Cell painting is a popular technique for creating human-interpretable, high-contrast images of cell morphology. There are two major issues with cell paint: (1) it is labor-intensive and (2) it requires chemical fixation, making the study of cell dynamics impossible. We train a diffusion model (Morphological Observation Neural Enhancement Tool, or MONET) on a large dataset to predict cell paint channels from brightfield images. We show that model quality improves with scale. The model uses a consistency architecture to generate time-lapse videos, despite the impossibility of obtaining cell paint video training data. In addition, we show that this architecture enables a form of in-context learning, allowing the model to partially transfer to out-of-distribution cell lines and imaging protocols. Virtual cell painting is not intended to replace physical cell painting completely, but to act as a complementary tool enabling novel workflows in biological research.
@article{arxiv.2512.11928,
title = {MONET -- Virtual Cell Painting of Brightfield Images and Time Lapses Using Reference Consistent Diffusion},
author = {Alexander Peysakhovich and William Berman and Joseph Rufo and Felix Wong and Maxwell Z. Wilson},
journal= {arXiv preprint arXiv:2512.11928},
year = {2025}
}