Abstract
Viruses employ a variety of strategies to hijack cellular activities through the orchestrated recruitment of macromolecules to specific virus-induced cellular micro-environments. Adenoviruses (Ad) and other DNA viruses induce extensive reorganization of the cell nucleus and formation of nuclear Replication Compartments (RCs), where the viral genome is replicated and expressed. In this work an automatic algorithm designed for detection and segmentation of RCs using ellipses is presented. Unlike algorithms available in the literature, this approach is deterministic, automatic, and can adjust multiple RCs using ellipses. The proposed algorithm is non iterative, computationally efficient and is invariant to affine transformations. The method was validated over both synthetic images and more than 400 real images of Ad-infected cells at various timepoints of the viral replication cycle obtaining relevant information about the biogenesis of adenoviral RCs. As proof of concept the algorithm was then used to quantitatively compare RCs in cells infected with the adenovirus wild type or an adenovirus mutant that is null for expression of a viral protein that is known to affect activities associated with RCs that result in deficient viral progeny production.
Highlights
Dynamics of formation of replication centers or compartments (RCs) at different times of the viral replication cycle[3]
The fluorescent staining of DNA binding protein (DBP) was used as a bona fide marker to detect RCs, which were approximated through ellipses at various times of the viral replication cycle
Because the viral DBP associates with ssDNA and participates directly in viral DNA replication, many studies have used this protein as a bona fide marker of RCs
Summary
Dynamics of formation of RCs at different times of the viral replication cycle[3]. Two years after the initial proposal of Fitzgibbon’s algorithm, Halir and Flusser presented a numerically stable version, incorporating a block decomposition strategy[16] This is the most commonly used alternative for the implementation of the “Direct Least Square Fitting Ellipse” or DLSFE algorithm. The DLSFE can only adjust one ellipse to a data set (it is not possible to adjust multiple ellipses) and it is very sensitive to outliers For these reasons, the direct application of this algorithm in problems of image segmentation is not generally considered viable. In this work a very simple and efficient approach to detect RCs by adjusting ellipses is proposed This new algorithm is automatic, deterministic, non iterative, can simultaneously detect multiple viral RCs and has a linear computational complexity. Using a set of useful parameters: the number of RCs per cell nucleus; the area of RCs within nuclei; the intensity of the signal associated with RCs; and the ellipse eccentricity, as a measure that facilitates distinction between different RCs morphologies, the method allowed the automatic determination of a previously unrecognised effect of this mutation on the dynamics of formation of AdRC
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