of aneuploid embryos, we identified gene dosage effects; while embryos with trisomies 18 and 21 exhibit transcriptional signatures similar to euploid embryos, those with trisomies 16 and 22 demonstrate more divergent profiles, correlating with their previously reported implantation and clinical outcomes. These findings enhance current literature on the transcriptomic consequences of mosaicism in the trophectoderm of embryos. They suggest that mosaic blastocysts exhibit transcriptional signatures that reflect their mixed chromosomal composition, potentially influencing their implantation efficiency and developmental potential. Despite the inherent cellular stress, a proportion of mosaics retain developmental resilience, underscoring the complexity of embryo selection in ART.
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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 | 0.000 | 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) | 0.001 | 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 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".