Human and mouse oocytes' developmental potential can be predicted by their mechanical properties. Their development into blastocysts requires a specific stiffness window. In this study, we combine live-cell and computational imaging, laser ablation, and biophysical measurements to investigate how deregulation of cortex tension in the oocyte contributes to early developmental failure. We focus on extra-soft cells, the most common defect in a natural population. Using two independent tools to artificially decrease cortical tension, we show that chromosome alignment is impaired in extra-soft mouse oocytes, despite normal spindle morphogenesis and dynamics, inducing aneuploidy. The main cause is a cytoplasmic increase in myosin-II activity that could sterically hinder chromosome capture. We describe here an original mode of generation of aneuploidies that could be very common in oocytes and could contribute to the high aneuploidy rate observed during female meiosis, a leading cause of infertility and congenital disorders.

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Separase proteolytically removes cohesin complexes from sister chromatid arms in meiosis I, which is essential for chromosome segregation. Regulation of separase activity is essential for proper cell cycle progression and correct chromosome segregation. Onset of endogenous separase activity has not yet been observed in live oocytes.We describe here a method for detecting separase activity in mouse oocytes in vivo. This method utilizes a previously described cleavage sensor made up of H2B-mCherry fused with Scc1(107-268 aa)-YFP. The cleavage sensor is loaded on the chromosomes through its H2B-tag, and the signal from both mCherry and YFP is visible. Upon separase activation the Scc1 fragment is cleaved and YFP dissociates from the chromosomes. The change in the ratio between mCherry and YFP fluorescence intensity is a readout of separase activity.

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Meiotic maturation is a crucial step of oocyte development allowing its potential fertilization and embryo development. Elucidating this process is important both for fundamental research and assisted reproductive technology. However, only few computational tools, based on non-invasive measurements, are currently available to characterize oocyte meiotic maturation. Here, we develop a computational framework to phenotype oocytes based on images or movies acquired exclusively in transmitted light. We first trained neural networks to segment the contours of oocytes and their zona pellucida using a diverse cohort of both mouse and human oocytes. We then defined a comprehensive set of morphological features to describe a single oocyte. We have implemented these steps in a versatile and user-friendly open source Fiji plugin available to the mouse and human oocyte community. Then, we present a machine learning pipeline based on selected features to automatically recognize oocyte populations and determine their morphological differences. Its first application is a novel approach to screen oocyte strains and automatically identify their morphological characteristics.We demonstrate its potential by phenotyping a well characterized genetically modified mouse oocyte strain. Its second application is to predict and characterize the maturation potential of oocytes. Importantly, we identify two new features to assess mouse oocyte maturation potential, consisting inthe texture of the zona pellucida and the cytoplasmic particles size. Eventually, we tested whether these mouse oocyte quality features were applicable to human oocyte's developmental potential.