A team led by a West Virginia University biomedical engineer is working to ramp up and reimagine how medical professionals diagnose tick-borne infections such as Lyme disease.
Soumya Srivastava, assistant professor at the Benjamin M. Statler College of Engineering and Mineral Resources, is developing a tool that more quickly detects tick-borne diseases via a blood sample on a single chip. Srivastava's model aims to detect disease within one to two weeks after the onset of an infection, whereas existing approaches rely on a symptom-based questionnaire – which might ask if a person has a fever or a rash – and tests that aren't reliable until at least a few weeks after infection.
Srivastava's project was recently awarded $1.2 million as a joint initiative between the National Science Foundation and the National Institutes of Health.
Tick-borne pathogens can be passed to humans by the bite of infected ticks. Those ticks can carry bacteria, viruses or parasites. Srivastava's efforts could produce a much-needed tool in the fight against tick-borne illnesses, which have ballooned in recent years. Lyme disease cases now hover around 30,000 a year in the U.S., up from 22,000 in 2010, according to the Centers for Disease Control and Prevention.
Tick-borne disease can lead to serious morbidity and mortality, and it has increased significantly in the last 15-20 years in the U.S. This project will create a rapid, sensitive and label-free diagnostic tool to improve early detection and their co-infections in order to reduce complications and death from undiagnosed and late-diagnosed disease."
Soumya Srivastava, Assistant Professor, Benjamin M. Statler College of Engineering and Mineral Resources
Srivastava's research will involve cross-disciplinary use of microfluidics, sensors and machine-learning. Those factors will enable improved diagnosis of tick-borne infections via a non-invasive, affordable, quick and user-friendly tool.
After collecting a blood sample from a patient, the tool will analyze the cells. All cells have a set of dielectric properties like permittivity and conductivity that are unique for cell membrane and cell cytoplasm, Srivastava explained. Those properties are heavily dependent on the state of the cell, such as whether it is normal or abnormal.
The unique properties depend on the shape and size of the cell; if the membrane is rough, smooth or leaky; and what is happening within the cell interior.
"We basically are measuring these properties on our microfluidic chip," she said, "and the electrical signal coming from the sensor will help us determine if there is an infection or not. This technique is known as dielectrophoresis."
Once a few drops of blood enter the device, an electric field will sort them based on the state, size and shape of the cells. The sorted cells will have a baseline value of capacitance that will show up by the sensor and thus we can conclude the type of infection, Srivastava said.
"Machine-learning is applied to make this tool robust and sensitive to detect multiple infection within few minutes."
What makes the project more unique is its ability to detect multiple tick-borne infections at once, and in a timely fashion.
"Additionally, our platform will detect anaplasmosis, babesiosis, and Lyme disease at an early stage non-invasively compared to the other available techniques that test four to six weeks after the development of infection," Srivastava said. "Most tests available currently are symptom-based and symptoms develop four to six weeks after a tick bite. Our platform can detect these diseases early on, within one to two weeks, in under 30 minutes using a portable diagnostic tool. If successful, this tool may be useful for a variety of health applications beyond tick-borne diseases.
"Rapid detection could reduce the risk of hospitalization, doctor's visits and prevent the disease from progressing into a chronic, life-long condition."
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