Spatial transcriptomics (ST) enables transcriptome-wide profiling while preserving the spatial context of tissues, offering unprecedented opportunities to study tissue organization and cell-cell interactions in situ. Despite recent advances, existing methods often lack effective integration of histological morphology with molecular profiles, relying on shallow fusion strategies or omitting tissue images altogether, which limits their ability to resolve ambiguous spatial domain boundaries. To address this challenge, we propose MultiST, a unified multimodal framework that jointly models spatial topology, gene expression, and tissue morphology through cross-attention-based fusion. MultiST employs graph-based gene encoders with adversarial alignment to learn robust spatial representations, while integrating color-normalized histological features to capture molecular-morphological dependencies and refine domain boundaries. We evaluated the proposed method on 13 diverse ST datasets spanning two organs, including human brain cortex and breast cancer tissue. MultiST yields spatial domains with clearer and more coherent boundaries than existing methods, leading to more stable pseudotime trajectories and more biologically interpretable cell-cell interaction patterns. The MultiST framework and source code are available at https://github.com/LabJunBMI/MultiST.git.

翻译：空间转录组学（ST）能够在保持组织空间背景的同时进行全转录组分析，为原位研究组织结构和细胞间相互作用提供了前所未有的机遇。尽管近期取得进展，现有方法往往缺乏组织学形态与分子谱的有效整合，依赖于浅层融合策略或完全忽略组织图像，这限制了其解析模糊空间区域边界的能力。为应对这一挑战，我们提出MultiST，一个统一的多模态框架，通过基于交叉注意力的融合共同建模空间拓扑、基因表达和组织形态。MultiST采用基于图的基因编码器与对抗对齐学习鲁棒的空间表示，同时整合颜色归一化的组织学特征以捕获分子-形态依赖性并优化区域边界。我们在涵盖两个器官（包括人类大脑皮层和乳腺癌组织）的13个多样化ST数据集上评估了所提方法。与现有方法相比，MultiST产生的空间区域边界更清晰、更一致，从而获得更稳定的伪时间轨迹和更具生物学可解释性的细胞间相互作用模式。MultiST框架及源代码可在https://github.com/LabJunBMI/MultiST.git获取。