Understanding how cancer cells respond to treatment is one of the biggest challenges in biology. Even within the same tumor, cells can behave very differently, which makes it harder to fully eliminate the disease. Researchers at The Hebrew University have developed a new RNA sequencing approach called sc-rDSeq that helps uncover this hidden complexity at the single-cell level.

Traditional single-cell RNA sequencing methods mainly focus on one type of RNA, typically those with polyA tails. While useful, this leaves out many important RNA molecules, including non-coding RNAs and histone RNAs, which can play key roles in how cells grow, divide, and respond to stress. sc-rDSeq captures a much broader range of RNA types, giving a more complete picture of what is happening inside each cell.

One of the major advantages of sc-rDSeq is its sensitivity. It detects far more RNA molecules per cell than standard approaches, allowing researchers to see subtle differences between cells that might otherwise be missed. When applied to lung cancer cells, the method revealed multiple previously hidden subpopulations, each with distinct biological behaviors.

These differences became especially important when the cells were treated with EGFR inhibitors, a common type of cancer therapy. Instead of responding uniformly, the cells split into several groups with different survival strategies. Some showed signs of stopping their growth cycle, while others activated pathways related to movement, metabolism, or structural changes. These variations help explain why some cancer cells survive treatment and eventually lead to drug resistance.

Another key finding was the role of non-polyA RNAs in tracking these responses. Changes in histone RNA levels provided a clear signal of how cells were reacting to treatment. The method also uncovered differences in RNA splicing and genetic variations between resistant cell groups, offering additional clues about how resistance develops.

By combining all of this information, sc-rDSeq provides a more detailed and layered view of cancer biology. This could be especially valuable for personalized medicine, where understanding the exact behavior of a patient’s tumor may guide more effective treatment decisions.

Overall, this approach highlights how expanding RNA sequencing beyond traditional targets can reveal important biological signals that were previously hidden, improving our ability to study complex diseases like cancer.