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Automated classification and visualization of fluorescent live cell microscopy images
Author(s) -
URISH K. L.,
DEASY B. M.,
HUARD J.
Publication year - 2013
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.12010
Subject(s) - visualization , flow cytometry , computer science , software , microscopy , fluorescence microscope , cytometry , throughput , artificial intelligence , computer vision , pattern recognition (psychology) , biological system , computational biology , biology , fluorescence , microbiology and biotechnology , physics , optics , telecommunications , wireless , programming language
Summary 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.