KAIST

<Professor Sungmin Son, (From Upper Left) Professor Dan Fletcher, Professor Melaine Ott>

As the spread of infectious diseases accelerates, technologies that can accurately distinguish multiple viruses in a single test are becoming increasingly important. KAIST and an international research team have developed a new diagnostic technology that simultaneously identifies various viruses and variants by controlling the “speed” of gene scissors. This technology is expected to transform responses to emerging infectious diseases, as it can detect multiple infections at once while reducing the complexity of testing procedures.

KAIST (President Kwang Hyung Lee) announced on the 26th of April that a research team led by Professor Sungmin Son from the Department of Bio and Brain Engineering, in collaboration with researchers from the University of California, Berkeley (UC Berkeley) and the Gladstone Institutes, has developed a new ribonucleic acid (RNA) diagnostic technology that can distinguish multiple viruses and variants simultaneously by utilizing the reaction speed of gene scissors.

The tool used by the research team is a CRISPR-based protein called Cas13. Gene scissors are proteins that locate and cut specific genetic material, becoming activated when they recognize their target. Cas13 specifically targets RNA. When it finds its target, it becomes activated and cuts surrounding RNA, generating a fluorescent signal.

Existing technologies require the use of different gene scissors or various fluorescent colors to detect multiple viruses simultaneously, making the system complex and difficult to apply in real-world settings.

The research team took a different approach. They focused on the fact that when gene scissors bind to their target, the speed of “cutting” varies depending on the type of virus. By observing at the single-molecule level within tiny droplets, they confirmed that unique reaction speed patterns emerge depending on the combination of guide RNA and target RNA. Guide RNA is an RNA molecule that provides “positional information,” guiding the gene scissors to their target.

< Conceptual diagram of kinetic barcoding using the reaction rate of the CRISPR Cas13 enzyme. The dashed area on the right represents the guide RNA region modified to control the reaction rate. >

Based on this, the research team developed a “kinetic barcoding” technology that uses differences in reaction speed like a barcode. This method interprets reaction speeds as signal patterns to distinguish different viruses. Through this technology, it became possible to simultaneously identify multiple viruses and variants using only a single type of gene scissors.

< Multiplex virus detection using microdroplet-based kinetic barcoding >

In addition, by adjusting the design of guide RNA, the cutting speed of gene scissors can be tuned, enabling scalable and simultaneous detection of a wide range of viruses.

The testing process has also been greatly simplified. In conventional methods, detecting RNA viruses requires a “reverse transcription” process that converts RNA into DNA, but this technology enables direct detection of RNA as it is. Reverse transcription is a step that increases testing time and complicates procedures.

When tested on actual clinical samples, the technology successfully distinguished various respiratory viruses and SARS-CoV-2 variants in a single reaction.

Professor Sungmin Son stated, “This study goes beyond simply determining whether a virus is present, and is the first case to use the reaction speed of gene scissors as a new form of diagnostic information,” adding, “It will become a next-generation platform capable of diagnosing various infectious diseases at once in the field.”

This study was led by Professor Sungmin Son of KAIST as the first author and co-corresponding author, and was published on March 31, 2026, in the world-renowned journal in bioengineering, Nature Biomedical Engineering.
 ※ Paper title: “Programmable kinetic barcoding for multiplexed RNA detection with Cas13a,” DOI: 10.1038/s41551-026-01642-6

This research was supported by KAIST’s New Faculty Settlement Research Fund and by the U.S. National Institutes of Health (NIH/NIAID).