A method for improved clustering and classification of microscopy images using quantitative co-localization coefficients

• Published in: BMC research notes 2012-06, Vol.5 (1), p.281-281, Article 281
• Main Authors: Singan, Vasanth R, Handzic, Kenan, Curran, Kathleen M, Simpson, Jeremy C
• Format: Article
• Language: English
• Subjects: Algorithms; Antibodies; Automation; Binding proteins; Biomedical research; Cell culture; Cellular proteins; Classification; Cluster Analysis; Clustering; Fluorescence microscopy; G proteins; HeLa Cells; Humans; Image analysis; Imaging, Three-Dimensional - classification; Imaging, Three-Dimensional - methods; Methods; Microscopy; Microscopy - methods; Physiological aspects; Principal components analysis; Protein binding; Protein Transport; Proteins; Quantitative co-localization; rab GTP-Binding Proteins - metabolism; Rab proteins; Science; Software; Studies; Subcellular Fractions - metabolism; Technical Note; Texture features; University colleges; Viral antibodies
• ISSN: 1756-0500; 1756-0500
• DOI: 10.1186/1756-0500-5-281

The localization of proteins to specific subcellular structures in eukaryotic cells provides important information with respect to their function. Fluorescence microscopy approaches to determine localization distribution have proved to be an essential tool in the characterization of unknown proteins, and are now particularly pertinent as a result of the wide availability of fluorescently-tagged constructs and antibodies. However, there are currently very few image analysis options able to effectively discriminate proteins with apparently similar distributions in cells, despite this information being important for protein characterization. We have developed a novel method for combining two existing image analysis approaches, which results in highly efficient and accurate discrimination of proteins with seemingly similar distributions. We have combined image texture-based analysis with quantitative co-localization coefficients, a method that has traditionally only been used to study the spatial overlap between two populations of molecules. Here we describe and present a novel application for quantitative co-localization, as applied to the study of Rab family small GTP binding proteins localizing to the endomembrane system of cultured cells. We show how quantitative co-localization can be used alongside texture feature analysis, resulting in improved clustering of microscopy images. The use of co-localization as an additional clustering parameter is non-biased and highly applicable to high-throughput image data sets.