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DeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks
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DeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks
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DeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks
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Journal Article
DeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks
2020
Overview
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 FUCCI2 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.
Synopsis
DeepCycle is a deep neural network able to reconstruct a cyclic cell cycle trajectory from unsegmented cell images. The model is validated on cells undergoing a full cell cycle by comparing the progression of the inferred trajectory to real time.
The deep learning model DeepCycle reconstructs a cyclic cell cycle trajectory solely from unsegmented images in the Hoescht and Brightfield channels.
The model was trained using fluorescently labelled cell cycle markers from the FUCCI2 system.
The reconstructed DeepCycle pseudotime was validated by comparing its progression to the measured real cell cycle time of cells undergoing an entire cell cycle.
Graphical Abstract
DeepCycle is a deep neural network able to reconstruct a cyclic cell cycle trajectory from unsegmented cell images. The model is validated on cells undergoing a full cell cycle by comparing the progression of the inferred trajectory to real time.
