Dataset Information

Multi-image colocalization and its statistical significance.

ABSTRACT: Accurately localizing molecules within the cell is one of main tasks of modern biology, and colocalization analysis is one of its principal and most often used tools. Despite this popularity, interpretation is often uncertain because colocalization between two or more images is rarely analyzed to determine whether the observed values could have occurred by chance. To address this, we have developed a robust methodology, based on Monte Carlo randomization, to measure the statistical significance of a colocalization. The method works with voxel-based, intensity-based, object-based, and nearest-neighbor metrics. We extend all of these to measure colocalization in images with three colors. We also introduce three new metrics; blob colocalization, where the blob consists of a local maximum surrounded by a three-dimensional group of voxels; cluster diameter, to measure the clustering of fluorophores in three or more images; and the intercluster distance to measure the distance between these clusters. The robustness of these metrics was tested by varying the image thresholds over a broad range, which produced no change in the statistical significance of the colocalizations. A comparison of blob colocalization with voxel and Manders colocalization metrics shows that the different measures produce consistent results with similar values for significance and nonsignificance. Using our methodology, we are able to determine not only whether the labeled molecules colocalize with a probability greater than chance, but also whether they are sequestrated into different compartments. The program, written in C++, is freely available as source, as well as in a Linux version.

SUBMITTER: Fletcher PA

PROVIDER: S-EPMC2941013 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

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Multi-image colocalization and its statistical significance.

Fletcher Patrick A PA Scriven David R L DR Schulson Meredith N MN Moore Edwin D W ED

Biophysical journal 20100901 6

Accurately localizing molecules within the cell is one of main tasks of modern biology, and colocalization analysis is one of its principal and most often used tools. Despite this popularity, interpretation is often uncertain because colocalization between two or more images is rarely analyzed to determine whether the observed values could have occurred by chance. To address this, we have developed a robust methodology, based on Monte Carlo randomization, to measure the statistical significance ...[more]

PMID: 20858446

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Project description:Motivation:Colocalization of structures in biomedical images can lead to insights into biological behaviors. One class of colocalization problems is examining an annular structure (disk-shaped such as a cell, vesicle or molecule) interacting with a network structure (vascular, neuronal, cytoskeletal, organellar). Examining colocalization events across conditions is often complicated by changes in density of both structure types, confounding traditional statistical approaches since colocalization cannot be normalized to the density of both structure types simultaneously. We have developed a technique to measure colocalization independent of structure density and applied it to characterizing intercellular colocation with blood vessel networks. This technique could be used to analyze colocalization of any annular structure with an arbitrarily shaped network structure. Results:We present the circular colocalization affinity with network structures test (CIRCOAST), a novel statistical hypothesis test to probe for enriched network colocalization in 2D z-projected multichannel images by using agent-based Monte Carlo modeling and image processing to generate the pseudo-null distribution of random cell placement unique to each image. This hypothesis test was validated by confirming that adipose-derived stem cells (ASCs) exhibit enriched colocalization with endothelial cells forming arborized networks in culture and then applied to show that locally delivered ASCs have enriched colocalization with murine retinal microvasculature in a model of diabetic retinopathy. We demonstrate that the CIRCOAST test provides superior power and type I error rates in characterizing intercellular colocalization compared to generic approaches that are confounded by changes in cell or vessel density. Availability and implementation:CIRCOAST source code available at: https://github.com/uva-peirce-cottler-lab/ARCAS. Supplementary information:Supplementary data are available at Bioinformatics online.

Dataset Information

Multi-image colocalization and its statistical significance.

Publications

Multi-image colocalization and its statistical significance.

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