SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors

• Published in: Cancer research (Chicago, Ill.) Vol. 85; no. 1; pp. 171 - 182
• Main Authors: Liu, Xinyi, Tang, Gongyu, Chen, Yuhao, Li, Yuanxiang, Li, Hua, Wang, Xiaowei
• Format: Journal Article
• Language: English
• Published: United States 02.01.2025
• Subjects: Animals; Cluster Analysis; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; Humans; Mice; Neoplasms - genetics; Neoplasms - pathology; Single-Cell Analysis - methods; Transcriptome; Tumor Microenvironment - genetics
• ISSN: 0008-5472; 1538-7445; 1538-7445
• 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.