Detecting disease in a blood sample. Monitoring contaminants in drinking water. Identifying biological threats before they can spread. DNA biosensors play a critical role in each of these, but many rely on a slow process that can miss fleeting signals or delay results.
At 麻豆原创, researchers are developing a new approach inspired by squids, octopuses and other cephalopods, one that doesn鈥檛 wait for targets to arrive, but actively reaches out to capture them. Led by , a professor in 麻豆原创鈥檚 , the work introduces a DNA-based system designed to capture target molecules more efficiently by extending into the surrounding solution.
鈥淥ne of the biggest challenges in biosensing is something surprisingly simple: molecules take time to move,鈥� Kolpashchikov says. 鈥淚magine trying to catch fish in a huge lake with a tiny net, most fish will never come close enough to be caught. Traditional sensors work the same way: they passively wait for target molecules (analytes) to randomly bump into them.鈥�
The project, supported by a $272,000 award from the U.S. National Science Foundation, reframes how biosensors operate, shifting from passive detection toward active engagement.
Targeting Molecules Through DNA
Conventional biosensors rely on diffusion, meaning target molecules must randomly move through a solution before encountering a sensing surface. This process, known as mass transport limitation, can slow detection and limit performance in time-sensitive applications.
Kolpashchikov鈥檚 approach addresses this constraint by incorporating nanostructures composed of DNA strands that extend outward from the sensor. These flexible extensions function like molecular tentacles, weakly interacting with passing targets and increasing the likelihood that they will be captured.
Rather than waiting for signals to arrive, the system draws them closer.
Speeding Detection
The speed at which a sensor can detect its target is often as important as detection sensitivity and specificity. In contexts such as medical diagnostics, environmental monitoring and food safety, delays can reduce reliability or limit usefulness altogether.
By increasing the rate at which target molecules are gathered and concentrated near the sensing surface, the DNA cephalopod approach may enable faster, more responsive detection systems, particularly in applications that depend on real-time or near-real-time analysis.
鈥淪low sensors can miss short-lived biological signals, allow samples to degrade, and delay responses to threats,鈥� Kolpashchikov says, 鈥淔aster detection reduces costs (less time, fewer reagents), improves accuracy, and enables real-time monitoring 鈥� something essential for healthcare, environmental safety, and biosecurity.鈥�
DNA as Structure and Sensor
The system uses DNA not only as a recognition element but also as a structural material. Engineered strands extend from the sensor into the surrounding environment, forming a dynamic interface that interacts with nearby molecules.
These extensions do not bind targets permanently at first. Instead, they weakly capture and release them, effectively increasing the local concentration of target molecules near the sensor鈥檚 core detection region. This process improves detection efficiency without requiring additional mechanical or chemical input.
By designing DNA nanostructures that actively interact with nearby molecules, the system creates a sensing environment that is more responsive and efficient.
鈥淒NA is uniquely suited for building nanoscale machines,鈥� Kolpashchikov says. 鈥淚t鈥檚 programmable, predictable and relatively inexpensive.鈥�
In this system, DNA strands self-assemble into a structure resembling a microscopic octopus, what the team calls聽 a 鈥溾�楧NA cephalopod.鈥�.鈥� A central sensor is surrounded by long, flexible 鈥溾�榯entacles鈥濃�� that extend into the solution. Each tentacle carries weak binding sites that briefly capture target molecules and pass them along from one site to the next, guiding them toward the center, where the sensor binds them more strongly and triggers detection.
Applications Across Fields
The improved speed and sensitivity of this approach expand the potential use of biosensors across multiple domains.
Possible applications include rapid detection of harmful bacteria in water and food systems, early-stage diagnosis through identification of DNA or RNA biomarkers, and forensic analysis requiring precise detection of biological material
By enabling sensors to detect smaller quantities of target molecules more quickly, the technology may support more timely and accurate decision-making in both clinical and field settings.
鈥淭he potential applications are broad: rapid disease diagnostics, including early cancer detection, and real-time monitoring of pathogens in water and food. Perhaps most exciting is that this is a general strategy. The same 鈥榯entacle鈥� concept could be applied for detection of proteins and small biological molecules.鈥� 鈥� Dmitry Kolpashchikov, professor of chemistry, 麻豆原创 College of Sciences
鈥淭his approach could dramatically improve how we detect biological molecules,鈥� Kolpashchikov says. 鈥淭he potential applications are broad: rapid disease diagnostics, including early cancer detection, real-time monitoring of pathogens in water and food. Perhaps most exciting is that this is a general strategy. The same 鈥榯entacle鈥� concept could be applied for detection of proteins and small biological molecules.鈥�
A New Method of Rapid Analyte Detection
As with many emerging technologies, translating laboratory advances into real-world systems presents challenges. Performance in complex environments, where multiple substances interact simultaneously, remains an area for further study.
Scaling the technology and integrating it into existing diagnostic platforms will also be critical steps in determining its broader applicability.
Rather than treating biosensing as a passive process governed by chance encounters, Kolpashchikov鈥檚 work suggests a different model, one in which sensors actively engage with their environment, reaching into the surrounding space to capture what drifts.
This material is based upon work supported by the U.S. National Science Foundation under Award No. 2555933. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the U.S. National Science Foundation.
