Machine Learning Reveals The Effect of Maternal Age on The Mouse Pre-Implantation Embryo Morphokinetics

Advanced maternal age has a negative effect on mammalian pre-implantation embryo development. Yet, our understanding of the interplay between embryos’ morphology, developmental dynamics, and maternal age is still lacking since isolating the effect of maternal aging from other confounding factors in humans is challenging. In this work, we have developed an artificial intelligence platform that deciphers the effect of maternal age on mouse embryos’ developmental stage and captures tens of morphological properties and developmental dynamics. This allows a novel, quantitative description of the embryo’s developmental trajectory. We show that developmental timing, particularly in embryos from maternally aged females, is the most informative and predictive morphokinetic property. Analyzing the timing distributions reveals that viable embryos are confined into an age-independent temporal corridor. Yet, the deviation of non-viable embryos from the temporal corridor is age-dependent. Furthermore, there is a significant correlation between consecutive developmental stages transition times that diminishes in maternally old embryos. Overall, our results suggest that maternally old embryos’ most apparent morphokinetic property is the loss of temporal regulation.