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Michael Parsons
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Michael Parsons

Michael Parsons
by Sophie Lily Polan


michael parsonsMichael is the manager of flow cytometry core facilities at LTRI which are located at 25 Orde Street (rm. L4-420) and 600 University Avenue (rm. 980 ). The latter location has a focus on imaging cytometry whereas the Orde Street lab is geared around its cell sorting capabilities. Michael has had a real fascination with flow cytometry since 1990 joining Dr. Jim Woodgett’s laboratory in 1995 his work has focused on getting the most out of this technology. Recognizing the need for a shared resource flow cytometry facility he moved over to establish and manage the LTRI Flow cytometry facility in 2010. What helps differentiate the LTRI flow cytometry facilities from others in Toronto is the amount of time spent working as scientific collaborators from experimental design on through analysis as opposed to acting solely as instrument providers.


Conventional flow cytometry allows the study of cells or particles in suspension as they flow through a “flow cell” while interrogated by various lasers of distinct wavelengths. Through the use of fluorescent dyes with specific properties or antibodies bound to a variety of fluorochromes targeted to either surface or intracellular proteins much information as to phenotype and cellular processes can be ascertained. Furthermore, since data is generated from large numbers of cells or particles robust sub-population hierarchy along with population statistics can be attained.


Flow cytometry is frequently employed in clinical analysis to help aid disease diagnosis and is particularly useful for evaluation of blood samples and hematological diseases such as leukemia’s. Research flow cytometry tends towards higher complexity and capability. For this reason the team which includes Annie Bang acts as a repository of flow cytometry knowledge while training post-doctoral fellows and students on how to use the equipment and analyze data. One of the challenges is the demand for scientific collaborations are rising along with the number of users of the facility which is placing time greater demands on staff.


Michael sees machine learning (a form of artificial intelligence) as having great potential for flow analysis, particularly imaging flow as such algorithms are highly effective in pattern recognition within large data sets that often have high dimensionality. He and his collaborators continue to work on a form of machine learning known as “deep learning” in which algorithms are able to find structure within the data without first being exposed to the training sets used in traditional machine learning. This is an exciting avenue of research since it has the potential to uncover relationships previously unseen while increasing power and robustness during data analysis. 



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