Development and validation of 'AutoRIF': software for the automated analysis of radiation-induced foci
1 Centre for Cell Chromosome Biology, Division of Biosciences, Brunel University, Uxbridge UB8 3PH, UK
2 Centre for Infection, Immunity and Disease Mechanisms, Division of Biosciences, Brunel University, Uxbridge UB8 3PH, UK
3 Centre for Information and Knowledge Management, School for Information Systems, Computing and Mathematics, Brunel University, Uxbridge UB8 3PH, UK
Genome Integrity 2012, 3:1 doi:10.1186/2041-9414-3-1Published: 26 January 2012
The quantification of radiation-induced foci (RIF) to investigate the induction and subsequent repair of DNA double strands breaks is now commonplace. Over the last decade systems specific for the automatic quantification of RIF have been developed for this purpose, however to ask more mechanistic questions on the spatio-temporal aspects of RIF, an automated RIF analysis platform that also quantifies RIF size/volume and relative three-dimensional (3D) distribution of RIF within individual nuclei, is required.
A java-based image analysis system has been developed (AutoRIF) that quantifies the number, size/volume and relative nuclear locations of RIF within 3D nuclear volumes. Our approach identifies nuclei using the dynamic Otsu threshold and RIF by enhanced Laplacian filtering and maximum entropy thresholding steps and, has an application 'batch optimisation' process to ensure reproducible quantification of RIF. AutoRIF was validated by comparing output against manual quantification of the same 2D and 3D image stacks with results showing excellent concordance over a whole range of sample time points (and therefore range of total RIF/nucleus) after low-LET radiation exposure.
This high-throughput automated RIF analysis system generates data with greater depth of information and reproducibility than that which can be achieved manually and may contribute toward the standardisation of RIF analysis. In particular, AutoRIF is a powerful tool for studying spatio-temporal relationships of RIF using a range of DNA damage response markers and can be run independently of other software, enabling most personal computers to perform image analysis. Future considerations for AutoRIF will likely include more complex algorithms that enable multiplex analysis for increasing combinations of cellular markers.