Spatial transcriptomics is a rapidly growing field that promises to comprehensively characterize tissue organization and architecture at the single-cell or subcellular resolution. Such information provides a solid foundation for mechanistic understanding of many biological processes in both health and disease that cannot be obtained by using traditional technologies. The development of computational methods plays important roles in extracting biological signals from raw data. Various approaches have been developed to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical biases. Downstream analysis tools formulate spatial organization and cell–cell communications as quantifiable properties, and provide algorithms to derive such properties. Integrative pipelines further assemble multiple tools in one package, allowing biologists to conveniently analyze data from beginning to end. Overview of spatial transcriptomics analysis methods.
Through spacial proteomics:
Analysis can be performed on the image itself, ranging from earlytasks such as cell segmentation to support of subcellular analysis through cell shape and size classification.
Cell types can be identified through clustering and annotation. Additional integration with external scRNA-seq data or deconvolution of spatial units that cover multiple cells can be performed to finetune cell type mapping.
The spatial distribution of cell types and the underlying cell-to-cell communication can be computed.
Spatial expression patterns are identified and visualized based on information of gene expression and spatial coordinates.
Data at subcellular resolution can be used to identify spatial and temporal dynamics of transcripts within a single cell.
