January 13th, 2016
Conventional flow cytometry is a powerful technique for measuring cell phenotype and function, but it relies on fluorescent stains, or labels, to identify particular cell subpopulations. At times, those labels can be incompatible with live cells, or unavailable to researchers. Now, researchers from the Broad Institute’s Imaging Platform, Swansea University’s College of Engineering, and fellow international collaborators have found a new way to detect these cellular subpopulations, by applying machine learning to the hidden information in images of unlabeled cells generated from image flow cytometry. Their method is described in Nature Communications and a Swansea University press release, and their open-source workflow is available online.