New Transcriptomic Framework Identifies Early Cell Damage Before Organ Failure

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, enables damage to individual cells to be precisely quantified. The method uses molecular markers – specifically gene expression – and thus enables the detailed analysis of disease progression in individual tissue samples, such as a biopsy. The study "Cell-type-specific damage scores reveal kidney and liver disease trajectories in single-cell and spatial transcriptomics" has been published in the journal Cell Genomics.

The method is based on a computer-assisted approach that identifies specific marker genes to measure damage to kidney cells (podocytes) and liver cells (hepatocytes). Both cell types are 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."

Andreas Beyer, Professor, University of Cologne

The study is the result of close collaboration between scientists working 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 with a slow progression. Further teams, including liver and metabolism specialists, have since joined the consortium.

This 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. For example, the scientists are now refining the method in order to better predict disease progression in patients.