Open AccessDeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks
Author(s) -
Rappez Luca,
Rakhlin Alexander,
Rigopoulos Angelos,
Nikolenko Sergey,
Alexandrov Theodore
Publication year - 2020
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.15252/msb.20209474
Subject(s) - biology , cell cycle , convolutional neural network , cell , live cell imaging , microbiology and biotechnology , cell division , trajectory , microscopy , biological system , artificial intelligence , computer science , optics , physics , genetics , astronomy
The advent of single‐cell methods is paving the way for an in‐depth understanding of the cell cycle with unprecedented detail. Due to its ramifications in nearly all biological processes, the evaluation of cell cycle progression is critical for an exhaustive cellular characterization. Here, we present DeepCycle, a deep learning method for estimating a cell cycle trajectory from unsegmented single‐cell microscopy images, relying exclusively on the brightfield and nuclei‐specific fluorescent signals. DeepCycle was evaluated on 2.6 million single‐cell microscopy images of MDCKII cells with the fluorescent FUCCI 2 system. DeepCycle provided a latent representation of cell images revealing a continuous and closed trajectory of the cell cycle. Further, we validated the DeepCycle trajectories by showing its nearly perfect correlation with real time measured from live‐cell imaging of cells undergoing an entire cell cycle. This is the first model able to resolve the closed cell cycle trajectory, including cell division, solely based on unsegmented microscopy data from adherent cell cultures.