Protein subcellular localization is a major determinant of protein function. However, this important protein feature is often described in terms of discrete and qualitative categories of subcellular compartments, and therefore it has limited applications in quantitative protein function analyses. Here, we present Protein Localization Analysis and Search Tools (PLAST), an automated analysis framework for constructing and comparing quantitative signatures of protein subcellular localization patterns based on microscopy images. PLAST produces human-interpretable protein localization maps that quantitatively describe the similarities in the localization patterns of proteins and major subcellular compartments, without requiring manual assignment or supervised learning of these compartments. Using the budding yeast Saccharomyces cerevisiae as a model system, we show that PLAST is more accurate than existing, qualitative protein localization annotations in identifying known co-localized proteins. Furthermore, we demonstrate that PLAST can reveal protein localization-function relationships that are not obvious from these annotations. First, we identified proteins that have similar localization patterns and participate in closely-related biological processes, but do not necessarily form stable complexes with each other or localize at the same organelles. Second, we found an association between spatial and functional divergences of proteins during evolution. Surprisingly, as proteins with common ancestors evolve, they tend to develop more diverged subcellular localization patterns, but still occupy similar numbers of compartments. This suggests that divergence of protein localization might be more frequently due to the development of more specific localization patterns over ancestral compartments than the occupation of new compartments. PLAST enables systematic and quantitative analyses of protein localization-function relationships, and will be useful to elucidate protein functions and how these functions were acquired in cells from different organisms or species. A public web interface of PLAST is available at http://plast.bii.a-star.edu.sg.

BackgroundHigh-throughput, image-based screens of cellular responses to genetic or chemical perturbations generate huge numbers of cell images. Automated analysis is required to quantify and compare the effects of these perturbations. However, few of the current freely-available bioimage analysis software tools are optimized for efficient handling of these images. Even fewer of them are designed to transform the phenotypic features measured from these images into discriminative profiles that can reveal biologically meaningful associations among the tested perturbations.ResultsWe present a fast and user-friendly software platform called "cellXpress" to segment cells, measure quantitative features of cellular phenotypes, construct discriminative profiles, and visualize the resulting cell masks and feature values. We have also developed a suite of library functions to load the extracted features for further customizable analysis and visualization under the R computing environment. We systematically compared the processing speed, cell segmentation accuracy, and phenotypic-profile clustering performance of cellXpress to other existing bioimage analysis software packages or algorithms. We found that cellXpress outperforms these existing tools on three different bioimage datasets. We estimate that cellXpress could finish processing a genome-wide gene knockdown image dataset in less than a day on a modern personal desktop computer.ConclusionsThe cellXpress platform is designed to make fast and efficient high-throughput phenotypic profiling more accessible to the wider biological research community. The cellXpress installation packages for 64-bit Windows and Linux, user manual, installation guide, and datasets used in this analysis can be downloaded freely from http://www.cellXpress.org.

The authors present a three-dimensional (3D) reconstruction algorithm and reconstruction-based deblurring method for light microscopy using a micro-rotation device. In contrast to conventional 3D optical imaging where the focal plane is shifted along the optical axis, micro-rotation imaging employs dielectric fields to rotate the object inside a fixed optical set-up. To address this entirely new 3D-imaging modality, the authors present a reconstruction algorithm based on Bayesian inversion theory and use the total variation function as a structure prior. The spectral properties of the reconstruction by simulations that illustrate the strengths and the weaknesses of the micro-rotation approach, compared with conventional 3D optical imaging, were studied. The reconstruction from real data sets shows that this method is promising for 3D reconstruction and offers itself as a deblurring method using a reconstruction-based procedure for removing out-of-focus light from the micro-rotation image series.