Development of algorithms to improve microflow analysis

Our primary mandate at API is to advance basic research and innovation to commercialization by providing access to world-class industry expertise, services, and infrastructure. Our activities focuses on engaging and supporting drug discovery and development initiatives, ensuring compliance with regulatory standards and driving innovation and commercialization through collaborative research and clinical studies. The investigation of extracellular vesicles (EV) as the next frontier for diagnostic evaluation is underway across the globe. There is a myriad of techniques available to interrogate extracellular vesicles, but the most versatile by far is EV flow cytometry (evFC). This technique can assess millions of potential cell fragments from microlitres of biological material such as plasma, urine or cerebrospinal fluid. From these investigations, we can begin to unravel potential new biomarkers when intact tumor cells may simply be too rare to monitor. Similarly, we can monitor patient response to medical treatments without requiring excessive amounts of material. In addition, we can detect and monitor viral and bacterial infections due to the sub-micron resolution of the technique.
However, with any technique that enhances resolution, detection of the appropriate signals becomes critical. Identifying the true positive signal from the “negative” background has traditionally been a subjective protocol with cell-based flow cytometry. This process, called “gating” involves selecting a subset of events from all events collected during a flow cytometry experiment for further analysis or data presentation. This includes general clean-up of the data, such as removing dead and dying cells or events consisting of multiple cells, as well as isolating your target cell population using their characteristic size, granularity, and expression of various cell markers. Proper gating, along with smart panel-building, can make your data easier to interpret and more publication-ready. The development of machine-learning based approaches for dynamic gating and instrument calibration (refractive index and size) will not only enhance the analysis of small particle evFC data, but also significantly increase the throughput capability of analyses of large datasets from months to weeks or even days.

Faculty Supervisor:

John Lewis

Student:

Partner:

Applied Pharmaceutical Innovation

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services; Retail trade

University:

University of Alberta

Program: