DetecDiv, a generalist deep-learning platform for automated cell division tracking and survival analysis.
Fiche publication
Date publication
août 2022
Journal
eLife
Auteurs
Membres identifiés du Cancéropôle Est :
Dr CHARVIN Gilles
Tous les auteurs :
Aspert T, Hentsch D, Charvin G
Lien Pubmed
Résumé
Automating the extraction of meaningful temporal information from sequences of microscopy images represents a major challenge to characterize dynamical biological processes. So far, strong limitations in the ability to quantitatively analyze single-cell trajectories have prevented large-scale investigations to assess the dynamics of entry into replicative senescence in yeast. Here, we have developed DetecDiv, a microfluidic-based image acquisition platform combined with deep learning-based software for high-throughput single-cell division tracking. We show that DetecDiv can automatically reconstruct cellular replicative lifespans with high accuracy and performs similarly with various imaging platforms and geometries of microfluidic traps. In addition, this methodology provides comprehensive temporal cellular metrics using time-series classification and image semantic segmentation. Last, we show that this method can be further applied to automatically quantify the dynamics of cellular adaptation and real-time cell survival upon exposure to environmental stress. Hence, this methodology provides an all-in-one toolbox for high-throughput phenotyping for cell cycle, stress response, and replicative lifespan assays.
Mots clés
S. cerevisiae, cell biology, computational biology, systems biology
Référence
Elife. 2022 08 17;11: