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 novel spatially conditioned generative framework for predicting cell morphologies under perturbations. 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、基因表达与空间环境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目前能够提供细胞图像与基因表达谱的高分辨率测量数据，但现有方法通常孤立地或在有限分辨率下分析这些模态。我们通过引入SPATIA来解决这一问题——这是一个多层级生成与预测模型，通过融合从细胞到组织层级的形态学、基因表达及空间上下文信息，学习统一且具有空间感知能力的表征。SPATIA还整合了一种新颖的空间条件生成框架，用于预测扰动条件下的细胞形态。具体而言，我们提出了一种置信感知流匹配目标函数，该函数基于不确定性对弱最优传输配对进行重新加权。我们进一步应用形态-表达谱对齐策略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换的建模。我们构建了一个多尺度数据集，涵盖17种组织中的2590万个细胞-基因配对。我们在12项任务（涵盖表型生成、注释、聚类、基因插补及跨模态预测等类别）中，将SPATIA与18个模型进行了基准测试。SPATIA在各项任务中均优于现有最先进模型，生成保真度提升8%，预测准确率最高提升3%。