AI 中文总结
研究旨在从H&E图像预测空间基因表达，提出HistoGPA框架，通过两条并行路径利用局部形态、位置和载玻片上下文检索基因先验信息，在多种癌症类型上表现优异，推动了组织学至表达预测的上下文依赖观点。
AI 中文摘要
从常规苏木精和伊红（H&E）图像预测空间基因表达为实验性空间转录组学提供了实用补充。现有方法聚焦局部或多尺度视觉特征，常将预训练基因表示视为固定先验，而局部形态学解释和基因先验相关性取决于组织上下文。我们提出HistoGPA，一个上下文条件基因先验注意力框架，在两条并行路径中使用共享的载玻片级表示：一条调节局部形态特征，另一条对预训练基因嵌入进行条件设定并通过交叉注意力检索基因先验信息。该设计使每个空间位置能利用其局部形态、位置和载玻片上下文检索适应上下文的基因先验信息。在HEST-1k中的十种癌症类型上，HistoGPA在相同评估协议下，对于前50和前1500个高变基因集，在比较方法中实现了最高的宏观平均基因级皮尔逊相关系数。额外分析表明，HistoGPA能更好地恢复癌症相关基因的空间表达模式，且在从预测和真实表达谱独立衍生的簇之间产生更大一致性。这些发现推动了对组织学至表达预测的上下文依赖观点，即局部形态表示和基因先验共同适应更广泛的组织上下文。
英文摘要
Predicting spatial gene expression from routine hematoxylin and eosin (H&E) images provides a practical complement to experimental spatial transcriptomics. Existing approaches focus on local or multi-scale visual features and often treat pretrained gene representations as fixed priors, although the interpretation of local morphology and the relevance of gene priors depend on tissue context. We propose HistoGPA, a context-conditioned gene-prior attention framework that uses a shared slide-level representation in two parallel pathways: one modulates local morphological features, whereas the other conditions pretrained gene embeddings and retrieves gene-prior information through cross-attention. This design enables each spatial location to retrieve context-adapted gene-prior information using its local morphology, position, and slide context. Across ten cancer types in HEST-1k, HistoGPA achieves the highest macro-averaged gene-wise Pearson correlation coefficient among the compared methods under the same evaluation protocol for both the top-50 and top-1,500 highly variable gene sets. Additional analyses show that HistoGPA better recovers the spatial expression patterns of cancer-associated genes and yields greater agreement between clusters derived independently from predicted and ground-truth expression profiles. Together, these findings motivate a context-dependent view of histology-to-expression prediction, in which local morphological representations and gene priors are jointly adapted to the broader tissue context.
Comments8 pages, 2 figures, 3 tables
