Informatics and quantitative analysis in biological imaging

Abstract

Biological imaging is now a quantitative technique for probing cellular structure and dynamics and is increasingly used for cell-based screens. However, the bioinformatics tools required for hypothesis-driven analysis of digital images are still immature. We are developing the Open Microscopy Environment (OME) as an informatics solution for the storage and analysis of optical microscope image data. OME aims to automate image analysis, modeling, and mining of large sets of images and specifies a flexible data model, a relational database, and an XML-encoded file standard that is usable by potentially any software tool. With this design, OME provides a first step toward biological image informatics.

Figure 1. Applications for Quantitative Imaging

The image shows an XlK2 cell during the process of cytokinesis stained for DNA (blue), microtubules (green) and the aurora-B protein kinase (red) (13). While the image demonstrates the relative localization of different cellular components and structures, quantitative analysis reveals specific characteristics that can be used to assay effects of inhibitors or expressed proteins. For example, integrating the signal from a DNA-specific fluorophore (top right) reveals defects in segregation of the genome in mitosis. Measuring the overlap of microtubules and aurora-B (e.g., using a cross correlation analysis (14)) within a sub-region of a dividing cell (dotted box) might be used to assess effectors of cytokinesis. Scale, 5 μm.

Figure 2. The Database is the Interface: OME Architecture

OME is constructed as a standard “three-tier” application with a relational database that stores information in a table-based structure (blue), an application server that processes data and a client that lives on the users desktop and communicates via the internet (that is, via IP). Multiple clients can communicate with OME including Web browsers, commercial microscopy software and data mining applications. The OME data model is instantiated via a relational database (“OME database;” blue) in which metadata is stored in tables as specified by the schema and binary image data is stored in a trusted file system (the “image repository;” red). When data is transported between databases, or stored in a flat file, metadata and image data in the database are translated into XML (“OME XML File;” green). The OME database communicates with analysis modules via a subsystem (“analysis subsystem”) that ensures the consistent treatment of semantic datatypes. The analysis modules also calculate and store the history of the analysis chain (see online supplemental material). When analysis modules are chained together, each communicates independently with the database (“actual data path;” yellow block) even though the conceptual path appears is from one module to the next.(“conceptual data path”). Existing commercial or independent software tools can read OME data without substantial modification. OME itself is open source and available through a LGPL license but applications that talk to it can be either open or proprietary.