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.

Table 2 Top studies utilizing spatial transcriptomics in diabetic kidney disease

Number	Research topic	Application of ST and key issues resolved	Findings	Ref.
1	Discovery of disease-specific cell neighborhoods and signaling pathways	Used Slide-seqV2 technology to build cell-neighborhood maps and uncovered cell localization patterns and signaling networks	Revealed the cell interactions and signaling pathways in diabetic nephropathy	Marshall et al[15], Chen et al[16]
2	Epithelial-mesenchymal transition and interactions of renal tubular cells in DKD	Combined single-cell RNA sequencing with spatial transcriptomics to define the epithelial-mesenchymal transition of renal tubular epithelial cells and their interactions	Gained an in-depth understanding of the dynamic changes and mechanisms of renal tubular epithelial cells	Wang et al[17]
3	Immune cell infiltration patterns in DKD	Utilized spatial transcriptomic analysis to observe increases in specific immune cells in glomeruli	Clarified the role of immune cell infiltration in disease progression	Zhang et al[18]
4	Fibrosis-related protein biomarkers in DKD	Conducted spatial proteomic analysis to reveal late-stage fibrosis-related protein biomarkers	Provided potential diagnostic and therapeutic biomarkers	Hu et al[19]
5	Molecular mechanisms of renal tubular injury in DKD	Integrated spatial transcriptomic and proteomic analyses to find IL-32 upregulation and its mechanisms	Uncovered the key role of IL-32 in renal tubular injury	Chung et al[20]
6	Pathway alterations in DKD	Combined ST with multi-omics approaches to identify signaling pathways involved in DKD	Uncovered spatial alterations in inflammatory and apoptotic signaling pathways	Delrue and Speeckaert[21]
7	Involvement of AEBP1 in DKD	Spatial transcriptomic analysis of kidney biopsies from DKD patients	AEBP1 is notably upregulated and associated with fibrosis and inflammation	Tao et al[22]

DKD: Diabetic kidney disease; IL: Interleukin; ST: Spatial transcriptomics.

Table 3 Newcastle-Ottawa Scale quality assessment of included studies

Ref.	Selection				Comparability		Exposure			Sources
	A representative and well-defined study population	Method of sample selection	Non-response rate of study subjects	Additional confirmation of study subjects	Matching of study and control groups based on specific factors	Adjustment of study and control groups based on specific factors	Method of determining study outcomes	Blinded assessment of outcomes	Follow-up time and adequacy
Lay et al[1]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	No	7
Marshall et al[15]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	9
Chen et al[16]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	No	7
Winfree et al[23]	Yes	Yes	No	Yes	Yes	Yes	Yes	No	No	6
Zhang et al[18]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	No	7
Kondo et al[24]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	No	7
Tao et al[22]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	No	7

Marshall et al[15] scored 9/9, indicating very high quality with a highly rigorous study design and implementation. The remaining six studies scored 7/9, indicating good quality but with some unclear details, such as the blinding assessment and follow-up time.