Recently, the research collaboration among the Bioinformatics & Intelligent Medicine team, Southern Medical University Zhujiang Hospital, Tianjin Medical University Cancer Hospital, etc. has resulted in the publication titled “Precise detection of cell-type-specific domains in spatial transcriptomics” in the journal Cell Reports Methods.

In clinical practice, accurately dissecting the spatial heterogeneity of cell types within tissues is crucial for understanding disease mechanisms, assessing prognostic risks, and developing personalized treatment strategies. However, traditional spatial domain analysis methods face significant challenges in spatial transcriptomics (SRT) research, particularly when detecting the spatial localization of low-proportion cell types. These cells often overlap with or are located within other high-proportion cell types, making it difficult to identify them accurately.

To the challenges above, the research team proposed the novel concept of "cell-type-specific domains", which are further expected to be spatially heterogeneous domains where the proportions of given cell types are significantly higher than other domains. Compared to conventional spatial domains, this concept can better characterize the anatomical regions where low-proportion cell types reside in spatial transcriptomics. Further, the research team developed De-spot that enables precise detection of cell-type-specific domains in both spot-level and single-cell SRT slices. De-spot bridges the gap between cell-type proportions and spatial domains by integrating potential candidates, inferring enrichment regions of cell-type proportions, and mapping enrichment regions to spatial domains. De-spot takes advantage of segmentation and deconvolution simultaneously, making it more effective in selecting cell-type-specific domains as ROIs without manual fine-tuning, including dealing with low-proportion cell types.

Furthermore, the research team also developed a new spatial multi-modal data format (SMD) with extensible interfaces, which are compatible with multiple SRT platforms, greatly enhancing its versatility and practicality. To visually present the cell type-specific domains, the research team also developed the 3D Landscape, enabling researchers to gain a more intuitive understanding of the distribution and interactions of cell type-specific domains in space.

De-spot not only addresses the limitations of current computational methods in detecting cell type-specific spatial domains but also provides a new perspective for exploring complex biological processes such as the tumor microenvironment (TME).

Experimental evaluation showed that De-spot discovered the co-localizations between cancer-associated fibroblasts and immune-related cells that indicate potential tumor microenvironment (TME) domains in given slices, which were obscured by previous computational methods. The research team further elucidated the identified domains and found that Srgn may be a critical TME marker in SRT slices. By deciphering T cell-specific domains in breast cancer tissues, De-spot also revealed that the proportions of exhausted T cells were significantly increased in invasive vs. ductal carcinoma. By precisely identifying cell-type-specific domains, De-spot offers new insights into disease mechanisms and the optimization of treatment strategies in clinical research.

doi：https://doi.org/10.1016/j.crmeth.2024.100841

github：https://github.com/lyotvincent/Despot