Geneshifters has developed biologic and synthetic binding agents that selectively target free DNA and RNA in samples without disrupting other contents. The technology enables contamination removal in under 30 minutes using simple, shelf-stable reagents, supporting cleaner results in molecular biology workflows.
Nucleic acid contamination is a persistent challenge in molecular biology labs, where stray DNA and RNA from dead cells or environmental sources can compromise sensitive assays and downstream analyses. Geneshifters is developing a rapid, targeted solution that eliminates free nucleic acid contamination in under 30 minutes using simple reagents with good shelf life and reasonable cost.
The approach centers on custom-designed biologic and synthetic binding agents that specifically recognize and bind free DNA and RNA within a sample without interfering with other sample contents. Once bound, the resulting complexes can be removed using standard laboratory techniques already present in most facilities. The technology also explores differences between live and dead cells as a basis for selective targeting, enabling more precise contamination control.
Key features:
How it works:
The binding agents leverage well-understood molecular recognition principles, similar to how certain chemotherapeutic compounds recognize specific DNA binding sites. By building on established concepts in synthetic and biologic molecular binding, the agents selectively capture free DNA and RNA, including nucleic acids released from dead cells. The captured complexes are then separated from the sample using standard removal methods.
The technology is at an early-to-mid stage of development. Both synthetic and biologic binding agents have been demonstrated to specifically bind nucleic acids, but protocols still require optimization for target matrices, cell types, efficiency, and ease of use. Four planned experiments will evaluate binding efficiency, binding and removal performance across matrices, comparative performance between agent types, and accurate live-versus-dead cell discrimination. The team is positioned to advance toward broader validation and application-specific optimization.