O-086 Survival of the fittest: unravelling aneuploid cell depletion in mosaic embryos
Bibliographic record
Abstract
Abstract Study question What molecular mechanisms trigger aneuploid cell depletion in post-implantation mosaic embryos, enabling self-correction and pregnancy success? Summary answer Aneuploid cell depletion in day 9-11 mosaic embryos is driven by disrupted stress responses, pluripotency dysregulation and impaired energy expenditure, mediated by aneuploid-euploid cell competition. What is known already Embryo aneuploidy is a major cause of adverse pregnancy outcomes. However, mosaic embryos - especially those with low-level aneuploidy (<50%), have significantly higher developmental potential, resulting in ongoing pregnancy and live birth of normal babies. Although their implantation rates are lower than euploid embryos, multicenter data from over 3,500 mosaic embryo transfers show that only 1.2% of mosaicism persists during pregnancy and postnatally, suggesting a self-correction capacity in many mosaic embryos. Despite these promising outcomes, the use of mosaic embryos in IVF remains limited due to an incomplete understanding of the biological mechanisms underlying their developmental success. Study design, size, duration Donated preimplantation euploid, aneuploid and mosaic embryos from the CReATe Fertility Centre and Zouves Fertility Center (Veritas IRB protocol #16580) were cultured from day 5-6 to day 9-11. 3870 cells from 31 post-implantation embryos (aneuploid, n = 7; mosaic, n = 8; euploid, n = 16) were included for single-cell RNA sequencing (scRNA-seq) analysis. An iPSC-based mosaic model was established by co-culturing euploid and reversine-treated (aneuploid) naive iPSCs in a transwell system for 96 hours. Participants/materials, setting, methods Cultured embryos were dissociated into single cells and pooled for scRNA-seq (10x Chromium). Sequencing results were analyzed using Freemuxlet to identify embryo-of-origin, Seurat for clustering, and InferCNV to determine post-implantation cell ploidy. Lineage developmental trajectory was inferred from pseudotime analysis. Enriched genetic markers and pathways were identified through differential expression analyses. RT-qPCR and immunofluorescence assays were used to quantify gene and protein expression in iPSC models. Main results and the role of chance scRNA-seq data from 3870 embryonic cells revealed diverse post-implantation lineages, including epiblast, hypoblast, and multiple trophoblast subtypes. Transcriptomic and regulon analysis showed aneuploid cell survival was dependent on embryonic environments. In homogenous aneuploid embryos, we observed adaptive stress responses that support temporary survival and aneuploidy tolerance. This includes delayed differentiation across all lineages, upregulation of genes related to pluripotency (e.g. KLF4) and unfolded protein response (e.g. ATF4). In contrast, mosaic embryos exhibited complex cell competition dynamics, where aneuploid cells in mosaic embryos were driven toward pluripotency exit, evidenced by advanced progression along developmental trajectory and downregulated pluripotency genes. This shift was accompanied by disrupted homeostatic pathways, including glycolysis (e.g. MYC) and proteostasis (e.g. DDIT4), leading to elevated apoptotic activity (e.g. CASP8). Our iPSC-based mosaic model recapitulated differential fitness levels in mosaic embryos. In a mosaic environment, aneuploid iPSCs exhibited impaired viability and growth, along with significant downregulation of DDIT4 (log2FC=-3.16, p < 0.05), and KLF4 (log2FC=-0.54, p < 0.05). Enhanced autophagy was observed in euploid iPSCs, indicated by accumulation of LC3 complex, while aneuploid iPSCs displayed increased apoptotic signaling, marked by elevated Caspase-8 levels. Together, Our results pointed to a diminished survival advantage in the aneuploid cells of mosaic embryos, driving their selective depletion. Limitations, reasons for caution We compared the transcriptomic signatures of aneuploid cells in aneuploid and mosaic embryos. Expanding the sample size will improve generalizability of the findings. Furthermore, while the iPSC-based mosaic model mirrors key aspects of the mosaic environment, it may not fully recapitulate the complexity of in vivo embryonic development. Wider implications of the findings Our study unravels molecular mechanisms contributing to self-correction and pregnancy success of mosaic embryos. These results provide the foundation for interrogating developmental competence of mosaic embryos, potentially expanding the range of viable embryos suitable for IVF treatment and improving options for fertility preservation. Trial registration number No
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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".