Liu Xinyi, Tang Gongyu, Chen Yuhao, Li Yuanxiang, Li Hua, Wang Xiaowei
Department of Pharmacology and Regenerative Medicine, University of Illinois at Chicago, Chicago, Illinois.
Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, Missouri.
Cancer Res. 2025 Jan 2;85(1):171-182. doi: 10.1158/0008-5472.CAN-24-1472.
The rapid development of spatial transcriptomics (ST) technologies has enabled transcriptome-wide profiling of gene expression in tissue sections. Despite the emergence of single-cell resolution platforms, most ST sequencing studies still operate at a multicell resolution. Consequently, deconvolution of cell identities within the spatial spots has become imperative for characterizing cell-type-specific spatial organization. To this end, we developed Spatial Deconvolution Explorer (SpatialDeX), a regression model-based method for estimating cell-type proportions in tumor ST spots. SpatialDeX exhibited comparable performance to reference-based methods and outperformed other reference-free methods with simulated ST data. Using experimental ST data, SpatialDeX demonstrated superior performance compared with both reference-based and reference-free approaches. Additionally, a pan-cancer clustering analysis on tumor spots identified by SpatialDeX unveiled distinct tumor progression mechanisms both within and across diverse cancer types. Overall, SpatialDeX is a valuable tool for unraveling the spatial cellular organization of tissues from ST data without requiring single-cell RNA-seq references. Significance: The development of a reference-free method for deconvolving the identity of cells in spatial transcriptomics datasets enables exploration of tumor architecture to gain deeper insights into the dynamics of the tumor microenvironment.
空间转录组学（ST）技术的迅速发展使得在组织切片中对基因表达进行全转录组分析成为可能。尽管出现了单细胞分辨率平台，但大多数ST测序研究仍在多细胞分辨率下进行。因此，对空间点内的细胞身份进行反卷积分析对于表征细胞类型特异性的空间组织变得至关重要。为此，我们开发了空间反卷积探索器（SpatialDeX），这是一种基于回归模型的方法，用于估计肿瘤ST点中的细胞类型比例。在模拟ST数据中，SpatialDeX表现出与基于参考的方法相当的性能，并且优于其他无参考方法。使用实验性ST数据，与基于参考和无参考的方法相比，SpatialDeX都显示出卓越的性能。此外，对由SpatialDeX识别出的肿瘤点进行的泛癌聚类分析揭示了不同癌症类型内部和之间独特的肿瘤进展机制。总体而言，SpatialDeX是一种有价值的工具，可用于从ST数据中解析组织的空间细胞组织，而无需单细胞RNA测序参考。意义：开发一种用于反卷积空间转录组学数据集中细胞身份的无参考方法，能够探索肿瘤结构，从而更深入地了解肿瘤微环境的动态变化。
