Sequence models for continuous cell cycle stage prediction from brightfield images

TL;DR

This paper evaluates deep learning models for predicting continuous cell cycle stages from label-free brightfield images, demonstrating that sequence models like causal and transformer-based approaches outperform simpler methods and enable high-resolution, non-invasive cell cycle tracking.

Contribution

It provides a comprehensive comparison of deep learning models for cell cycle prediction from brightfield images, highlighting the effectiveness of sequence models in this context.

Findings

Sequence models outperform single-frame approaches.

High-resolution prediction of G1/S transition within 1 hour.

Large dataset of 1.3 million images used for evaluation.

Abstract

Understanding cell cycle dynamics is crucial for studying biological processes such as growth, development and disease progression. While fluorescent protein reporters like the Fucci system allow live monitoring of cell cycle phases, they require genetic engineering and occupy additional fluorescence channels, limiting broader applicability in complex experiments. In this study, we conduct a comprehensive evaluation of deep learning methods for predicting continuous Fucci signals using non-fluorescence brightfield imaging, a widely available label-free modality. To that end, we generated a large dataset of 1.3 M images of dividing RPE1 cells with full cell cycle trajectories to quantitatively compare the predictive performance of distinct model categories including single time-frame models, causal state space models and bidirectional transformer models. We show that both causal and transformer-based models significantly outperform single- and fixed frame approaches, enabling the prediction of visually imperceptible transitions like G1/S within 1h resolution. Our findings underscore the importance of sequence models for accurate predictions of cell cycle dynamics and highlight their potential for label-free imaging.