Imaris Colocalization Software | Andor


Obtaining accurate information about the position of stained tissue and cellular components is the primary goal of digital microscopy. ImarisColoc has been designed to give researchers the most powerful colocalization analysis tool to quantify and document co-distribution of multiple stained biological components.

  • Utilize multiple methods to select colocalization in images
  • Obtain colocalizaton statistics in real time
  • Present colocalized data as a new 3D or 4D color channel



Isolate, Visualize And Quantify Colocalized Regions

Cellular physiologists and microscopists use co-localization to support imaging data concerning the location of cellular components. Historically co-localization was documented by showing yellow regions on the screen or in paper prints where a red and a green channel overlapped. There was no statistical significance attached or quantitative information provided for such claims of co-localization. Often MIP projections were made of 3D images and the resulting yellow overlap, was called co-localization. Unfortunately, structures that happened to be overlapping each other from the perspective of the viewer were then called co-localized, even though in the Z dimension the structures we not close to one another. ImarisColoc completely departs from this qualitative approach and provides statistically significant measurements linked to precise views. ImarisColoc has been designed to give tools to quantify and document co-distribution of multiple stained biological components. Of course, ImarisColoc works in 3D and 4D and each operation is speed-optimized to give you instant results.

Unlike most other commercial products, ImarisColoc helps you with the key decision point in the analysis. The start of any co-localization analysis is the exclusion of regions that will only add noise and no signal. ImarisColoc provides possibilities to do this via masking out regions and by excluding certain intensity ranges. Masking can be completed within ImarisColoc using the intensities of one of the channels being analyzed or any other channel. Masking can also be completed as part of the functions of Imaris MeasurementPro. The determination of intensities to include / exclude from the study, i.e. the threshold selection, is achieved by thresholding the source channels used in the analysis. Several manual procedures such as selection in a scatter plot, selection in a histogram, or semi-automatic selection in the image itself can be used but these methods naturally bring along the risk of introducing user biases.

ImarisColoc makes it possible to automate the selection of the thresholds and get the user bias out of the equation. ImarisColoc utilizes the algorithms by Costes et. al (1) in the automatic co-localization selection. This also allows co-localization analysis to be performed on diffuse signals. With such signals it does not make sense to threshold each of the signals individually but the two thresholds must be chosen together. Without ImarisColoc it would be difficult to exactly determine thresholds that exclude noise but none of the signal either for structural or for diffuse stains. Time dependant co-localization can also be analyzed with the automatic threshold method of Imaris Coloc. The thresholds are automatically selected for each time point, cutting down analysis time, and improving accuracy over selection of a single threshold.

ImarisColoc provides an array of statistical parameters that include the number of co-localized voxels, the % of dataset, ROI or channel that is co-localized. More importantly, ImarisColoc offers a choice of well-established co-localization coefficients, the Pearson’s coefficient and also Manders coefficient. These coefficients allow you to check the statistical validity of the co-localization selection. Every time the co-localization selection is updated, immediately the results are updated as well. The image then shows the co-localized region plus the table with key statistical parameters such as the % of co-localized intensity and the correlation coefficient is updated.

With ImarisColoc you can easily generate a new channel that only contains voxels that represent the co-localization result. This result allows ImarisColoc to seamlessly work with all other functions of Imaris. This integration results in a short turn-around cycle for image analysis and enables users to change analytical parameters based on findings shown in the 3D displays of. Because co-localization results are displayed as a separate color channel, they can be visualized with the original data or many be segmented, quantified and tracked like any other color channel in.

(1) Costes SV, Daelemans D, Cho EH, Dobbin Z, Pavlakis G, Lockett S: Automatic and quantitative measurement of protein-protein colocalization in live cells. Biophysical journal 2004, 86(6):3993-4003.

