AUTOMATED QUANTIFICATION OF CELLULAR PROCESSES WITH APPLICATIONS TO HIGH-CONTENT SCREENING

Recent advances in fluorescence microscopy provide us with an unprecedented powerful way to observe many biological objects (pathogens, vesicles, single molecules, etc.) and events (intracellular trafficking, adhesion, migration, pathogens entry) in the living cell under multiple experimental conditions. To understand the underlying biological phenomena, modern microscopy imaging confront researchers to the challenges of extracting and analyzing biological-relevant information from huge amounts of data. In this chapter, we focus on recent advances on automated image analysis frameworks to quantify specific cellular processes. Our aim is to demonstrate that systematic application of such frame-works on thousands of images produce statistically relevant measures of several interesting biological phenomena at the cell population level. Methodologically, we shall emphasize on a recently published robust cell segmentation approach using coupled shape-constrained active contours, and a statistical spot extraction method based on a multiscale variance stabilizing transform. We finally present an example of such approaches to the quantification of cellular endocytosis that demonstrates its usefulness to screening applications in biology.