Single-cell-based image analysis of high-throughput cell array screens for quantification of viral infection.

The identification of eukaryotic genes involved in virus entry and replication is important for understanding viral infection. Our goal is to develop a siRNA-based screening system using cell arrays and high-throughput (HT) fluorescence microscopy. A central issue is efficient, robust, and automated single-cell-based analysis of massive image datasets. We have developed an image analysis approach that comprises (i) a novel, gradient-based thresholding scheme for cell nuclei segmentation which does not require subsequent postprocessing steps for separation of clustered nuclei, (ii) quantification of the virus signal in the neighborhood of cell nuclei, (iii) localization of regions with transfected cells by combining model-based circle fitting and grid fitting, (iv) cell classification as infected or noninfected, and (v) image quality control (e.g., identification of out-of-focus images). We compared the results of our nucleus segmentation approach with a previously developed scheme of adaptive thresholding with subsequent separation of nuclear clusters. Our approach, which does not require a postprocessing step for the separation of nuclear clusters, correctly segmented 97.1% of the nuclei, whereas the previous scheme achieved 95.8%. Using our algorithm for the detection of out-of-focus images, we obtained a high discrimination power of 99.4%. Our overall approach has been applied to more than 55,000 images of cells infected by either hepatitis C or dengue virus. Reduced infection rates were correctly detected in positive siRNA controls, as well as for siRNAs targeting, for example, cellular genes involved in viral infection. Our image analysis approach allows for the automatic and accurate determination of changes in viral infection based on high-throughput single-cell-based siRNA cell array imaging experiments.

Adaptation of the hepatitis C virus to cell culture.

A major breakthrough in the field of HCV research was the development of a system that supports the production of infectious virus particles. The key to this achievement was the molecular cloning of a genotype 2a genome, designated JFH1, which replicates to exceptionally high levels and at the same time supports virus particle assembly and release. A major drawback of this system was, however, the rather low yield of infectious particles obtained with the JFH1 genome as well as with most JFH1-derived virus chimeras. One approach to overcoming this hurdle is adaptation of the HCV genomes to cell culture. We found that both JFH1 and all chimeras, except one, can easily be adapted to cultured cells, increasing virus yields by up to three orders of magnitude. Surprisingly, adaptation is achieved by a multitude of mutations residing in both the structural and the nonstructural proteins. We therefore argue that a complex interaction between structural proteins and the HCV replicase takes place to allow efficient virus particle production.