Cui Yan, Yuan Zhiyuan
Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
Nat Commun. 2025 Jul 2;16(1):6095. doi: 10.1038/s41467-025-61476-9.
Spatially resolved transcriptomics enables mapping of multiplexed gene expression within tissue contexts. While existing methods prioritize spatially variable genes within a single slice, few address identifying genes with differential spatial expression patterns (DSEPs) across multiple conditions-an critical need for complex experimental designs. Challenges include modeling cross-slice spatial variation, scalability to large datasets, and disentangling inter-slice heterogeneity. We introduce DSEP gene prioritization as a new analytical task and present River, an interpretable deep learning framework that identifies genes exhibiting condition-relevant spatial changes. River features a two-branch predictive architecture and a post hoc attribution strategy to rank genes (or other features) by their contribution to condition differences. Its spatially-informed modeling ensures scalability to large spatial datasets, and we further decouple spatial and non-spatial components to enhance interpretability. We evaluate River on simulations and apply it to diverse biological contexts, including embryogenesis, diabetes-affected spermatogenesis, and lupus-associated splenic changes. In triple-negative breast cancer, River prioritizes survival-associated spatial patterns that generalize across patients. River is distribution-agnostic and compatible with diverse spatial data types, offering a flexible and scalable solution for analyzing tissue-wide expression dynamics across multiple biological conditions.
空间分辨转录组学能够在组织环境中绘制多重基因表达图谱。虽然现有方法优先考虑单个切片内空间可变的基因，但很少有方法能够识别跨多种条件下具有差异空间表达模式（DSEP）的基因，而这对于复杂的实验设计至关重要。挑战包括对跨切片空间变异进行建模、对大型数据集的可扩展性以及解析切片间的异质性。我们引入DSEP基因优先级排序作为一项新的分析任务，并提出River，这是一个可解释的深度学习框架，用于识别表现出与条件相关空间变化的基因。River具有双分支预测架构和事后归因策略，可根据基因（或其他特征）对条件差异的贡献对其进行排序。其基于空间信息的建模确保了对大型空间数据集的可扩展性，并且我们进一步解耦了空间和非空间成分以增强可解释性。我们在模拟中评估了River，并将其应用于多种生物学背景，包括胚胎发育、糖尿病影响的精子发生以及狼疮相关的脾脏变化。在三阴性乳腺癌中，River确定了在患者中具有普遍性的与生存相关的空间模式。River与分布无关，并且与多种空间数据类型兼容，为分析跨多种生物学条件的全组织表达动态提供了一种灵活且可扩展的解决方案。
