Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China.
Central Laboratory of Stomatology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China.
Sci Adv. 2024 Sep 27;10(39):eadp4942. doi: 10.1126/sciadv.adp4942.
Tumor tissues exhibit a complex spatial architecture within the tumor microenvironment (TME). Spatially resolved transcriptomics (SRT) is promising for unveiling the spatial structures of the TME at both cellular and molecular levels, but identifying pathology-relevant spatial domains remains challenging. Here, we introduce SpaTopic, a statistical learning framework that harmonizes spot clustering and cell-type deconvolution by integrating single-cell transcriptomics and SRT data. Through topic modeling, SpaTopic stratifies the TME into spatial domains with coherent cellular organization, facilitating refined annotation of the spatial architecture with improved performance. We assess SpaTopic across various tumor types and show accurate prediction of tertiary lymphoid structures and tumor boundaries. Moreover, marker genes derived from SpaTopic are transferrable and can be applied to mark spatial domains in other datasets. In addition, SpaTopic enables quantitative comparison and functional characterization of spatial domains across SRT datasets. Overall, SpaTopic presents an innovative analytical framework for exploring, comparing, and interpreting tumor SRT data.
肿瘤组织在肿瘤微环境（TME）中呈现出复杂的空间结构。空间分辨转录组学（SRT）有望在细胞和分子水平上揭示 TME 的空间结构，但识别与病理学相关的空间域仍然具有挑战性。在这里，我们介绍了 SpaTopic，这是一种统计学习框架，通过整合单细胞转录组学和 SRT 数据，协调斑点聚类和细胞类型去卷积。通过主题建模，SpaTopic 将 TME 分层为具有一致细胞组织的空间域，从而可以更精细地注释空间结构，并提高性能。我们在各种肿瘤类型中评估了 SpaTopic，并显示出对三级淋巴结构和肿瘤边界的准确预测。此外，来自 SpaTopic 的标记基因是可转移的，可以应用于标记其他数据集的空间域。此外，SpaTopic 可以实现 SRT 数据集之间空间域的定量比较和功能特征描述。总的来说，SpaTopic 为探索、比较和解释肿瘤 SRT 数据提供了一种创新的分析框架。
