Biology subjects

Labrune, E.

Publications and source records attributed to Labrune, E..

4 recordsLinked to original sources

bioRxiv2026

The post-transcriptional RNA splicing landscape driving oocyte division

During growth in ovaries, mammalian oocytes accumulate maternal transcripts, proteins, and metabolites that support meiotic divisions and embryogenesis. As oocytes become fully grown, RNA Polymerase II is degraded and transcription is effectively silenced. However, RNA splicing appears to be required for oocyte division, a requirement which remains poorly characterized. Here, we define this post-transcriptional RNA splicing landscape of fully grown mouse oocytes and develop a computational resource that identifies post-transcriptional splicing events, signatures, sequence features, and protein binding motifs responsive to splicing perturbation. We show that, unlike transcription, RNA splicing in fully grown oocytes is essential for meiotic progression. Applying our resource across perturbations, we identify hundreds of genes whose proper splicing is required for oocyte division and reveal that distinct perturbations converge on overlapping regulatory programs controlling cytoskeletal organization and cell cycle progression that drive oocyte division. We experimentally validate selected resource-derived predictions using chemical and protein-based splicing perturbations. These findings demonstrate that extensive RNA processing persists after transcription has ceased and position post-transcriptional splicing as a fundamental regulator of mammalian oocyte development.

developmental biology↗

bioRxiv2025

Atomic Force Microscopy reveals differences in mechanical properties linked to cortical structure in mouse and human oocytes

Cell mechanical properties regulate biological processes such as oocyte development. Cortical tension is regulated via actomyosin cortex remodeling to ensure optimal oocyte quality. However, the evolution of other mechanical parameters and their relationship with cortex structure remain poorly understood in mammalian oocytes. In this work, we propose a methodology combining multiple mechanical parameters measured through Atomic Force Microscopy to investigate the relationship between oocyte mechanical properties and cortex organization. By studying mouse oocytes at various stages of development, along with engineered ones with specific cortex organization, we demonstrate that a thin actin cortex corresponds to stiff oocytes while a thick one is associated with softer oocytes. We further reveal that maternal age, a critical factor for fertility, affects mouse oocytes mechanics correlating with alterations in their cortex structure. Finally, we show that the evolution of mechanical properties differs between human and mouse oocyte development, highlighting species-specific differences in cortex organization.

biophysics↗

bioRxiv2025

Non-invasive characterization of oocyte deformability in micro-constrictions

Oocytes naturally present mechanical defects that hinder their development after fertilization. Thus, in the context of assisted reproduction, oocyte selection based on their mechanical properties has great potential to improve the quality of the resulting embryos and the success rate of these procedures. However, the use of mechanical properties as a quantifiable selective criterion requires robust and non-destructive measurement tools. This study developed a constriction-based microfluidic device that monitors the deformation of mouse oocytes under controlled pressure. The device can distinguish mechanically aberrant oocyte groups from healthy control ones. Based on a mathematical model, we propose that deformability measurements infer both oocyte tension and elasticity, elasticity being the most discriminating factor in our geometry. Despite force transmission during oocyte deformation, no long-term damage was observed. This non-invasive characterization of mouse oocyte deformability in micro-constrictions allows for a significant advance in assessing the mechanical properties of mammalian oocytes and has potential application as a quantifiable selective criterion in medically assisted reproduction. TeaserA measurement method for translating advances in oocyte mechanics into selection criteria for improving medically assisted reproduction.

developmental biology↗

bioRxiv2022

An interpretable and versatile machine learning approach for oocyte phenotyping

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 in the texture of the zona pellucida and the cytoplasmic particles size. Eventually, we tested whether these mouse oocyte quality features were applicable to human oocytes developmental potential.

bioinformatics↗