Studying the pancreas at the single-cell level is essential for understanding diseases like pancreatitis and pancreatic cancer. However, isolating healthy pancreatic cells has been difficult because enzymes in the tissue can quickly damage cells and degrade RNA, making it challenging to generate reliable data.

Researchers at the Technical University of Munich have developed a simple and efficient method to isolate high-quality pancreatic cells. Their approach works across healthy tissue, inflamed tissue, and tumor models, making it broadly useful for research.

A key focus of the work was acinar cells, which make up the majority of the pancreas and are responsible for producing digestive enzymes. These cells are often underrepresented in single-cell RNA sequencing datasets because they are particularly sensitive to damage during tissue processing. The new method improves recovery of these cells, allowing for a more accurate picture of pancreatic biology.

Using this improved workflow, the team found that acinar cells in healthy tissue show relatively limited diversity, although some subgroups express specific enzymes or regulatory genes. After injury, such as in acute pancreatitis, these cells begin to show more varied gene expression patterns, including markers associated with other cell types. This suggests that acinar cells may adapt or transition in response to stress or damage.

The method also supports additional analyses, such as immune cell profiling in pancreatic cancer models, providing a more complete view of how the pancreas responds to disease. By improving cell isolation, the researchers have made it easier to study both normal function and disease processes at high resolution.

Overall, this work highlights how technical improvements in RNA sequencing workflows can unlock new biological insights and help researchers better understand complex tissues like the pancreas.