Single-cell transcriptomic profiling of human kidneys uncovers profound principal cell responses to diabetic conditions, enabling targeted drug discovery
- 1Department of Genetics and Molecular Biology, Isfahan University of Medical Sciences, Isfahan, Iran
- 2Regenerative Medicine Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
Abstract
Background:: Ranked as the fifth leading global health burden in 2022, diabetes mellitus is the driver of diabetic kidney disease (DKD), a condition responsible for nearly half of end-stage kidney disease cases. Despite its significant impact, therapeutic progress in DKD is hindered by an incomplete understanding of the underlying cell-specific molecular mechanisms.
Materials and Methods:: Single-cell RNA sequencing data (GSE183276) from the kidney precision medicine project, including kidney samples from 20 healthy individuals and 14 DKD patients, were obtained. After quality control, batch effect correction, and cell type annotation, cell type-specific differentially expressed genes were identified. Enrichment analyses were performed using WebGestalt. Trajectory inference was conducted to characterize dynamic cellular states. Finally, DrugTar, a previously developed deep learning tool, was applied to identify the most druggable genes.
Results:: Among the 22 annotated cell types, collecting duct principal cells exhibited the largest number of differentially expressed genes, even after adjustment for cell abundance. This finding was also observed in an independent human kidney single-cell RNA-seq dataset (GSE211785), suggesting that this cell type experiences a profound transcriptional reprogramming in response to a diabetic milieu. Notably, trajectory analysis revealed distinct transcriptional states within principal cells, with mitochondrial metabolic processes being enriched across these subpopulations. Furthermore, a list of the most druggable genes is proposed as potential candidates for targeted delivery to principal cells.
Conclusion:: This study underscores the significant transcriptional reprogramming of principal cells in DKD pathogenesis, a phenomenon that has rarely received attention in previous works. These cells can be considered as candidates in strategies aimed at targeted drug delivery for DKD.
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BibTeX
@article{hamidifarid2026singlecell,
title = {{Single-cell transcriptomic profiling of human kidneys uncovers profound principal cell responses to diabetic conditions, enabling targeted drug discovery}},
author = {Zahra Hamidifarid and Yousof Gheisari},
journal = {Journal of Research in Medical Sciences : The Official Journal of Isfahan University of Medical Sciences},
year = {2026},
month = aug,
doi = {10.4103/jrms.jrms_1331_25},
url = {https://doi.org/10.4103/jrms.jrms_1331_25},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - JOUR
TI - Single-cell transcriptomic profiling of human kidneys uncovers profound principal cell responses to diabetic conditions, enabling targeted drug discovery
AU - Zahra Hamidifarid
AU - Yousof Gheisari
PY - 2026
DA - 2026/08/28
JO - Journal of Research in Medical Sciences : The Official Journal of Isfahan University of Medical Sciences
DO - 10.4103/jrms.jrms_1331_25
UR - https://doi.org/10.4103/jrms.jrms_1331_25
AB - Background:: Ranked as the fifth leading global health burden in 2022, diabetes mellitus is the driver of diabetic kidney disease (DKD), a condition responsible for nearly half of end-stage kidney disease cases. Despite its significant impact, therapeutic progress in DKD is hindered by an incomplete understanding of the underlying cell-specific molecular mechanisms. Materials and Methods:: Single-cell RNA sequencing data (GSE183276) from the kidney precision medicine project, including kidney samples from 20 healthy individuals and 14 DKD patients, were obtained. After quality control, batch effect correction, and cell type annotation, cell type-specific differentially expressed genes were identified. Enrichment analyses were performed using WebGestalt. Trajectory inference was conducted to characterize dynamic cellular states. Finally, DrugTar, a previously developed deep learning tool, was applied to identify the most druggable genes. Results:: Among the 22 annotated cell types, collecting duct principal cells exhibited the largest number of differentially expressed genes, even after adjustment for cell abundance. This finding was also observed in an independent human kidney single-cell RNA-seq dataset (GSE211785), suggesting that this cell type experiences a profound transcriptional reprogramming in response to a diabetic milieu. Notably, trajectory analysis revealed distinct transcriptional states within principal cells, with mitochondrial metabolic processes being enriched across these subpopulations. Furthermore, a list of the most druggable genes is proposed as potential candidates for targeted delivery to principal cells. Conclusion:: This study underscores the significant transcriptional reprogramming of principal cells in DKD pathogenesis, a phenomenon that has rarely received attention in previous works. These cells can be considered as candidates in strategies aimed at targeted drug delivery for DKD.
SN - 1735-1995
SN - 1735-7136
ER -