CellProfiler 2.0 is an improved version of the open-source, automated image analysis software designed for biological research. It offers enhanced robustness, user-friendliness, and functionality, with new algorithms and features to support high-throughput image analysis. The software is now available in Python, replacing the previous MATLAB version, and uses high-performance libraries such as NumPy and SciPy. It is free and open-source, available at http://www.cellprofiler.org under the GPL v. 2 license, and can be used on Mac OS X, Windows, and Linux. CellProfiler allows researchers to build modular, interoperable pipelines for image analysis, enabling the identification and measurement of biological objects and features. It supports a wide range of assays, including cell counting, staining intensity measurement, and complex phenotype scoring using machine learning. It has been used to analyze various cell types, including budding yeast, Drosophila, mouse, rat, and many human cell types. CellProfiler 2.0 has a significantly improved user interface, including drag-and-drop operations, context-sensitive menus, undo capabilities, and user-friendly error reporting. It also includes a test mode for optimizing image analysis settings. The software now supports a wide range of image formats and can load metadata. It has enhanced database capabilities and can upload data directly to MySQL or SQLite databases. CellProfiler 2.0 is designed to be extensible and interoperable, allowing developers to create and distribute new modules. It can run in batch mode, processing images on multiple computing cores or cluster nodes. It also supports integration with ImageJ plugins and can run ImageJ macros within its pipeline. Future developments will leverage the improved infrastructure of CellProfiler 2.0 to add new functionality, including workflow management software and machine learning-based classification of pixels or whole images. The software continues to be a valuable tool for automated biological image analysis, with a growing user base and widespread adoption in the scientific community.CellProfiler 2.0 is an improved version of the open-source, automated image analysis software designed for biological research. It offers enhanced robustness, user-friendliness, and functionality, with new algorithms and features to support high-throughput image analysis. The software is now available in Python, replacing the previous MATLAB version, and uses high-performance libraries such as NumPy and SciPy. It is free and open-source, available at http://www.cellprofiler.org under the GPL v. 2 license, and can be used on Mac OS X, Windows, and Linux. CellProfiler allows researchers to build modular, interoperable pipelines for image analysis, enabling the identification and measurement of biological objects and features. It supports a wide range of assays, including cell counting, staining intensity measurement, and complex phenotype scoring using machine learning. It has been used to analyze various cell types, including budding yeast, Drosophila, mouse, rat, and many human cell types. CellProfiler 2.0 has a significantly improved user interface, including drag-and-drop operations, context-sensitive menus, undo capabilities, and user-friendly error reporting. It also includes a test mode for optimizing image analysis settings. The software now supports a wide range of image formats and can load metadata. It has enhanced database capabilities and can upload data directly to MySQL or SQLite databases. CellProfiler 2.0 is designed to be extensible and interoperable, allowing developers to create and distribute new modules. It can run in batch mode, processing images on multiple computing cores or cluster nodes. It also supports integration with ImageJ plugins and can run ImageJ macros within its pipeline. Future developments will leverage the improved infrastructure of CellProfiler 2.0 to add new functionality, including workflow management software and machine learning-based classification of pixels or whole images. The software continues to be a valuable tool for automated biological image analysis, with a growing user base and widespread adoption in the scientific community.