Automated interpretation of subcellular patterns from immunofluorescence microscopy

Abstract

Immunofluorescence microscopy is widely used to analyze the subcellular locations of proteins, but current approaches rely on visual interpretation of the resulting patterns. To facilitate more rapid, objective, and sensitive analysis, computer programs have been developed that can identify and compare protein subcellular locations from fluorescence microscope images. The basis of these programs is a set of features that numerically describe the characteristics of protein images. Supervised machine learning methods can be used to learn from the features of training images and make predictions of protein location for images not used for training. Using image databases covering all major organelles in HeLa cells, these programs can achieve over 92% accuracy for two-dimensional (2D) images and over 95% for three-dimensional images. Importantly, the programs can discriminate proteins that could not be distinguished by visual examination. In addition, the features can also be used to rigorously compare two sets of images (e.g., images of a protein in the presence and absence of a drug) and to automatically select the most typical image from a set. The programs described provide an important set of tools for those using fluorescence microscopy to study protein location.