用药物和基因表达联合生成细胞形态，提升虚拟细胞预测能力
TRIDENT: A Trimodal Cascade Generative Framework for Drug and RNA-Conditioned Cellular Morphology Synthesis
- 构建三模态级联生成框架，同时以药物和基因表达为条件生成细胞图像
- 在98种化合物上实现7倍性能提升，对未见药物具有强泛化能力
- 首次证明基因表达对细胞形态的决定性作用，适合药物研发与系统生物学研究
准确建模扰动、转录响应与表型变化之间的关系是构建人工智能虚拟细胞（AIVC）的关键。现有方法多局限于直接关联建模，如扰动→RNA或扰动→形态，忽略了从RNA到形态的重要因果链。为此，我们提出TRIDENT，一种级联生成框架，通过同时结合药物扰动和对应基因表达谱来合成真实细胞形态。为训练与评估该任务，我们构建了新数据集MorphoGene，包含98种化合物的L1000基因表达数据与Cell Painting图像。TRIDENT显著优于现有先进方法，在未见化合物上实现最高7倍性能提升，并具备强泛化能力。对多西他赛的案例研究表明，基于RNA引导的合成能准确生成对应表型；消融实验进一步证实RNA条件对模型高保真度至关重要。通过显式建模转录组-表型映射，TRIDENT提供强大体外仿真工具，推动向可预测虚拟细胞迈进。
原文摘要 · Abstract (English)
Accurately modeling the relationship between perturbations, transcriptional responses, and phenotypic changes is essential for building an AI Virtual Cell (AIVC). However, existing methods typically constrained to modeling direct associations, such as Perturbation $\rightarrow$ RNA or Perturbation $\rightarrow$ Morphology, overlook the crucial causal link from RNA to morphology. To bridge this gap, we propose TRIDENT, a cascade generative framework that synthesizes realistic cellular morphology by conditioning on both the perturbation and the corresponding gene expression profile. To train and evaluate this task, we construct MorphoGene, a new dataset pairing L1000 gene expression with Cell Painting images for 98 compounds. TRIDENT significantly outperforms state-of-the-art approaches, achieving up to 7-fold improvement with strong generalization to unseen compounds. In a case study on docetaxel, we validate that RNA-guided synthesis accurately produces the corresponding phenotype. An ablation study further confirms that this RNA conditioning is essential for the model's high fidelity. By explicitly modeling transcriptome-phenome mapping, TRIDENT provides a powerful in silico tool and moves us closer to a predictive virtual cell.
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