Peng Xiyu, Smithy James W, Yosofvand Mohammad, Kostrzewa Caroline E, Bleile MaryLena, Ehrich Fiona D, Lee Jasme, Postow Michael A, Callahan Margaret K, Panageas Katherine S, Shen Ronglai
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Department of Statistics, Texas A&M University, College Station, TX, USA.
Nat Commun. 2025 Jul 18;16(1):6619. doi: 10.1038/s41467-025-61821-y.
Recent progress in multiplexed tissue imaging is deepening our understanding of tumor microenvironments related to treatment response and disease progression. However, analyzing whole-slide images with millions of cells remains computationally challenging, and few methods provide a principled approach for integrative analysis across images. Here, we introduce SpatialTopic, a spatial topic model designed to decode high-level spatial tissue architecture from multiplexed images. By integrating both cell type and spatial information, SpatialTopic identifies recurrent spatial patterns, or "topics," that reflect biologically meaningful tissue structures. We benchmarked SpatialTopic across diverse single-cell spatial transcriptomic and proteomic imaging platforms spanning multiple tissue types. We show that SpatialTopic is highly scalable to large-scale images, along with high precision and interpretability. It consistently identifies biologically and clinically significant spatial topics, such as tertiary lymphoid structures, and tracks spatial changes over disease progression. Its computational efficiency and broad applicability will enhance the analysis of large-scale imaging datasets.
多重组织成像的最新进展正在加深我们对与治疗反应和疾病进展相关的肿瘤微环境的理解。然而，分析包含数百万个细胞的全切片图像在计算上仍然具有挑战性，并且很少有方法提供跨图像进行综合分析的原则性方法。在这里，我们介绍SpatialTopic，一种空间主题模型，旨在从多重图像中解码高级空间组织结构。通过整合细胞类型和空间信息，SpatialTopic识别反映生物学上有意义的组织结构的反复出现的空间模式或“主题”。我们在跨越多种组织类型的不同单细胞空间转录组学和蛋白质组学成像平台上对SpatialTopic进行了基准测试。我们表明，SpatialTopic对大规模图像具有高度可扩展性，同时具有高精度和可解释性。它始终能识别出生物学和临床上重要的空间主题，如三级淋巴结构，并跟踪疾病进展过程中的空间变化。其计算效率和广泛适用性将增强对大规模成像数据集的分析。
