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Neuronally differentiated macula densa cells regulate tissue remodeling and regeneration in the kidney
Georgina Gyarmati, … , Matthias Kretzler, János Peti-Peterdi
Georgina Gyarmati, … , Matthias Kretzler, János Peti-Peterdi
Published April 10, 2024
Citation Information: J Clin Invest. 2024;134(11):e174558. https://doi.org/10.1172/JCI174558.
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Research Article Nephrology Article has an altmetric score of 186

Neuronally differentiated macula densa cells regulate tissue remodeling and regeneration in the kidney

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Abstract

Tissue regeneration is limited in several organs, including the kidney, contributing to the high prevalence of kidney disease globally. However, evolutionary and physiological adaptive responses and the presence of renal progenitor cells suggest an existing remodeling capacity. This study uncovered endogenous tissue remodeling mechanisms in the kidney that were activated by the loss of body fluid and salt and regulated by a unique niche of a minority renal cell type called the macula densa (MD). Here, we identified neuronal differentiation features of MD cells that sense the local and systemic environment and secrete angiogenic, growth, and extracellular matrix remodeling factors, cytokines and chemokines, and control resident progenitor cells. Serial intravital imaging, MD nerve growth factor receptor and Wnt mouse models, and transcriptome analysis revealed cellular and molecular mechanisms of these MD functions. Human and therapeutic translation studies illustrated the clinical potential of MD factors, including CCN1, as a urinary biomarker and therapeutic target in chronic kidney disease. The concept that a neuronally differentiated key sensory and regulatory cell type responding to organ-specific physiological inputs controls local progenitors to remodel or repair tissues may be applicable to other organs and diverse tissue-regenerative therapeutic strategies.

Authors

Georgina Gyarmati, Urvi Nikhil Shroff, Anne Riquier-Brison, Dorinne Desposito, Wenjun Ju, Sean D. Stocker, Audrey Izuhara, Sachin Deepak, Alejandra Becerra Calderon, James L. Burford, Hiroyuki Kadoya, Ju-Young Moon, Yibu Chen, Markus M. Rinschen, Nariman Ahmadi, Lester Lau, Daniel Biemesderfer, Aaron W. James, Liliana Minichiello, Berislav V. Zlokovic, Inderbir S. Gill, Matthias Kretzler, János Peti-Peterdi

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Figure 3

MD cell transcriptome analysis.

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MD cell transcriptome analysis.
(A) Workflow of MD and control cell isol...
(A) Workflow of MD and control cell isolation for transcriptome analysis in control (normal salt [NS]) and low-salt (LS) conditions. Scale bar: 25 μm. (B) Graphical summary of MD single-cell transcriptome analysis. The top activated (indicated by orange, positive Z-score) biological activities, pathways, and genes are listed based on unbiased IPA analysis. IPA system node shapes and colors are used (octagon, function; square, cytokine; triangle, kinase; ellipse, transcription regulator). (C) UMAP visualization (top) of integrated MD single-cell transcriptomic analysis from a single mouse in LS conditions. Graph-based analysis in Partek Flow identified 5 clusters (MD1–5). Top enriched genes (Fabp3, Egf, Ccn1, Foxq1, Cxcl12, Vash2, Pamr1, Vegfa, Nov) show clustering. Violin plot (bottom) of genes that are highly enriched in all 5 MD clusters. (D) Heatmap (mean expression) of top enriched MD-specific genes in MD vs. control cells in control (normal salt) and physiological stimulation (low salt + ACEi [LS]) conditions using bulk RNA analysis (n = 2 mice for MD, n = 4 mice for control cells from each condition). Genes were grouped into 5 categories as indicated according to their biological function. Scale indicates Z-score values.

Copyright © 2025 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

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