整合生物学知识以实现De Novo细胞系的鲁棒显微图像分析
Integrating Biological Knowledge for Robust Microscopy Image Profiling on De Novo Cell Lines
AI总结：
针对de novo细胞系显微图像分析因异质性导致的泛化难题，提出整合外部生物学知识（如STRING和Hetionet知识图谱及单细胞转录组特征）的新框架，显式解耦扰动与细胞系特异性表征，显著提升了模型在真实药物发现场景中的鲁棒性。
摘要：
高通量筛选技术，如对基因和化学扰动的细胞响应进行显微成像，在药物发现和生物医学研究中发挥着至关重要的作用。然而，由于不同细胞系间存在显著的形态和生物学异质性，对de novo细胞系进行鲁棒的扰动筛选仍具挑战。为此，我们提出一种将外部生物学知识整合到现有预训练策略中的新框架，以增强显微图像分析模型。该方法利用外部生物学信息，显式解耦扰动特异性和细胞系特异性表征。具体而言，我们利用来自STRING和Hetionet数据库的蛋白质相互作用数据构建知识图谱，引导模型在预训练时关注扰动特异性特征。此外，我们引入来自单细胞基础模型的转录组特征，以捕获细胞系特异性表征。通过学习这些解耦特征，我们的方法提升了成像模型对de novo细胞系的泛化能力。我们在RxRx数据库上评估了该框架，通过对RxRx1细胞系进行单样本微调，并对RxRx19a数据集中的细胞系进行少样本微调。实验结果表明，该方法增强了对de novo细胞系的显微图像分析，突显了其在现实世界基于表型的药物发现应用中的有效性。
英文摘要：
High-throughput screening techniques, such as microscopy imaging of cellular responses to genetic and chemical perturbations, play a crucial role in drug discovery and biomedical research. However, robust perturbation screening for \textit{de novo} cell lines remains challenging due to the significant morphological and biological heterogeneity across cell lines. To address this, we propose a novel framework that integrates external biological knowledge into existing pretraining strategies to enhance microscopy image profiling models. Our approach explicitly disentangles perturbation-specific and cell line-specific representations using external biological information. Specifically, we construct a knowledge graph leveraging protein interaction data from STRING and Hetionet databases to guide models toward perturbation-specific features during pretraining. Additionally, we incorporate transcriptomic features from single-cell foundation models to capture cell line-specific representations. By learning these disentangled features, our method improves the generalization of imaging models to \textit{de novo} cell lines. We evaluate our framework on the RxRx database through one-shot fine-tuning on an RxRx1 cell line and few-shot fine-tuning on cell lines from the RxRx19a dataset. Experimental results demonstrate that our method enhances microscopy image profiling for \textit{de novo} cell lines, highlighting its effectiveness in real-world phenotype-based drug discovery applications.
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