Automated determination of protein subcellular locations from 3D fluorescence microscope images

Knowing the subcellular location of a protein is critical to a full understanding of its function, and automated, objective methods for assigning locations are needed as part of the characterization process for the thousands of proteins expressed in each cell type. Fluorescence microscopy is the most common method used for determining subcellular location, and we have previously described automated systems that can recognize all major subcellular structures in 2D fluorescence microscope images. Here we show that 2D pattern recognition accuracy is dependent on the choice of the vertical position of the 2D slice through the cell and that classification of protein location patterns in 3D images results in higher accuracy than in 2D. In particular, automated analysis of 3D images provides excellent distinction between two Golgi proteins whose patterns are indistinguishable by visual examination.