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Two new studies examining kidney disease at both the cellular and protein levels provided novel insights into disease biology, suggesting paths toward more precise and individualized therapies for patients with kidney disease.

A study published in Nature Genetics suggested that applying single-cell tools at the patient level could usher in a new era of diagnostics and therapeutics.

Researchers used artificial intelligence to identify and catalog 70 distinct kidney cell types across human and animal samples, creating an integrated single-cell kidney atlas from more than 1 million cells collected across 140 samples. In turn, this work led to the development of CellSpectra, an open-source tool that quantifies changes in gene expression coordination across cellular functions.

“CellSpectra can be applied to individual patients to yield biological insights about their specific disease,” said study co-investigator Nancy Zhang, PHD, the Ge Li and Ning Zhao professor of statistics and data science at the Wharton School, University of Pennsylvania, Philadelphia. Zhang emphasized, however, that while this work represents “an important step toward clinical applications,” larger studies and clinical trials are required before it can be practically applied.

If validated, these types of biological insights could lead to improved methods for early detection of kidney diseases and more personalized management of chronic kidney disease (CKD).

Cellular Level Disease Insights

Utilizing the massive dataset, known as SISKA 1.0 Atlas, the researchers were able to detect disease-related problems at the cellular level. Through cross-species and patient-to-patient comparisons, they moved beyond differential gene expression analysis to assess pathway-level changes in coordination.

“This allows you to identify cell type-level signatures, which can, for example, serve as a ‘report card’ for a patient’s disease,” Zhang told Medscape Medical News. 

Proteomics vs Gene Expression

A separate study, published in Nature Medicine, found that protein levels in kidney cells often do not match gene activity. This discordance suggests that genetic data alone cannot fully explain disease mechanisms.

The findings point to a broader understanding of kidney disease biology, extending beyond RNA to the functional protein level. By integrating protein profiles with clinical traits such as blood pressure, lipid levels, and kidney function, researchers hope to enable more targeted therapy selection.

“These findings have not yet been validated for clinical use,” said study investigator Katalin Susztak, MD, PhD, director of the Penn/CHOP Kidney Innovation Center, Philadelphia. Susztak stressed that validation in clinical trials and proof of actionability are required before the results can be adopted in clinical practice.

Susztak and colleagues analyzed 337 human kidney samples using whole-genome sequencing, RNA sequencing, and proteomics. They demonstrated that tissue proteomics reveal critical insights into cardio-kidney metabolic (CKM) disease not captured by tissue gene expression or blood proteomics.

Focusing on proteins linked to CKM traits, the team identified multiple targetable mechanisms, including the role of kidney angiopoietin-like 3 in lipid levels and kidney function, and charged multivesicular body protein 1A in kidney function and hypertension. They also identified several proteins not previously prioritized for CKM diseases. These proteins may help explain why some patients with kidney diseases respond to therapy and others do not.

Raising the Bar in Kidney Disease Research

Asked to comment, Matthew B. Lanktree, MD, PhD, director of the McMaster Kidney Genetics Clinic & Nephrology Genetics Fellowship Program, St. Joseph’s Healthcare, McMaster University, Hamilton, Ontario, Canada, said these two papers “raise the bar for genomic studies into kidney disease.”

By using high-throughput single-cell and multiomic techniques, the researchers analyzed both the rate at which genes signal protein production and the actual amount of protein within each kidney cell type. This allowed them to “read the tea leaves to identify patterns among the mass of information,” said Lanktree, who was not involved in the studies. “Undoubtedly, this research has provided new tools and data, furthering our understanding of kidney health.”

Lanktree emphasized that CKD often results from multiple factors rather than a single cause that can be precisely targeted.

“Even in genetic forms of kidney disease, caused by one genetic change, lifestyle choices can have enormous impact on disease severity. Treatment needs to focus on all facets of cardio-metabolic-kidney health and risk factors, not just that one genetic change,” he told Medscape Medical News.

“These two papers give clues toward new therapies to add to our growing treatment armamentarium for kidney disease, some of which may be more applicable to specific groups of patients,” he added.

However, Lanktree cautioned that larger studies across more diverse populations will be required before these technologies can be applied clinically.

“I wouldn’t expect to see changes in the clinic tomorrow,” he said. “But this work does represent huge progress in higher-resolution genetic and protein studies of the kidney.”

The studies were supported by the National Institutes of Health (2R01DK076077-15, 5R01DK087635-15, 5P50DK114786-07, 5R01DK105821-08, 5R01DK132630-02, R01 DK105821, R01 DK087635, R01 DK076077, R01 DK12345, and 1R56AG081351), the National Science Foundation DMS/NIGMS (2245575), and the Translation Genetics in Renal Medicine grant from the University of Pennsylvania. Zhang, Susztak, and Lanktree reported having no relevant financial relationships.

John Schieszer, MA, is an award-winning national journalist and podcast broadcaster of The Medical Minute. He can be reached at medicalminutes@gmail.com