Colocalization software

In some cases, the distribution of two probes might be expected to overlap but not proportionally. For example, the fluorescently labeled cargo of an endosome would be expected to co-occur in the same vesicles labeled with a green fluorescent protein GFP chimera of an endocytic Rab protein, but there is no necessary reason that the amount of cargo should scale with the amount of the Rab protein. In other cases, the two probes might be expected to codistribute proportionally with one another so that the fluorescence levels of probes labeling each would be spatially correlated.

An example of this would be two molecules that interact with the same molecular complexes. Throughout this guide, we will emphasize how the various methods used to measure colocalization differ in their sensitivity to these two components, and how this factor should be considered in choosing a colocalization metric. So, for example, colocalization of fluorescein and rhodamine can be apparent in structures that appear yellow, because of the combined contributions of green and red fluorescence, respectively.

Since both probes are internalized via the same transferrin receptors, they would be expected to codistribute in endosomes following internalization, as is apparent in the constant yellow color of endosomes. In contrast, internalized Texas Red-dextran and Alexa transferrin distribute to two distinct compartments, which appear red and green, respectively, in Fig. Colocalization analysis of endocytic probes.

D : scatterplot of red and green pixel intensities of the two cells shown in A. Arrows indicate examples of lysosomes containing Texas Red-dextran. H : scatterplot of red and green pixel intensities of the image shown in E. Superposition of fluorescence images is certainly the most prevalent method for evaluating colocalization, and tools for displaying multiple-channel fluorescence images as merged color images are implemented in all biological image analysis software.

However, results can be ambiguous. The problem is that an intermediate color, indicating colocalization, is obtained only if the intensities of the two probes are similar. The insets in Fig. For this reason, the overall degree of colocalization throughout a sample may be visually apparent only under very specific labeling conditions, when the fluorescence of the two probes occurs in a fixed and nearly equal proportion. In general, the most reliable method for visually comparing the relative distribution of two probes is a side-by-side comparison of the two images, with arrows provided as landmarks compare the colocalization of the probes in Fig.

The results of fluorescence colocalization studies can also be represented graphically in scatterplots where the intensity of one color is plotted against the intensity of the second color for each pixel, similar to the output provided for flow cytometry data. Under the conditions of proportional codistribution, such as in the data shown in Fig. In contrast, the lack of colocalization of dextran and transferrin in the image shown in Fig.

The ability to produce and export scatterplots is common to nearly all biological image analysis software packages. Scatterplots can provide additional insights into colocalization studies. First, they can be used to identify populations of distinct compartments. Our laboratory has used scatterplots to identify two populations of endosomes in MDCK epithelial cells, one at the apex that is enriched in internalized IgA and lacking internalized transferrin, and the other in lower portions of the cell that contains both IgA and transferrin Fig.

These two compartments are readily distinguished in scatterplots in which two different linear relationships are obtained, with the slopes reflecting the distinctive ratios of internalized IgA and transferrin in each type of compartment Fig. The scatterplot obtained from images of cells treated with brefeldinA was used to support the observation that brefeldinA induced a fusion of these different compartments, resulting in a population of endosomes with a single, intermediate ratio of IgA to transferrin Fig.

The visual techniques described above are useful for exploring the relative distribution of different molecules in cells. Superposition of images is useful for providing a spatial sense of colocalization, identifying regions of the cell or compartments where molecules colocalize. Scatterplots are useful for detecting the presence of different populations of compartments.

They also provide a qualitative indication of the degree of colocalization. However, these representations are generally not helpful for comparing the degree of colocalization in different experimental conditions nor for determining whether the amount of colocalization exceeds random coincidence. In the next sections we will describe several approaches that can be used to quantify colocalization.

These methods are simple to employ and have been implemented in a variety of image processing software packages. However, there are numerous subtleties and assumptions in each that must be understood before they can be productively applied to biological images.

The discussion of scatterplots above suggests the use of Pearson's correlation coefficient PCC as a statistic for quantifying colocalization. The formula for PCC is given below for a typical image consisting of red and green channels. Values near zero reflect distributions of probes that are uncorrelated with one another.

