MorphGen可生成多细胞类型、多干预的荧光显微图像，保持亚细胞结构细节。
MorphGen: Controllable and Morphologically Plausible Generative Cell-Imaging
- 基于扩散模型，联合生成多通道荧光图像，保留各细胞器结构
- 在多个细胞类型上生成图像，FID比MorphoDiff降低35%以上
- 适合药物筛选与基因编辑的高通量成像模拟研究
模拟体外细胞对干预措施的响应是加速基于高内涵成像检测的重要方向，对推动药物发现和基因编辑具有重要意义。为此，我们提出MorphGen——一种先进的基于扩散的荧光显微生成模型，支持跨多种细胞类型和扰动条件的可控生成。为捕捉与已知细胞形态一致的生物学有意义模式，MorphGen通过对齐损失将其表示与OpenPhenom（一种先进的生物基础模型）的表型嵌入对齐。与以往将多通道染色压缩为RGB图像的方法不同（导致细胞器特异性细节丢失），MorphGen联合生成完整荧光通道，保留各细胞器结构，支持精细的形态分析，对生物学解释至关重要。我们通过CellProfiler特征验证了生成图像的生物学一致性；MorphGen的FID得分比先前最先进的MorphoDiff（仅生成单细胞类型RGB图像）降低35%以上。代码已公开于https://github.com/czi-ai/MorphGen。
原文摘要 · Abstract (English)
Simulating in silico cellular responses to interventions is a promising direction to accelerate high-content image-based assays, critical for advancing drug discovery and gene editing. To support this, we introduce MorphGen, a state-of-the-art diffusion-based generative model for fluorescent microscopy that enables controllable generation across multiple cell types and perturbations. To capture biologically meaningful patterns consistent with known cellular morphologies, MorphGen is trained with an alignment loss to match its representations to the phenotypic embeddings of OpenPhenom, a state-of-the-art biological foundation model. Unlike prior approaches that compress multichannel stains into RGB images -- thus sacrificing organelle-specific detail -- MorphGen generates the complete set of fluorescent channels jointly, preserving per-organelle structures and enabling a fine-grained morphological analysis that is essential for biological interpretation. We demonstrate biological consistency with real images via CellProfiler features, and MorphGen attains an FID score over 35% lower than the prior state-of-the-art MorphoDiff, which only generates RGB images for a single cell type. Code is available at https://github.com/czi-ai/MorphGen.
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