Automatic multiparameter fluorescence imaging for determining lymphocyte phenotype and activation status in melanoma tissue sections.

Abstract

A system has been developed that combines multiparameter fluorescence imaging and computer vision techniques to provide automatic phenotyping of multiple cell types in a single tissue section. This system identifies both the nuclear and cytoplasmic boundary of each cell. A routine based on the watershed algorithm has been developed to segment an image of Hoechst-stained nuclei with an accuracy of greater than 85%. Deformable splines initially positioned at the nuclear boundaries are applied to images of fluorescently labelled cell-surface antigens. The splines lock onto the peak fluorescence signal surrounding the cell, providing an estimate of the cell boundary. From measurements acquired at this boundary, each cell is classified according to antigen expression. The system has been piloted in biopsies from melanoma patients participating in a clinical trial of the antibody R24. Thin tissue sections have been stained with Hoechst and three different fluorescent antibodies to antigens that permit the typing and evaluation of activity of T-cells. Changes in the infiltrates evaluated by multiparameter imaging were consistent with results obtained by immunoperoxidase analysis. The multiparameter fluorescent technique enables simultaneous determination of multiple cell subsets and can provide the spatial relationships of each cell type within the tissue.