Spatial omics has transformed our understanding of tissue architecture by preserving spatial context of gene expression patterns. Simultaneously, advances in imaging AI have enabled extraction of morphological features describing the tissue. The intersection of spatial omics and imaging AI presents opportunities for a more holistic understanding. In this review we introduce a framework for categorizing spatial omics-morphology combination methods, focusing on how morphological features can be translated or integrated into spatial omics analyses. By translation we mean finding morphological features that spatially correlate with gene expression patterns with the purpose of predicting gene expression. Such features can be used to generate super-resolution gene expression maps or infer genetic information from clinical H&E-stained samples. By integration we mean finding morphological features that spatially complement gene expression patterns with the purpose of enriching information. Such features can be used to define spatial domains, especially where gene expression has preceded morphological changes and where morphology remains after gene expression. We discuss learning strategies and directions for further development of the field.

翻译：空间组学通过保留基因表达模式的空间背景，彻底改变了我们对组织结构的理解。与此同时，成像人工智能的进步使得描述组织的形态学特征提取成为可能。空间组学与成像人工智能的交叉为更全面的理解提供了机遇。在本综述中，我们提出了一个用于分类空间组学-形态学组合方法的框架，重点关注形态学特征如何被转化或整合到空间组学分析中。所谓转化，是指寻找与基因表达模式在空间上相关的形态学特征，旨在预测基因表达。此类特征可用于生成超分辨率基因表达图谱，或从临床H&E染色样本中推断遗传信息。所谓整合，是指寻找在空间上补充基因表达模式的形态学特征，旨在丰富信息。此类特征可用于定义空间区域，特别是在基因表达先于形态学变化以及基因表达后形态特征仍然保留的情况下。我们讨论了该领域进一步发展的学习策略与方向。