Embryo Development Archives - The NOMIS Foundation

Recent advances in stem cell-derived embryo models have transformed developmental biology, offering insights into embryogenesis without the constraints of natural embryos. However, variability in their development challenges research standardization. To address this, we use deep learning to enhance the reproducibility of selecting stem cell-derived embryo models. Through live imaging and AI-based models, we classify 900 mouse post-implantation stem cell-derived embryo-like structures (ETiX-embryos) into normal and abnormal categories. Our best-performing model achieves 88% accuracy at 90 h post-cell seeding and 65% accuracy at the initial cell-seeding stage, forecasting developmental trajectories. Our analysis reveals that normally developed ETiX-embryos have higher cell counts and distinct morphological features such as larger size and more compact shape. Perturbation experiments increasing initial cell numbers further supported this finding by improving normal development outcomes. This study demonstrates deep learning’s utility in improving embryo model selection and reveals critical features of ETiX-embryo self-organization, advancing consistency in this evolving field.

A key feature of many developmental systems is their ability to self-organize spatial patterns of functionally distinct cell fates. To ensure proper biological function, such patterns must be established reproducibly, by controlling and even harnessing intrinsic and extrinsic fluctuations. While the relevant molecular processes are increasingly well understood, we lack a principled framework to quantify the performance of such stochastic self-organizing systems. To that end, we introduce an information-theoretic measure for self-organized fate specification during embryonic development. We show that the proposed measure assesses the total information content of fate patterns and decomposes it into interpretable contributions corresponding to the positional and correlational information. By optimizing the proposed measure, our framework provides a normative theory for developmental circuits, which we demonstrate on lateral inhibition, cell type proportioning, and reaction–diffusion models of self-organization. This paves a way toward a classification of developmental systems based on a common information-theoretic language, thereby organizing the zoo of implicated chemical and mechanical signaling processes.

Retrospective lineage reconstruction of humans predicts that dramatic clonal imbalances in the body can be traced to the 2-cell stage embryo. However, whether and how such clonal asymmetries arise in the embryo is unclear. Here, we performed prospective lineage tracing of human embryos using live imaging, non-invasive cell labeling, and computational predictions to determine the contribution of each 2-cell stage blastomere to the epiblast (body), hypoblast (yolk sac), and trophectoderm (placenta). We show that the majority of epiblast cells originate from only one blastomere of the 2-cell stage embryo. We observe that only one to three cells become internalized at the 8-to-16-cell stage transition. Moreover, these internalized cells are more frequently derived from the first cell to divide at the 2-cell stage. We propose that cell division dynamics and a cell internalization bottleneck in the early embryo establish asymmetry in the clonal composition of the future human body.

Publications in Embryo Development by NOMIS researchers

AI-based approach to dissect the variability of mouse stem cell-derived embryo models

Information content and optimization of self-organized developmental systems

The first two blastomeres contribute unequally to the human embryo