Researchers at the University of Notre Dame have developed an innovative automated device that can diagnose glioblastoma, an aggressive and currently incurable brain cancer, in less than an hour. This development offers hope for faster, more efficient diagnostics for a disease where the average patient survival time is just 12–18 months after diagnosis.
The core technology behind this breakthrough is a biochip that utilizes electrokinetic technology to detect biomarkers, specifically active Epidermal Growth Factor Receptors (EGFRs). These biomarkers are overexpressed in cancers like glioblastoma and can be found in extracellular vesicles, tiny particles secreted by cells.
“Extracellular vesicles, or exosomes, are unique nanoparticles secreted by cells. They are significantly larger than molecules—about 10 to 50 times bigger—and they carry a weak charge. Our technology was specifically designed to detect these nanoparticles, leveraging their unique properties,” explained Hsueh-Chia Chang, Bayer Professor of Chemical and Biomolecular Engineering at Notre Dame and lead author of the study published in Communications Biology.
Innovative Detection Method
The researchers faced a two-fold challenge: creating a process that could differentiate between active and inactive EGFRs, and developing a diagnostic tool that is both sensitive and selective enough to detect active EGFRs on extracellular vesicles from blood samples.
To tackle this, they engineered a biochip equipped with an inexpensive, electrokinetic sensor approximately the size of a ball in a ballpoint pen. Given the size of the extracellular vesicles, antibodies on the sensor can form multiple bonds with a single vesicle, significantly enhancing the sensitivity and selectivity of the diagnostic.
Synthetic silica nanoparticles then “report” the presence of active EGFRs on the captured vesicles while bringing a high negative charge. When these vesicles are detected, a noticeable voltage shift occurs, indicating the presence of glioblastoma in the patient. This charge-sensing approach minimizes interference that is common with current technologies that rely on electrochemical reactions or fluorescence.
“Our electrokinetic sensor allows us to perform tasks that other diagnostics cannot,” said Satyajyoti Senapati, a research associate professor of chemical and biomolecular engineering at Notre Dame and co-author of the study. “We can directly use blood samples without needing any pretreatment to isolate extracellular vesicles because our sensor isn’t affected by other particles or molecules. This gives our technology lower noise and higher sensitivity for disease detection than existing methods.”
Cost-Effective and Versatile
The device is composed of three main parts: an automation interface, a prototype of a portable machine to administer materials for the test, and the biochip itself. While each test requires a new biochip, the automation interface and machine are reusable. Running a test takes under an hour and requires only 100 microliters of blood. The cost of materials for each biochip is less than $2, making the technology both fast and affordable.
Though initially developed for glioblastoma, the researchers believe this diagnostic device can be adapted to detect other types of biological nanoparticles, paving the way for its use in identifying a variety of biomarkers associated with different diseases. Chang noted that the team is exploring the potential of this technology for diagnosing pancreatic cancer and other conditions, such as cardiovascular disease, dementia, and epilepsy.
“Our technique isn’t limited to glioblastoma. We chose to start with this cancer due to its deadly nature and the lack of early screening tests available,” Chang added. “Our goal is that with more feasible early detection, survival rates could improve.”
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Future Research
This groundbreaking development represents a significant step forward in the fight against glioblastoma and potentially other serious diseases, offering a faster, cheaper, and more effective diagnostic tool that could transform patient outcomes globally.
