Nanomachines to the Rescue

Brock researchers aim to reduce wait period for diagnostics

By Zoe Chong, Colleen Jones and Nicholas Galipeau

Faster disease diagnosis is upon us according to researchers at Brock University. The team of five, led by assistant professor of chemistry Feng Li, are developing a streamlined approach to diagnostics that will allow for same day diagnoses, eliminating the suspenseful wait period by simplifying the lab testing process.

“Mainly the idea is trying to develop a very simple way to detect disease biomarkers,” Li said.

“All you need is to mix what we’ve developed with the human blood samples then you can directly read off whether there is a disease biomarker or not, then you can detect a certain disease,” Li explained.

“Tuberculosis is not a big issue in North America but it is a big issue in China.” – Feng Li

The nanobot emits a fluorescent signal when it detects the targeted biomarker. [Photo courtesy of Xiaolong Yang]

Biomarkers are indicators that can be found in a human blood sample. There are many different types of biomarkers, each representing different diseases or medical conditions. They are made up of a measurable number of molecules that can be found in that blood sample, and the presence of certain biomarkers in a blood sample indicates the patient has a certain disease, like cancer or tuberculosis for example.

The nanomachine is made up of short and long strands of DNA that are attached to the gold nanoparticle. The long DNA strands contain the biomarkers of the specific disease being tested for. The short DNA strands carry fluorescent signal reporters.

When the nanomachine is dropped into a human serum sample, and the biomarkers of the specific disease are detected, the long strands cut off the short DNA strands and activate the fluorescent signals.

In recent years nanotechnology research like Li’s has increased significantly. This is due to advancements in medical and computer technologies which allow researchers to engineer and manipulate individual cells according to Maria DeRosa.

“We are now able to rationally design and manipulate things at that small size.  And because we’re starting to understand their unique properties, we can exploit those properties to improve our products and technology,” said DeRosa, an associate professor of chemistry at Carleton University.

Currently, Li’s research groups is working on developing dependable diagnostic results using tuberculosis as their controlled test disease.

“So we mix this tuberculosis DNA with human serum samples from healthy donors so once we mix them it can represent, or mimic, a human serum sample from a tuberculosis patient. So it works well from this sample, but this is still a model sample, not a real diagnosis yet,” said Li.

“Currently we’re trying our best to keep improving the sensitivity and specificity and ability to actually detect biomarkers from complex human samples.” – Feng Li

“All we needed to do was to take the data samples from a patient and isolate the TB biomarkers – a DNA or RA – from the sample. The TB biomarker will then mix with our DNA nanorobot in a test tube and if there is a TB infection, our nanomachine will operate and the test tube will light up with a fluorescence. We can measure the fluorescence to get the signal,” said Xiaolong Yang, a member of Li’s team and the lead author of their research paper.

This system of signalling will make it easier for medical professionals to diagnose diseases and could drastically reduce wait times for test results to only 30 minutes.

Because tuberculosis no longer poses a serious threat to North Americans it may seem like a strange choice for Li’s initial testing samples. However Li says he specifically chose to develop diagnostics for tuberculosis because of its prominence in his homeland, China.

“Tuberculosis is not a big issue in North America but it is a big issue in China,” Li said.

This method could also potentially be useful in Canadian aboriginal communities, as tuberculosis is still a problem in many remote areas.

“For tuberculosis, a severe issue is drug resistance because those pathogens mutate very frequently so it’s very difficult to design a good therapy. So there is a big need to actually detect mutation, so that is why we were interested in this disease to begin with,” Li said.

While this research is still in its testing phase Li says that once finished this technology will change the way health professionals diagnose diseases.

They have also broadened their research to include prostate cancer which they will be focusing on in the future.

“Currently we’re trying our best to keep improving the sensitivity and specificity and ability to actually detect biomarkers from complex human samples,” Li said.

“This could become a very powerful tool in the next few years.”