TRIDENT：面向药物与 RNA 条件细胞形态合成的三模态级联生成框架
TRIDENT: A Trimodal Cascade Generative Framework for Drug and RNA-Conditioned Cellular Morphology Synthesis
把转录组表达谱当作生成条件去合成 Cell Painting 细胞形态图像，为视觉研究者提供了一个「多模态条件级联生成 + 生物表型预测」的可复用范式：可用 RNA 条件替代或补充化学扰动条件，并借助 MorphoGene 这类配对数据集评估生成保真度与对未见化合物的泛化。
原文依据 we propose TRIDENT, a cascade generative framework that synthesizes realistic cellular morphology by conditioning on both the perturbation and the corresponding gene expression profile
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论准确建模扰动、转录响应与表型变化之间的关系，是构建 AI 虚拟细胞（AIVC）的关键。然而，现有方法通常局限于建模直接关联，例如「扰动→RNA」或「扰动→形态」，忽略了从 RNA 到形态这一关键因果链路。为弥补这一空白，我们提出 TRIDENT，一个级联生成框架，它以扰动及对应的基因表达谱为条件合成逼真的细胞形态。为训练与评估该任务，我们构建了 MorphoGene，这是一个将 L1000 基因表达与 Cell Painting 图像配对的新数据集，覆盖 98 种化合物。TRIDENT 显著优于最优方法，最高取得 7 倍提升，并对未见化合物具有强泛化能力。在多西他赛的案例研究中，我们验证了 RNA 引导的合成能够准确产生相应的表型。消融实验进一步确认，RNA 条件对该模型的高保真度至关重要。通过显式建模转录组—表型映射，TRIDENT 提供了一个强大的计算工具，并使我们在迈向可预测虚拟细胞的道路上更进一步。
英文原文摘要 1231 字符
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 -> RNA* or *Perturbation -> 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.