So, for example, PCC measures 0. In contrast, only 0. PCC measures the pixel-by-pixel covariance in the signal levels of two images. Because it subtracts the mean intensity from each pixel's intensity value, PCC is independent of signal levels and signal offset background. Thus PCC can be measured in two-color images without any form of preprocessing, making it both simple and relatively safe from user bias.

Tools for quantifying PCC are provided in nearly all image analysis software packages. Whereas the meaning of extreme values of PCC is generally clear, intermediate values are more difficult to interpret, except when used in comparative studies. Our laboratory has used PCC in a number of studies as a means of characterizing different endocytic pathways.

For example, quantitative comparisons of the distributions of Rab10, Rab11, and internalized transferrin and immunoglobulin A IgA were used to identify two types of endocytic compartments in polarized MDCK cells: one predominantly containing internalized transferrin and associated with Rab10 Fig.

The same analysis showed that a single point mutation in Rab10 altered its distribution such that it matched Rab Whenever possible, the appropriate performance of a colocalization analysis should be demonstrated with positive and negative controls. In the studies described above, the positive control of the colocalization analysis was provided by evaluation of cells that had internalized two different colors of transferrin. The negative control was provided by quantifying PCC for the same images, but after rotation of one by 90 degrees, a condition in which only random colocalization is observed Fig.

In many image analysis software packages, PCC is measured for entire images by default. The issue here is that, since PCC values depend upon a simple linear relationship, they will be depressed if measured over a field of cells with heterogeneous expression or uptake of the target molecules, thus under-representing the degree of correlation.

For example, we find that transferrin and IgA are both internalized into the same early endosomes of Chinese hamster ovary CHO cells transfected with transferrin receptor and polymeric Ig receptor Fig. Indeed, the two are internalized in a constant proportion that reflects the relative number of transferrin and polymeric Ig receptors expressed by the cell.

However, because the numbers of transferrin and polymeric Ig receptors expressed varies between cells, the ratio of transferrin to IgA internalized likewise varies between cells, an effect that is apparent in the different color of the endosomes in the merged color image.

The different ratios of transferrin to IgA are even clearer in the combined scatterplot of the three cells, which shows three different linear relationships Fig. Accordingly, whereas the PCC for each cell in the field is relatively high 0. D : combined scatterplot of red and green pixel intensities of the three cells in the indicated ROIs of A , showing 3 different linear relations.

Arrows indicate endosomes labeled for both transferrin and Rab H : combined scatterplot of red and green pixel intensities of the three ROIs shown in E , showing 2 different linear relations. I : single image plane of fluorescently labeled actin J and mitochondria in bovine endothelia K. L : scatterplot of red and green pixel intensities of the image shown in I.

Values from pixels outside the cells are shown in red. N : as in M but an image plane passing through the nucleus. O : scatterplot of red and green pixel intensities of the middle cell shown in N.

Values from pixels from the region of the nucleus are shown in red. P : scatterplot of red and green pixel intensities from the cytosolic region of the middle cell, delineated with the ROI shown in N.

The problem of cell-cell variability is particularly pervasive in studies of cells transiently expressing GFP chimeras, since expression of the transfected protein can vary widely between cells. The internalized transferrin Fig. As with the example above, the differences are more apparent in the scatterplot of the combined pixel data, which shows two different linear relationships Fig. As a consequence, while measurement of the PCC in each cell indicates a reasonably strong correlation 0.

This effect results from the fact that these empty areas contain pixels for which both the red and green signals are significantly below their average levels. This point is demonstrated by an analysis of the image shown in Fig.

However, when quantified over the entire image, including empty areas between the cells, PCC increases to 0. By simply including pixels from extracellular regions that lack significant amounts of either red or green signal, the PCC of two essentially uncorrelated probes is significantly increased.

The influence of extracellular pixels obviously depends on their number. For the example described here, more than one-third of the pixels used in this correlation arise from outside the cell depicted in red in the scatterplot shown in Fig. This error, while seemingly obvious, is deceptively easy to commit. For the incautious investigator, measuring PCC over an entire field of cells labeled in this way would result in PCC values that varied inversely with cell plating density.

The examples above demonstrate that carefully outlining the region in which two probes may potentially distribute is critical to accurate measurement of PCC. However, it may not be enough to simply outline the cell; there may be regions within the cell that exclude the structures of interest.

