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.

翻译：通过计算机模拟细胞对干预措施的响应，是加速基于高内涵图像分析的关键方向，对推动药物发现和基因编辑至关重要。为此，我们提出了MorphGen，这是一种基于扩散模型的最先进荧光显微镜图像生成模型，能够跨多种细胞类型和扰动实现可控生成。为捕捉与已知细胞形态一致的生物学意义模式，MorphGen在训练中采用对齐损失，使其表征与先进生物基础模型OpenPhenom的表型嵌入相匹配。与先前将多通道染色压缩为RGB图像（从而牺牲细胞器特异性细节）的方法不同，MorphGen联合生成完整的荧光通道集，保留了每个细胞器的结构，并支持对生物学解释至关重要的细粒度形态学分析。我们通过CellProfiler特征证明了生成图像与真实图像的生物学一致性，且MorphGen的FID分数比先前仅生成单一细胞类型RGB图像的最先进模型MorphoDiff降低了35%以上。代码发布于https://github.com/czi-ai/MorphGen。