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MosaicIA: an ImageJ/Fiji plugin for spatial pattern and interaction analysis.

Shivanandan A, Radenovic A, Sbalzarini IF - BMC Bioinformatics (2013)

Bottom Line: If they do not "feel" each other's presence, their spatial distributions are expected to be independent of one another.Spatial correlations in their distributions are indicative of interactions and can be modeled by an effective interaction potential acting between the points of the two sets.The presented showcases illustrate the usage of the software.

View Article: PubMed Central - HTML - PubMed

Affiliation: MOSAIC Group, Center of Systems Biology Dresden (CSBD), Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr 108, 01307 Dresden, Germany. ivos@mpi-cbg.de.

ABSTRACT

Background: Analyzing spatial distributions of objects in images is a fundamental task in many biological studies. The relative arrangement of a set of objects with respect to another set of objects contains information about potential interactions between the two sets of objects. If they do not "feel" each other's presence, their spatial distributions are expected to be independent of one another. Spatial correlations in their distributions are indicative of interactions and can be modeled by an effective interaction potential acting between the points of the two sets. This can be used to generalize co-localization analysis to spatial interaction analysis. However, no user-friendly software for this type of analysis was available so far.

Results: We present an ImageJ/Fiji plugin that implements the complete workflow of spatial pattern and interaction analysis for spot-like objects. The plugin detects objects in images, infers the interaction potential that is most likely to explain the observed pattern, and provides statistical tests for whether an inferred interaction is significant given the number of objects detected in the images and the size of the space within which they can distribute. We benchmark and demonstrate the present software using examples from confocal and PALM single-molecule microscopy.

Conclusions: The present software greatly simplifies spatial interaction analysis for point patterns, and makes it available to the large user community of ImageJ and Fiji. The presented showcases illustrate the usage of the software.

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Results of applying the plugin to clathrin–β2-AR data from single-molecule PALM. MosaicIA applied to PALM super-resolution imaging in fixed HeLa cells: The green channel (X) shows the GPCR protein β2-AR labelled with PSCFP2. The red channel (Y) shows Clathrin Light Chain-PAMCherry1. (a) Rendering of the PALM image as a probability map showing only molecules that localized into clusters of a given threshold size. (b) These molecules displayed as points without their corresponding localization uncertainty. Clusters of molecules are visualized by circles with × marking the cluster centers. (c,e,g) Distance distributions obtained after fitting the model with a linear L1 potential. (c,d) Fit and estimated interaction potential when using only cluster centers for the analysis. (e,f) Fit and estimated interaction potential when only using all individual molecules. (g,h) Randomization control using a randomly shuffled point pattern X with the same number of points as in c.
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Figure 5: Results of applying the plugin to clathrin–β2-AR data from single-molecule PALM. MosaicIA applied to PALM super-resolution imaging in fixed HeLa cells: The green channel (X) shows the GPCR protein β2-AR labelled with PSCFP2. The red channel (Y) shows Clathrin Light Chain-PAMCherry1. (a) Rendering of the PALM image as a probability map showing only molecules that localized into clusters of a given threshold size. (b) These molecules displayed as points without their corresponding localization uncertainty. Clusters of molecules are visualized by circles with × marking the cluster centers. (c,e,g) Distance distributions obtained after fitting the model with a linear L1 potential. (c,d) Fit and estimated interaction potential when using only cluster centers for the analysis. (e,f) Fit and estimated interaction potential when only using all individual molecules. (g,h) Randomization control using a randomly shuffled point pattern X with the same number of points as in c.

Mentions: We analyze the prototypical GPCR β2-adrenergic receptor (β2-AR) and its internalization in clathrin-coated vesicles post stimulation with the agonist isoproterenol in HeLa cells [19]. β2-AR is labelled with PSCFP2, and clathrin light chains are labeled with PAMCherry1 [19]. Figure 5a shows an exemplary rendered probability map from dual-color PALM after setting a clustering threshold to remove localized molecules that are not within a cluster of at least the threshold size [19]. The estimated locations of these individual fluorescent molecules are shown as dots in Figure 5b, without the localization uncertainty distributions. Circles mark clusters of fluorophores with the cluster centers given by the crosses.


