Cellular phenotype profiling
cellXpress is a cellular phenotype profiling software platform developed by the Loo Lab at the Bioinformatics Institute, A*STAR, Singapore. The platform is designed for fast and high-throughput analysis of cellular phenotypes based on microscopy images. It is especially useful for large-scale profiling of cellular responses to pharmacological compounds, gene knockdowns, and/or toxic substances.
18 Oct 2016 The cellXpress platform was used to build the first high-throughput imaging-based nephrotoxicity model published in Archives of Toxicology.
5 Oct 2015 cellXpress pro 1.3 is available now. New improvements include optimizations for 64-bit CPUs with AVX/AVX2 support.
27 July 2015 The cellXpress platform was used to analyze renal cells in a paper published in Scientific Reports.
Single-cell phenotype quantification Automatically identify individual cells from fluorescence microscopy images; and quantify cellular morphology, protein sub-cellular localization and other features.
Plate-based data organization Design to handle high volume of image data acquired from 96/384-well plates. Analyses of different fluorescent-marker configurations can be performed on the same plate data.
Quick plate analysis Perform cell count and fluorescence intensity measurements over the whole plates, and visualize well-to-well trend and variability.
Phenotypic profiling Transform raw image features into discriminative profiles that can be used to characaterize changes in cellular phenotypes.
Fast processing speed Written in C++ and optimized for modern 64-bit and multi-core CPUs. Support parallel processing and dynamic job scheduling for high-throughput applications.
User-friendly interface Intuitive graphical user interface for interactive configuration of computational algorithms and visualization of segmentation and feature extraction results.
Split-screen viewer View and compare different cell images, segmentation results and feature values side-by-side in the same windows.
Cross-platform data sharing Access the same projects under different operating systems. Data files can be imported into the R software environment for further processing or visualization.