Sequence models for continuous cell cycle stage prediction from brightfield images
Journal:
arXiv
Published Date:
Feb 4, 2025
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.
