An image score inference system for RNAi genome-wide screening based on fuzzy mixture regression modeling

With recent advances in fluorescence microscopy imaging techniques and methods of gene knock down by RNA interference (RNAi), genome-scale high-content screening (HCS) has emerged as a powerful approach to systematically identify all parts of complex biological processes. However, a critical barrier preventing fulfillment of the success is the lack of efficient and robust methods for automating RNAi image analysis and quantitative evaluation of the gene knock down effects on huge volume of HCS data. Facing such opportunities and challenges, we have started investigation of automatic methods towards the development of a fully automatic RNAi-HCS system. Particularly important are reliable approaches to cellular phenotype classification and image-based gene function estimation. We have developed a HCS analysis platform that consists of two main components: fluorescence image analysis and image scoring. For image analysis, we used a two-step enhanced watershed method to extract cellular boundaries from HCS images. Segmented cells were classified into several predefined phenotypes based on morphological and appearance features. Using statistical characteristics of the identified phenotypes as a quantitative description of the image, a score is generated that reflects gene function. Our scoring model integrates fuzzy gene class estimation and single regression models. The final functional score of an image was derived using the weighted combination of the inference from several support vector-based regression models. We validated our phenotype classification method and scoring system on our cellular phenotype and gene database with expert ground truth labeling. We built a database of high-content, 3-channel, fluorescence microscopy images of Drosophila Kc₁₆₇ cultured cells that were treated with RNAi to perturb gene function. The proposed informatics system for microscopy image analysis is tested on this database. Both of the two main components, automated phenotype classification and image scoring system, were evaluated. The robustness and efficiency of our system were validated in quantitatively predicting the biological relevance of genes.