Analysis of the 3D spatial organization of cells and sub cellular structures in tissue

Advancements in image analysis have recently made it possible to segment the cells and nuclei, of a wide variety of tissues, from 3D images collected using fluorescence confocal microscopy [Ortiz de Solorzano et at. J. Microscopy 193:212-226, 1999]. This has made it possible to analyze the spatial organization of individual cells and nuclei within the natural tissue context. We present here a spatial statistical method which examines an arbitrary 3D distribution of cells of two different types and determines the probability that the cells are randomly mixed, cells of one type are clustered, or cells of different types are preferentially associated. Beginning with a segmented 3D image of cells (or nuclei), the Voronoi diagram is calculated to indicate the nearest neighbour relationships of the cells. Then, in a test image of the same topology, cells are randomly assigned a type in the same proportions as in the actual specimen and the ratio of cells with nearest neighbours of the same type versus the other types is calculated. Repetition of this random assignment is used to generate a distribution function which is specific for the tissue image. Comparison of the ratios for the actual sample to this distribution assigns probabilities for the conditions defined above. The technique is being used to analyze the organization of genetically normal versus abnormal cells in cancer tissue.