Spatial transcriptomics meets diabetic kidney disease: Illuminating the path to precision medicine

Table 1 The differences of single-cell RNA sequencing and spatial transcriptomics

Characteristics	Single-cell RNA sequencing	Spatial transcriptomics
Resolution	Single-cell level	From near single-cell to tens of micrometers (most current ST platforms have a resolution of 50-100 μm)
Spatial information	Lost during tissue dissociation	Preserved, enabling in situ analysis of gene expression within tissue sections
Data complexity	High, requiring processing of large numbers of single-cell data and cell type identification	Very high, as data include gene expression matrices, spatial coordinates, and metadata related to tissue morphology and cell identity
Technical workflow	Tissue is dissociated into a single-cell suspension followed by sequencing	Tissue section processing, capturing gene expression information while preserving spatial location through specific technologies (e.g., Slide-seqV2)
Key advantages	Enables high-resolution profiling of individual cell transcriptomes and reveals cellular heterogeneity	Combines gene expression with spatial location, allowing analysis of cell-cell interactions, region-specific gene expression, and immune cell localization
Limitations	Loss of spatial context and inability to study the impact of tissue structure on function	Current resolution is insufficient for analyzing fine anatomical structures (e.g., glomeruli and tubules). ST also has high demands for sample preparation (e.g., tissue section thickness, integrity, and RNA quality)
Applications	Illuminates transcriptional dynamics of podocytes, tubular cells, and infiltrating immune cells in diabetic kidneys	Used in diabetic kidney disease research to analyze spatial interactions of cells in the renal microenvironment, lesion-specific gene expression patterns, immune infiltration, localized molecular alterations, and disease-associated pathway changes
Data analysis	Requires single-cell-specific tools (e.g., Seurat, Scanpy) for cell clustering and marker gene identification	Requires spatial analysis tools (e.g., SpaTrack, STlearn) for spatial clustering, gene pattern recognition, and cell-cell interaction modeling. Integration with machine learning and deep learning methods can enhance analytical capabilities
Typical output	Cell clusters, cell type-specific marker genes	Spatial cellular atlases, cell neighborhoods, spatially restricted gene expression patterns, disease-related pathway alterations

ST: Spatial transcriptomics.