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.
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 and its modules. 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 Imaris. 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 Imaris.
ImarisColoc provides several methods to select the co-localized voxels in 2D, 3D and 4D images. You can choose from manual, semi-automatic, or fully automatic selection methods to process the overlap between any two color channels in an image at a time. If co-localization analysis is desired for a combination of more than two channels, the first two channels are analyzed, and then the result is processed with each subsequent channel.
ImarisColoc offers a range of different histogram display options to choose from. This unique feature allows users to expand or narrow the region from which histograms for co-localization are computed.
ImarisColoc offers the fastest implementation on the market both for the immediate interactive visualization of selected co-localized regions and the immediate display of derived statistical parameters.
ImarisColoc, based on the selected thresholds, automatically determines statistics such as the percentage overlap of the channels and the co-localization coefficient, to characterize the degree of overlap between two channels in a microscopy image. These values are calculated on a per time point basis for 4D images and are displayed only for the currently displayed time point. Imaris MeasurementPro is NOT required for this functionality.
ImarisColoc allows you to build a new 2D, 3D, or 4D color channel that contains the co-localization results in image form.
ImarisColoc works in 4D so co-localization of an entire time series can be analyzed with just a few clicks of a button.
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