Software tool for 3D extraction of germinal centers.

Background: Germinal Centers (GC) are short-lived micro-anatomical structures, within lymphoid organs, where affinity maturation is initiated. Theoretical modeling of the dynamics of the GC reaction including follicular CD4+ T helper and the recently described follicular regulatory CD4+ T cell populations, predicts that the intensity and life span of such reactions is driven by both types of T cells, yet controlled primarily by follicular regulatory CD4+ T cells. In order to calibrate GC models, it is necessary to properly analyze the kinetics of GC sizes. Presently, the estimation of spleen GC volumes relies upon confocal microscopy images from 20-30 slices spanning a depth of ~ 20 - 50 μm, whose GC areas are analyzed, slice-by-slice, for subsequent 3D reconstruction and quantification. The quantity of data to be analyzed from such images taken for kinetics experiments is usually prohibitively large to extract semi-manually with existing software. As a result, the entire procedure is highly time-consuming, and inaccurate, thereby motivating the need for a new software tool that can automatically identify and calculate the 3D spot volumes from GC multidimensional images.

Results: We have developed pyBioImage, an open source cross platform image analysis software application, written in python with C extensions that is specifically tailored to the needs of immunologic research involving 4D imaging of GCs. The software provides 1) support for importing many multi-image formats, 2) basic image processing and analysis, and 3) the ExtractGC module, that allows for automatic analysis and visualization of extracted GC volumes from multidimensional confocal microscopy images. We present concrete examples of different microscopy image data sets of GC that have been used in experimental and theoretical studies of mouse model GC dynamics.

Conclusions: The pyBioImage software framework seeks to be a general purpose image application for immunological research based on 4D imaging. The ExtractGC module uses a novel clustering algorithm for automatically extracting quantitative spatial information of a large number of GCs from a collection of confocal microscopy images. In addition, the software provides 3D visualization of the GCs reconstructed from the image stacks. The application is available for public use at http://sourceforge.net/projects/pybioimage/.