MosaicIA: an ImageJ/Fiji plugin for spatial pattern and interaction analysis.

Shivanandan A, Radenovic A, Sbalzarini IF - BMC Bioinformatics (2013)

Results of applying the plugin to clathrin–β2-AR data from single-molecule PALM. MosaicIA applied to PALM super-resolution imaging in fixed HeLa cells: The green channel (X) shows the GPCR protein β2-AR labelled with PSCFP2. The red channel (Y) shows Clathrin Light Chain-PAMCherry1. (a) Rendering of the PALM image as a probability map showing only molecules that localized into clusters of a given threshold size. (b) These molecules displayed as points without their corresponding localization uncertainty. Clusters of molecules are visualized by circles with × marking the cluster centers. (c,e,g) Distance distributions obtained after fitting the model with a linear L1 potential. (c,d) Fit and estimated interaction potential when using only cluster centers for the analysis. (e,f) Fit and estimated interaction potential when only using all individual molecules. (g,h) Randomization control using a randomly shuffled point pattern X with the same number of points as in c.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4219334&req=5

Figure 5: Results of applying the plugin to clathrin–β2-AR data from single-molecule PALM. MosaicIA applied to PALM super-resolution imaging in fixed HeLa cells: The green channel (X) shows the GPCR protein β2-AR labelled with PSCFP2. The red channel (Y) shows Clathrin Light Chain-PAMCherry1. (a) Rendering of the PALM image as a probability map showing only molecules that localized into clusters of a given threshold size. (b) These molecules displayed as points without their corresponding localization uncertainty. Clusters of molecules are visualized by circles with × marking the cluster centers. (c,e,g) Distance distributions obtained after fitting the model with a linear L1 potential. (c,d) Fit and estimated interaction potential when using only cluster centers for the analysis. (e,f) Fit and estimated interaction potential when only using all individual molecules. (g,h) Randomization control using a randomly shuffled point pattern X with the same number of points as in c.
Mentions: We analyze the prototypical GPCR β2-adrenergic receptor (β2-AR) and its internalization in clathrin-coated vesicles post stimulation with the agonist isoproterenol in HeLa cells [19]. β2-AR is labelled with PSCFP2, and clathrin light chains are labeled with PAMCherry1 [19]. Figure 5a shows an exemplary rendered probability map from dual-color PALM after setting a clustering threshold to remove localized molecules that are not within a cluster of at least the threshold size [19]. The estimated locations of these individual fluorescent molecules are shown as dots in Figure 5b, without the localization uncertainty distributions. Circles mark clusters of fluorophores with the cluster centers given by the crosses.

Bottom Line: If they do not "feel" each other's presence, their spatial distributions are expected to be independent of one another.Spatial correlations in their distributions are indicative of interactions and can be modeled by an effective interaction potential acting between the points of the two sets.The presented showcases illustrate the usage of the software.

View Article: PubMed Central - HTML - PubMed

Affiliation: MOSAIC Group, Center of Systems Biology Dresden (CSBD), Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr 108, 01307 Dresden, Germany. ivos@mpi-cbg.de.

ABSTRACT

Background: Analyzing spatial distributions of objects in images is a fundamental task in many biological studies. The relative arrangement of a set of objects with respect to another set of objects contains information about potential interactions between the two sets of objects. If they do not "feel" each other's presence, their spatial distributions are expected to be independent of one another. Spatial correlations in their distributions are indicative of interactions and can be modeled by an effective interaction potential acting between the points of the two sets. This can be used to generalize co-localization analysis to spatial interaction analysis. However, no user-friendly software for this type of analysis was available so far.

Results: We present an ImageJ/Fiji plugin that implements the complete workflow of spatial pattern and interaction analysis for spot-like objects. The plugin detects objects in images, infers the interaction potential that is most likely to explain the observed pattern, and provides statistical tests for whether an inferred interaction is significant given the number of objects detected in the images and the size of the space within which they can distribute. We benchmark and demonstrate the present software using examples from confocal and PALM single-molecule microscopy.

Conclusions: The present software greatly simplifies spatial interaction analysis for point patterns, and makes it available to the large user community of ImageJ and Fiji. The presented showcases illustrate the usage of the software.

Show MeSH