New Method Quantifies Cell-Level Damage in Kidney and Liver Disease

Researchers at the University of Cologne, University Hospital Cologne, and the Max Planck Institute for Metabolism Research have developed a method that, for the first time, allows the damage sustained by individual cells to be measured with precision. The approach relies on molecular markers—specifically gene expression—to enable a detailed analysis of disease progression within individual tissue samples, such as a biopsy. The study appears in Cell Genomics.

The method is built on a computer-assisted approach that identifies specific marker genes to gauge damage in kidney cells (podocytes) and liver cells (hepatocytes), two cell types of key significance in age-related diseases. “Our approach works with single-cell RNA sequencing data as well as spatial transcriptome data. This means the method can be applied universally—including to other cell types and organs," says Professor Dr Andreas Beyer, who led the study. 

The work grew out of close collaboration between scientists in basic research and clinical medicine. Beyer, an expert in computational biology, and Dr Martin Kann, a nephrologist, launched the project with the aim of gaining a better understanding of degenerative diseases that progress slowly. Additional teams, including liver and metabolism specialists, have since joined the consortium. 

The method now makes it possible to distinguish early disease mechanisms from later changes, or even to differentiate between patient-specific and general disease progression. “This is an important step towards tailoring treatments more precisely to individual needs,” explains Kann.

“Using our method, we can, as it were, sort cells according to the extent of the damage they have sustained—and then use a computer to analyze which biological processes occur in sequence. This makes it possible to identify critical early stages at which an intervention would be particularly effective,” adds Beyer. 

The method has a wide range of potential applications. The scientists are now working to refine it further, with the aim of better predicting how disease will progress in individual patients.