Consider the example of two probes that associate with vesicles. Insofar as intracellular vesicles are excluded from the nucleus, their mutual exclusion from the nucleus is no more meaningful than their mutual exclusion from the extracellular space.

If pixels from these regions are included in analysis, they will artificially inflate PCC measurements, in the same way that pixels from the extracellular space artificially inflate PCC measurements. Figure 3 M shows an image of an MDCK cell in which lysosomes have been labeled with fluorescent dextran and endosomes with fluorescent transferrin. The two probes label distinct compartments, as reflected in a PCC of 0.

However, if we examine an image collected in a focal plane that includes a cross-section of the nucleus Fig. The scatterplot shown in Fig. An alternative approach for excluding irrelevant pixels is to restrict analysis to pixels whose intensity falls above a threshold value. This widely implemented method can be much simpler than the laborious process of manually outlining an ROI on the original image.

However, eliminating low-intensity pixels runs the risk of eliminating regions of mutual exclusion within the cells which, if they represent areas in which the probes could potentially distribute, are meaningful to quantifications of probe distributions.

For the example shown in Fig. Thus an intensity-based procedure that eliminated the pixels of the nuclear region would have the undesired effect of eliminating pixels from the relevant cytosolic region as well. Thus, when using an intensity-based approach for eliminating irrelevant pixels, it is crucial to evaluate its effect on relevant pixels. To productively use PCC to measure colocalization, it is important that investigators understand exactly what PCC measures.

PCC quantifies the degree to which the variability in red and green pixel intensities can be explained with a simple, linear relationship between the two. Thus it is sensitive to both signal co-occurrence the degree to which, for each pixel, red and green intensity values are either both above background or both below background and the more rigorous condition of signal correlation pixel-for-pixel proportionality in the signal levels of the two channels.

To the degree that the biology of the system is such that can be modeled by a linear relationship in the levels of two probes, PCC is an appropriate measure of association. However, there are many biological conditions in which this simple model is inadequate, in which cases PCC measurements only indirectly reflect probe colocalization. First, insofar as PCC measures fit to a single linear relationship, it provides a poor measure of colocalization in more complex situations, such as when probes co-occur in different proportions in different compartments of the cell.

Much like the examples of PCC quantification in heterogeneous cells, PCC values will be depressed if measured in a population of heterogeneous intracellular compartments. For example, as mentioned previously, we have found that transfected MDCK cells internalize transferrin and IgA into the same set of endosomes from which IgA is then sorted to an apical recycling endosome. As a consequence, transferrin and IgA occur at similar concentrations in early endosomes, but the concentration of transferrin is much reduced relative to IgA in the downstream compartment.

These compartments are arrayed along an apical-basal axis such that early endosomes captured in medial planes of the cell Fig. The change in the ratio of IgA to transferrin is shown not only in the change in the color of the overlaid images but also in the scatterplots of the three regions, which show the relationship of the two probes in the apical planes in red, that of the medial planes in green and the intermediate planes in blue Fig.

The presence of two kinds of compartments, with a ninefold difference in the proportion of IgA to transferrin, generates scatter in a two-dimensional scatterplot that cannot be explained with a simple linear model, resulting in a PCC value that under represents the degree of colocalization in this case, 0.

In situations such as this, where the data are more complex than modeled by linear regression, PCC measurements are ambiguous, if not misleading. Colocalization without a simple linear relationship. B : as in A but collected 1.

C : as in A but collected 2. D : scatterplots of red and green pixel intensities of the top cell collected from the focal plane shown in A green , B blue , or C red. Arrows indicate a few examples of compartments labeled with both probes. H : scatterplot of red and green pixel intensities of the cell shown in E. As discussed previously, even if two probes co-occur on the same cellular structures, there may be no reason that they should co-occur in fixed proportion to one another.

For the situation shown in Fig. However, for studies in which proportional codistribution is not necessarily expected, PCC can provide a poor measure of colocalization. Although the two probes distribute to the same intracellular compartments compare Fig. Thus, despite the extensive overlap between the two, PCC is relatively low 0. Thus for investigators primarily interested in quantifying probe overlap, PCC is an equivocal measure of colocalization; depending upon proportionality, extensively overlapping probes may yield either high or low PCC values.

