Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology. Image-based spatial transcriptomics technologies now provide high-resolution measurements of cell images and gene expression profiles, but existing methods typically analyze these modalities in isolation or at limited resolution. We address the problem by introducing SPATIA, a multi-level generative and predictive model that learns unified, spatially aware representations by fusing morphology, gene expression, and spatial context from the cell to the tissue level. SPATIA also incorporates a spatially conditioned generative framework with confidence-aware OT reweighting and morphology-profile alignment for modeling target-state morphology distributions. Specifically, we propose a confidence-aware flow matching objective that reweights weak optimal-transport pairs based on uncertainty. We further apply morphology-profile alignment to encourage biologically meaningful image generation, enabling the modeling of microenvironment-dependent phenotypic transitions. We assembled a multi-scale dataset consisting of 25.9 million cell-gene pairs across 17 tissues. We benchmark SPATIA against 18 models across 12 tasks, spanning categories such as phenotype generation, annotation, clustering, gene imputation, and cross-modal prediction. SPATIA achieves improved performance over state-of-the-art models, improving generative fidelity by 8% and predictive accuracy by up to 3%.

翻译：理解细胞形态、基因表达与空间背景如何共同塑造组织功能是生物学领域的核心挑战。基于图像的空间转录组学技术现可提供细胞图像与基因表达谱的高分辨率测量，但现有方法通常孤立分析这些模态或分辨率有限。我们通过提出SPATIA模型解决该问题，这是一个多层级生成与预测模型，通过融合从细胞到组织水平的形态学、基因表达与空间背景，学习统一的、空间感知的表征。SPATIA还整合了空间条件生成框架，结合置信度感知的OT重加权与形态-表达谱对齐，用于建模目标状态下的形态分布。具体而言，我们提出一种置信度感知的流匹配目标函数，基于不确定性对弱最优传输对进行重加权。进一步应用形态-表达谱对齐以促进具有生物学意义的图像生成，从而实现对微环境依赖性表型转变的建模。我们构建了一个涵盖17种组织、包含2590万个细胞-基因对的多尺度数据集。在12项任务（涵盖表型生成、注释、聚类、基因插补、跨模态预测等类别）上，将SPATIA与18个模型进行了基准对比。SPATIA在生成保真度上提升8%，预测精度最高提升3%，性能超越现有最优模型。