title: Automated classification and visualization of fluorescent live cell microscopy images.
abstract: Robotic, high-throughput microscopy is a powerful tool for small molecule screening and classifying cell phenotype, proteomic and genomic data. An important hurdle in the field is the automated classification and visualization of results collected from a data set of tens of thousands of images. We present a method that approaches these problems from the perspective of flow cytometry with supporting open-source code. Image analysis software was created that allowed high-throughput microscopy data to be analysed in a similar manner as flow cytometry. Each cell on an image is considered an object and a series of gates similar to flow cytometry is used to classify and quantify the properties of cells including size and level of fluorescent intensity. This method is released with open-source software and code that demonstrates the method's implementation. Accuracy of the software was determined by measuring the levels of apoptosis in a primary murine myoblast cell line after exposure to staurosporine and comparing these results to flow cytometry.
authors: Department of Orthopaedic Surgery and Rehabilitation, Division of Musculoskeletal Sciences, College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania, USA.
authors: K L
authors: KL
authors: Urish
authors: B M
authors: BM
authors: Deasy
authors: J
authors: J
authors: Huard
mesh_terms: D000818:Animals; D017209:Apoptosis; D001331:Automation / Q000379:methods*; D002478:Cells, Cultured; D003584:Cytological Techniques / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D008856:Microscopy, Fluorescence / Q000379:methods*; D032446:Myoblasts / Q000187:drug effects; D012984:Software; D019311:Staurosporine / Q000633:toxicity
chemical_list: D019311:Staurosporine