Algorithms for Applied Digital Image Cytometry

Abstract: Image analysis can provide genetic as well as protein level information from fluorescence stained fixed or living cells without loosing tissue morphology. Analysis of spatial, spectral, and temporal distribution of fluorescence can reveal important information on the single cell level. This is in contrast to most other methods for cell analysis, which do not account for inter-cellular variation. Flow cytometry enables single-cell analysis, but tissue morphology is lost in the process, and temporal events cannot be observed.The need for reproducibility, speed and accuracy calls for computerized methods for cell image analysis, i.e., digital image cytometry, which is the topic of this thesis.Algorithms for cell-based screening are presented and applied to evaluate the effect of insulin on translocation events in single cells. This type of algorithms could be the basis for high-throughput drug screening systems, and have been developed in close cooperation with biomedical industry.Image based studies of cell cycle proteins in cultured cells and tissue sections show that cyclin A has a well preserved expression pattern while the expression pattern of cyclin E is disturbed in tumors. The results indicate that analysis of cyclin E expression provides additional valuable information for cancer prognosis, not visible by standard tumor grading techniques.Complex chains of events and interactions can be visualized by simultaneous staining of different proteins involved in a process. A combination of image analysis and staining procedures that allow sequential staining and visualization of large numbers of different antigens in single cells is presented. Preliminary results show that at least six different antigens can be stained in the same set of cells.All image cytometry requires robust segmentation techniques. Clustered objects, background variation, as well as internal intensity variations complicate the segmentation of cells in tissue. Algorithms for segmentation of 2D and 3D images of cell nuclei in tissue by combining intensity, shape, and gradient information are presented.The algorithms and applications presented show that fast, robust, and automatic digital image cytometry can increase the throughput and power of image based single cell analysis.