Multi-image colocalization and its statistical significance.
Fletcher PA, Scriven DR, Schulson MN, Moore ED.,Biophys J. 2010 Sep 22;99(6):1996-2005.
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.
Automated organelle-based colocalization in whole-cell imaging.
Woodcroft BJ, Hammond L, Stow JL, Hamilton NA., Cytometry A. 2009 Nov;75(11):941-50.
The use of fluorescence microscopy to investigate protein colocalization is an invaluable tool for understanding subcellular structures and their associated proteins.
However, current techniques are largely limited to two-dimensional (2D) imaging and often require manual segmentation.
Here, we present OBCOL, a methodology to automatically segment and quantify protein colocalization not within an image as a whole but on all individual punctuate organelles
within a 3D multichannel image. A wide variety of colocalization statistics may then be calculated on the objects found,
and features reported for each such as position, degree of overlap between channels, and number of component objects. OBCOL was validated on imaging of two fluorescent
markers (Dextran, EGF) in 3D microscopy imaging. OBCOL's application was then exemplified by investigating the colocalization of three fluorescently tagged proteins
(VAMP3, Rab11, and transferrin) on recycling endosomes in mammalian cells. The methodology showed for the first time the diversity of endosomes labeled with one or more
of these proteins and quantitatively demonstrated the degree of overlap among these proteins in individual recycling endosomes. The consistent segregation of these markers
provides novel evidence for the subcompartmentalization of recycling endosomes. OBCOL is a flexible methodology for 3D multifluorophore image analysis. This study clearly
demonstrated its value for investigating subcellular structures and their constituent proteins.
A novel method for quantified, superresolved, three-dimensional colocalisation of isotropic, fluorescent particles.
Obara B, Jabeen A, Fernandez N, Laissue PP., Histochem Cell Biol. 2013 Mar;139(3):391-402.
Colocalisation, the overlap of subcellular structures labelled with different colours, is a key step to characterise cellular phenotypes.
We have developed a novel bioimage informatics approach for quantifying colocalisation of round, blob-like structures in two-colour,
highly resolved, three-dimensional fluorescence microscopy datasets. First, the algorithm identifies isotropic fluorescent particles,
of relative brightness compared to their immediate neighbourhood, in three dimensions and for each colour. The centroids of these spots are then determined,
and each object in one location of a colour image is checked for a corresponding object in the other colour image.
Three-dimensional distance maps between the centroids of differently coloured spots then display where and how closely they colocalise,
while histograms allow to analyse all colocalisation distances. We use the method to reveal sparse colocalisation of different human leukocyte
antigen receptors in choriocarcinoma cells. It can also be applied to other isotropic subcellular structures such as vesicles, aggresomes and chloroplasts.
The simple, robust and fast approach yields superresolved, object-based colocalisation maps and provides a first indication of protein-protein interactions of fluorescent, isotropic particles.