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. Utilizing a choice of manual, semi – automatic, and automatic co-localization selection methods, ImarisColoc enables easy isolation, visualization, and quantification of regional overlap of multiple stains in 3D and 4D images.
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.
(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.
The ImarisColoc functionality requires Imaris.
(1) Selection of Co-localized Voxels
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.
(2) Histogram Display Options
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.
(3) Real-Time Performance
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.
(4) Numerical Data Output
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.
(5) Graphical Output
ImarisColoc allows you to build a new 2D, 3D, or 4D color channel that contains the co-localization results in image form.
(6) Time Dependent Co-localization
ImarisColoc works in 4D so co-localization of an entire time series can be analyzed with just a few clicks of a button.
Channels for which analysis will be performed on
- Choose a combination of any two channels to perform the co-localization analysis on
- A 1D histogram of the intensity values for that channel are displayed
- Histogram can be a linear or log scale
2D Histogram scatter plot
- Plots intensity pairs for the image (I.e. the intensity of each color channel at a specific voxel)
- Plots the frequency of the occurrence of those pairs in the intensity of the points on the graph
- Frequency of pairs in the graph can be color coded into the graph
- A snapshot of just the histogram may be completed
- Provides an image viewing area that is identical to the slice view of Imaris
- Allows interactive zoom, pan, slice selection, and time point selection
- Shows the voxels selected as being co-localized in real time
Statistics Window - Provides a real time preview of all calculated statistics
Region of Interest Window
- Choose from any color channel to be used as a mask
- Mask the dataset based on an intensity threshold from the color channel
- Select the masking threshold interactively in the histogram
- Defines the regions to be included or excluded from the co-localization analysis
- Visually displayed in the slice view
Applies to both the 1D and 2D histograms used for selection
Single slice – Displays the histogram of the voxels only in the currently selected slice
Single time point - Displays the histogram of the voxels only in the currently time point, but for all slices in the time point
All time points - Displays the histogram of all the voxels in the dataset
Ignore border bins - Ignores all value with the highest and lowest intensity, allowing for a more reasonable histogram
As a particular threshold method is used for selection, all other views are updated in real time
Select a threshold value by clicking into the 1D histogram for each channel
Select a threshold value by typing in a threshold for each channel
Select a threshold by selecting a region in the 2D scatter plot
Select a threshold interactively in the slice view of the image
- A contour is drawn around the area that has similar intensity to the voxel at the tip of the mouse pointer
- Left click for the first channel, shift left click for the second channel
- Excellent for interactively visually defining objects and eliminating background
Select a region in the 2D histogram
- Create an arbitrarily shaped region in the histogram using the polygon mode
- Add or delete points to create the region
- Excellent for picking out populations in the histogram
- Good for dealing with cross-talk
Based on an algorithm developed by Costes and Lockett at the National Institute of Health, NCI/SAIC
Based on the exclusion of intensity pairs that exhibit no correlation (Pearson’s correlation below zero)
May only be performed on a specific ROI which excludes background
Non-random co-localization check (first step)
- Image is randomized and smoothed with a PSF as entered in the program
- Pearson correlation coefficient (PCC) is calculated for both the original and a series of the random images described above
- If the PCC of the original is not greater than 95% of the randomizations then analysis cannot continue
- Excellent for validating if automatic method is valid
Intensity thresholds are automatically selected (second step)
- Starts with high intensity values and reduces intensity in a step by step fashion
- Continues to reduce the threshold until the correlation reaches 0
Excellent for removing user bias for threshold selection
The voxels meeting the selected criteria (thus being identified as co-localized) are displayed in the image preview as white voxels.
The resulting voxels are shown / calculated in 2D, 3D or 4D as is appropriate for the image
The intensity of the resulting voxels showing co-localization is determined by either:
- The two original channel intensities
- Defined as a constant value
The resulting voxels and be displayed as a new Imaris color channel
- Allows for 3D or 4D visualization in Imaris with the rest of the data
- Allows for further analysis with any other Imaris module (i.e. Measurement, tracking)
- Allows for co-localization analysis to be performed between the co-localization result and another color channel (For a 3 color comparison in total)
Statistical values are calculated in real time as a preview in the main window
Final statistics are calculated once selection is complete
- Number of co-localized voxels
- Percentage of Region of Interest (ROI) co-localized
- Percentage of channels A or B volume above threshold co-localized
- Percentage of channels A or B material above threshold co-localized
- Percentage of ROI material A or B co-localized
- Pearson's correlation coefficient for ROI volume, data set volume and co-localized volume
- Manders coefficient (original and threshold) for channels A and B
Statistical values exported as a comma separated value file