用明场图像生成细胞染色图，实现动态观测与跨实验迁移。
MONET -- Virtual Cell Painting of Brightfield Images and Time Lapses Using Reference Consistent Diffusion
- 基于扩散模型，从明场图像预测荧光染色通道。
- 模型规模越大，生成质量越高，且支持时间序列重建。
- 可跨细胞系和成像协议迁移，适合生物动态研究场景。
细胞染色是生成高对比度、可解释的细胞形态图像的常用技术，但存在两大问题：(1) 操作繁琐，(2) 需要化学固定，无法用于动态研究。本文训练了一个扩散模型（称为MONET），在大规模数据上学习从明场图像预测细胞染色通道。结果显示，模型性能随规模提升而改善。该模型采用一致性架构，即使缺乏真实细胞染色视频训练数据，仍能生成时间序列图像。此外，该架构支持一种上下文学习形式，使模型能在未见的细胞系和成像协议间部分迁移。虚拟细胞染色并非完全替代物理染色，而是作为补充工具，推动生物学研究新流程的发展。
原文摘要 · Abstract (English)
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.
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