Off-center spindle positioning in mammalian oocytes enables asymmetric divisions in size, important for subsequent embryogenesis. The migration of the meiosis I spindle from the oocyte center to its cortex is mediated by F-actin. Specifically, an F-actin-cage surrounds the microtubule spindle and applies forces to it. To better understand how F-actin transmits forces to the spindle, we studied a potential direct link between F-actin and microtubules. For this, we tested the implication of Myosin-X, a known F-actin and microtubule binder involved in spindle morphogenesis and/or positioning in somatic cells, amphibian oocytes and embryos. Using a mouse strain conditionally invalidated for Myosin-X in oocytes and by live cell imaging, we show that Myosin-X is not localized on the spindle and dispensable for spindle and F-actin assembly. It is not required for force transmission since spindle migration and chromosome alignment occur normally. More broadly, Myosin-X is dispensable for oocyte developmental potential and female fertility. We therefore exclude a role for Myosin-X in transmitting F-actin-mediated forces to the spindle, opening new perspectives regarding this mechanism in mouse oocytes, which differs from most mitotic cells.

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.