A Deep Learning approach for time-consistent cell cycle phase prediction from microscopy data
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The cell cycle consists of four phases and impacts most cellular processes. In imaging assays, the cycle phase can be identified using dedicated cell-cycle markers. However, such markers occupy fluorescent channels that may be needed for other reporters. Here, we propose to address this limitation by inferring the phase from a widely used fluorescent reporter: SiR-DNA. Our method is based on a variational auto-encoder, enhanced with two auxiliary tasks: predicting the intensity of phase-specific markers and enforcing the latent space temporal consistency. Our model is freely available, along with a new dataset comprising over 600,000 annotated HeLa Kyoto nuclear images.
