Over 30 million US adults and nearly nine million UK adults over the age of 45 suffer from osteoarthritis (1)(2). The molecule hyaluronan, also known as hyaluronic acid, plays an essential role in joint physiological functions, giving rise to its use as a biomarker for osteoarthritis. The downside, however, is that the molecule offers neither high sensitivity nor a high dynamic range; consequently, the results it provides are only semiquantitative. Now, researchers from Wake Forest School of Medicine, Cornell University, and the University of Oklahoma Health Sciences Center are looking to boost sensitivity for more quantitative measurements with a solid-state nanopore sensor (3). To find out more about the technique, we spoke with Adam Hall, lead researcher and Assistant Professor of Biomedical Engineering at the Wake Forest School of Biomedical Engineering and Sciences.
How did the investigation come about?
My lab has a strong interest in applying nanopore technology to biomarkers, but we had only ever focused on nucleic acids. This particular project started when my colleague Ellie Rahbar and I were looking into possible ways to collaborate. She had prior experience working with hyaluronan as a biomarker of trauma and, knowing the mechanism of our technology, she recognized that nanopores might be able to measure it. We gave it a try – at first, just to see if it would work. To our delight, the signals were very clear! With the help of Paul DeAngelis, our collaborator at the University of Oklahoma College of Medicine, we eventually determined that we could identify the size of the hyaluronan very accurately on a molecule-by-molecule basis. The final piece of the puzzle came when I was giving a seminar at Cornell University and happened to meet Heidi Reesink, a veterinary scientist who was using conventional technology to study hyaluronan in the knee joints of osteoarthritic horses. The fit was too perfect to ignore, so we initiated a collaboration that allowed us to apply our technology to an ideal in vivo system.How easy would it be to fit this testing into a pathologist’s workflow?
The technology itself is well-positioned for translation. We and others have developed advanced technology to increase affordability, make the results relatively easy to collect, and keep the measurement system compact. In fact, we believe the entire apparatus could be attached to, and powered by, a smartphone at some point. There is clear evidence that osteoarthritis strongly affects hyaluronan, but definitive linkages between the disease’s molecular characteristics and its grading and progression remain to be determined. This is mostly because of limitations in the technologies available for studying it; we believe our technology will fill that gap. A key advantage of our platform is its sensitivity: by using extremely small amounts of hyaluronan, we may be able to test blood or urine (instead of synovial fluid drawn directly from the knee joint) – letting us make analysis less invasive.How does your approach compare with current osteoarthritis analyses?
Our approach will rival the precision and resolution of existing techniques that tend to be much more expensive and time-consuming – as well as requiring significant expertise and infrastructure. As with any new technology, it will take time for people to accept – but as our system becomes more accessible, and as we continue to show how our results compare with – or even exceed – those of conventional techniques, we think its advantages will become clear.What’s next?
We are pushing hyaluronan analysis further with more physiological testing as well as expanding to other possible diseases in which it may be important. We are also extending to other related molecules to diversify the utility of the platform, including our continued development of nucleic acid biomarker analysis.Newsletters
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References
- Centers for Disease Control and Prevention, “Osteoarthritis (OA)”, (2018). Available at: https://bit.ly/2uRicCU. Accessed March 29, 2018. Arthritis Research UK, “State of musculoskeletal health 2017”, (2017). Available at: https://bit.ly/2IbZE3M. Accessed March 29, 2018. F Rivas et al., “Label-free analysis of physiological hyaluronan size distribution with a solid-state nanopore sensor”, Nat Commun, 9, 1037 (2018). PMID: 29531292.
