Single-Cell Omics Decoding Cellular Heterogeneity in Kidney Diseases
A special issue of Cells (ISSN 2073-4409). This special issue belongs to the section "Cellular Pathology".
Deadline for manuscript submissions: 30 November 2026 | Viewed by 148
Editor
Interests: translational research; single cell sequencing; bioinformatics; transcriptome; signal transduction; podocytes; renal physiology; diabetes; glomerular diseases; molecular cell biology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue aims to highlight recent advances in single-cell and related omics technologies that are reshaping our understanding of kidney biology and disease. We welcome original research articles, reviews, and methodological studies that apply single-cell transcriptomics, epigenomics, proteomics, spatial omics, and multi-omics integration to investigate cellular heterogeneity in both acute and chronic kidney diseases. Topics of interest include the identification of rare or disease-associated cell states, immune and stromal cell dynamics, cell–cell communication, and microenvironmental remodeling during injury, repair, fibrosis, and disease progression. We also encourage submissions utilizing artificial intelligence and advanced computational approaches to integrate single-cell, spatial, imaging, and clinical datasets as well as studies using organoid and experimental models to uncover novel mechanisms, biomarkers, and therapeutic targets. As Cells aims to provide an advanced forum for studies in cell biology, molecular biology, and biophysics, manuscripts based solely on computational or bioinformatic data analysis, without molecular or cellular validation, will not be considered. By bridging technological innovation with clinical relevance, this Special Issue seeks to advance our understanding of kidney disease heterogeneity and accelerate progress toward personalized therapeutic strategies.
Dr. Jia Fu
Guest Editor
Manuscript Submission Information
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Keywords
- single-cell omics
- spatial omics
- multi-omics integration
- artificial intelligence
- machine learning
- kidney disease
- cellular heterogeneity
- cell–cell communication
- organoids
- immune microenvironment
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