Features and Benefits

Features Benefits
Automatic Mode Perform an automatic threshold run on both channels based on an advanced algorithm developed by Costes and Lockett at the National Institute of Health (NCI/SAIC). This algorithm relies on the exclusion of intensity pairs that exhibit no correlation. Before the thresholds for the channels are automatically computed ImarisColoc performs a separate analysis, which determines the probability of having non-random co-localization.
Threshold Mode Select thresholds from a 1D histogram for each channel or a 2D scatter plot showing intensity pairs in the image.
Polygon Mode Choose an arbitrarily shaped region from the 2D scatter plot to select co-localized voxels and exclude voxels showing no co-localization but only cross-talk.
Contouring Interactively click on the image to create a closed contour line surrounding areas equal to the intensity of the point selected and thus interactively choose the thresholds for each channel directly from the image.
Single Slice Histograms Single slice histograms allow you to easily recognize planes in Z with prominent co-localization because only data from the currently selected slice is shown in the 2D scatter plot. The scatter plot changes interactively as you browse up and down through the slices.
Single Time Point Histograms Spot single time points with high co-localization while browsing through time, as this option displays in the scatter plot all the voxels in all the slices of the 3D image for a single time point. The scatter plot changes interactively as you shift time points.
All Time Points The scatter plot displays information on all the voxels in the entire 3D image and at every time point all at the same time.
Masking A color channel can be selected as a masking area for the entire analysis. This feature allows for the definition of a region of interest (ROI) for the entire analysis. A masking channel can be one of the channels being analyzed or can be a third microscope color channel acquired with settings that allows for the definition of a region of interest by simple intensity selection. All voxels outside of the region of interest defined by the mask channel are ignored for the co-localization analysis and appear as crosshatched in the preview window. As the threshold for the mask is selected the area to be included or excluded in the analysis is visualized interactively. The mask channel is most commonly used in conjunction with the “Automatic Threshold” function. For this function, the selection of an appropriate ROI is essential for the algorithm to work properly.
Coloring The scatter plot can be displayed with colors showing the frequency of the occurrence of intensity pairs in the image or can simply be displayed as a black and white plot.
Scaling The scatter plot can be displayed on a standard or log scale.
Display The visual image displays an overlay of co-localized voxels, which are immediately updated after changes to input parameters are made.
Statistics An instant preview of key statistical values are displayed as selections are made.
Scatterplot The scatter plot is immediately updated when the choice of data to include or display in the plot is changed.
Pearson's Correlation Coefficient The Pearson's correlation coefficient in the co-localized region or the full volume can be used to measure if the overlap of the voxels in the image has a positive correlation (a value of 1), a negative correlation (a value of -1) or no correlation (a value of 0).
Manders Coefficient The Manders Coefficient is additional statistical value that is based on the Pearson’s coefficient with average intensities being taken out of the mathematical expression. This coefficient varies from 0 to 1 with 0 corresponding to non-overlapping images and 1 corresponding to 100% co-localization.
Co-localized Voxels A count based on the selection criteria of the co-localized voxels in the dataset and the present time point.
Co-localization Percentages ImarisColoc calculates several co-localization percentages. The percentages are calculated based both on volume of a channel and material of a channel (intensity) in relation to the other channel. The percentages are also calculated for both an ROI and on the entire dataset.
External Output All the statistics that are calculated in ImarisColoc may be exported as a CSV (comma separated value) file so that they can be used with any statistical analysis package.
Visualization Co-localization results may then be visualized in any other view alongside the rest of the original data
Analysis Co-localization results may be further analyzed with any of the other Imaris modules, such as measurement, tracking, or tracing.
Output Choices You can choose the intensity of the resulting co-localization channel as a constant value or a value that is calculated based on the source channels.
Time Dependent Co-Localization Channel Build a time dependent co-localization channel and monitor changes of co-localization in a time series of any desired size. With this method the automatic threshold will be applied correctly, even if the relationship between intensity and correlation is shifting over time because an automatic threshold calculation occurs for each time point.


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