Object-oriented image analysis for high content screening: detailed quantification of cells and sub cellular structures with the Cellenger software

Abstract

Background: Detailed image analysis still is a considerable bottleneck for many cellular assays, and automated solutions to the problem are desirable. However, dealing with the complexity and variability of structures in cellular images makes detailed and reliable analysis a nontrivial task.

Methods: Therefore, based on the object-oriented image analysis approach, a novel image analysis technology, a flexible and reliable system for image analysis in cellular assays was developed. It contains a library of predefined, adaptable modules, each of them developed for a specific analysis task. The system can be configured easily by combining appropriate modules and adapting them interactively to the specific image data, if necessary. By representing cells and sub cellular structures within a network of interlinked image objects, a large number of parameters can be derived that describe shape, intensity, and relevant structural and relational aspects of any chosen class of structures.

Results: Thus, multi-parameter analysis and multiplexing are supported. A sample application based on this approach demonstrates that GFP signals can be distinguished based on their properties and the relative location within the cell.