Histology imaging is an important tool in medical diagnosis and research, enabling the examination of tissue structure and composition at the microscopic level. Understanding the underlying molecular mechanisms of tissue architecture is critical in uncovering disease mechanisms and developing effective treatments. Gene expression profiling provides insight into the molecular processes underlying tissue architecture, but the process can be time-consuming and expensive. We present BLEEP (Bi-modaL Embedding for Expression Prediction), a bi-modal embedding framework capable of generating spatially resolved gene expression profiles of whole-slide Hematoxylin and eosin (H&E) stained histology images. BLEEP uses contrastive learning to construct a low-dimensional joint embedding space from a reference dataset using paired image and expression profiles at micrometer resolution. With this approach, the gene expression of any query image patch can be imputed using the expression profiles from the reference dataset. We demonstrate BLEEP's effectiveness in gene expression prediction by benchmarking its performance on a human liver tissue dataset captured using the 10x Visium platform, where it achieves significant improvements over existing methods. Our results demonstrate the potential of BLEEP to provide insights into the molecular mechanisms underlying tissue architecture, with important implications in diagnosis and research of various diseases. The proposed approach can significantly reduce the time and cost associated with gene expression profiling, opening up new avenues for high-throughput analysis of histology images for both research and clinical applications.

翻译：组织病理学成像是医学诊断与研究中的重要工具，可在微观层面实现组织结构与组成的检测。理解组织结构的潜在分子机制对揭示疾病机理及开发有效治疗方案至关重要。基因表达谱分析能够揭示组织结构背后的分子过程，但该过程往往耗时且昂贵。本文提出BLEEP（双模态嵌入表达预测框架），一种能从苏木精-伊红（H&E）染色全切片组织病理图像中生成空间分辨率基因表达谱的双模态嵌入框架。BLEEP利用对比学习，通过微米级分辨率的配对图像与表达谱参考数据集构建低维联合嵌入空间。采用该方法，可利用参考数据集中的表达谱对任意查询图像斑块的基因表达进行估算。通过在10x Visium平台采集的人类肝脏组织数据集上对基因表达预测性能进行基准测试，验证了BLEEP的有效性，其相较于现有方法取得显著提升。研究结果表明，BLEEP具有揭示组织结构分子机制的潜力，对多种疾病的诊断与研究具有重要意义。该方法可显著降低基因表达谱分析的时间与成本，为组织病理图像的高通量分析在科研与临床应用领域开辟新途径。