An interpretable and versatile machine learning approach for oocyte phenotyping

Author:

Letort Gaelle1,Eichmuller Adrien1,Da Silva Christelle1,Nikalayevich Elvira1,Crozet Flora1,Salle Jeremy2,Minc Nicolas2,Labrune Elsa3,Wolf Jean-Philippe45,Terret Marie-Emilie1,Verlhac Marie-Hélène1

Affiliation:

1. 1 Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, Université PSL, Paris, France

2. 2 Université Paris Cité, CNRS, Institut Jacques Monod, F-75013 Paris, France

3. 3 Service de Médecine de la Reproduction, Hôpital Femme Mère Enfant, Hospices Civils de Lyon, Bron, France; Université Claude Bernard Lyon 1, Lyon, France ; INSERM U1208, StemGamE, Bron, France

4. 4 Team ‘From Gametes To Birth’, Département « Développement, Reproduction, Cancer », Institut Cochin, Inserm U1016, CNRS UMR8104, Université de Paris, 22 rue Mechain, 75014 Paris, France

5. 5 Service d'Histologie-Embryologie-Biologie de la Reproduction, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, France

Abstract

Meiotic maturation is a crucial step of oocyte formation allowing its potential fertilization and embryo development. Elucidating this process is important both for fundamental research and assisted reproductive technology. Few computational tools based on non-invasive measurements are however available to characterize oocyte meiotic maturation. Here, we develop a computational framework to phenotype oocytes based on images acquired in transmitted light. We trained neural networks to segment the contour of oocytes and their zona pellucida using oocytes from diverse species. We defined a comprehensive set of morphological features to describe an oocyte. These steps are implemented in an open-source Fiji plugin. We present a feature based machine learning pipeline to recognize oocyte populations and determine their morphological differences. We first demonstrate its potential to screen oocyte from different strains and automatically identify their morphological characteristics. Its second application is to predict and characterize the maturation potential of oocytes. We identify the texture of the zona pellucida and the cytoplasmic particles size as features to assess mouse oocyte maturation potential and tested whether these features were applicable to human oocyte's developmental potential.

Funder

Investissements d'Avenir

Fondation pour la Recherche Medicale

Institut National Du Cancer

Agence Nationale de la Recherche

Publisher

The Company of Biologists

Subject

Cell Biology

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