The influence of proportionality on PCC can be directly demonstrated with the following example. Reducing the background levels in the image shown in Fig. If we then convert all of the non-zero values to a constant value, thus removing the correlation of signal levels within and between labeled structures, but not reducing overlap, PCC is reduced to 0. As with the visual inspection of color-merged images, the researcher needs to be clear as to whether probe colocalization in a particular study is expected to be accurately modeled by a single linear relationship.

To the degree that the signal levels of two probes are not predicted to be linearly related, and thus that the investigator is interested in probe occurrence alone, PCC provides an indirect and sometimes poor metric of colocalization. At the opposite extreme, it has also been argued that colocalization should only quantify pixel-by-pixel correlation in the subset of pixels that contain both fluorophores 2.

Limiting measurements to pixels that contain both probes profoundly changes the parameter that is measured by PCC. If one is interested in evaluating the distribution of a probe, the regions from which the probe is excluded are as meaningful as those in which the probe is found as long as these regions are potentially accessible to the probe. In the case of analyzing cellular distributions, if one limits colocalization analysis to pixels that contain both probes, the PCC is changed from a measure of probe codistribution within the cell to one addressing probe codistribution within the structures cohabited by the probes.

As expected, this procedure yields high values of PCC for probes with proportional codistributions. In contrast, this approach yields low PCC values under conditions in which probes overlap but not in a fixed proportion. One significant complication that arises in applying this technique is that it requires methods to classify pixels as either containing or lacking a fluorescent probe.

While this might seem simple, the process of distinguishing signal from background is frequently a complex problem, as described later. In response to the perceived difficulty of interpreting negative PCC values, an alternative but closely related metric, the Manders Overlap Coefficient MOC 34 , was developed. Eliminating the subtraction of mean signals from the equation has the effect of preventing negative MOC values, which may be reassuring to investigators confused by negative measurements of correlation.

However, it has a variety of other consequences that are arguably more confusing. Figure 5 , A — C , shows examples of data that are positively correlated, negatively correlated, and uncorrelated, with PCC values of 0.

Whereas an MOC value of 0. While this is at first confusing if not troubling , this behavior reflects the fact that, unlike PCC, MOC is almost independent of signal proportionality, instead it is primarily sensitive to co-occurrence, the fraction of pixels with positive values for both channels, regardless of signal levels. This is demonstrated in Fig. Whereas PCC values are essentially unaffected by the downward shift in the distributions, MOC values decline as the fraction of pixels with positive values decreases.

MOC reaches 0 only when the two probes are completely mutually exclusive; i. Solid symbols reflect offset in both red and green channels. Open symbols reflect offset in the red channel alone. In the context of the argument made above, that colocalization need not imply proportional codistribution, the insensitivity of MOC to signal proportionality might suggest MOC as a better indicator of colocalization than PCC for some analyses.

However, MOC only indirectly and somewhat unpredictably measures co-occurrence. Whereas PCC provides an effective statistic for measuring overall association of two probes in an image, it has the major shortcoming that it indirectly and sometimes poorly measures the quantity that is typically at the heart of most analyses of colocalization in cell biology: the fraction of one protein that colocalizes with a second protein.

This quantity can be measured via Manders' Colocalization Coefficients MCC 34 , metrics that are widely used in biological microscopy and have been implemented in all biological image analysis software packages. By providing measures of the fraction of total probe fluorescence that colocalizes with the fluorescence of a second probe, MCCs provide an intuitive and direct metric of the quantity of interest for most biological colocalization studies.

While simple in principle, measuring MCC is complicated by the fact that the input values used to measure MCC can almost never be taken directly from the original images. The problem is in the numerator of each expression, where pixel values are included in the sum if they occur in pixels in which the signal from the second probe exceeds zero. Although background could be eliminated in the collection process by adjusting detector settings, microscopists generally maintain a positive offset in detector settings to ensure that weak signals are detected; thus the detection process additionally contributes to background.

Since MCC depends utterly upon the ability to distinguish pixels with signal derived from a labeled structure in the focal plane from pixels whose signal results strictly from background sources, a necessary preliminary step is thus to eliminate the component of pixel intensity derived from background.

The most obvious method for eliminating background is to subtract a global threshold value from the pixel intensities of the image. However, for many images, estimating the appropriate threshold value representing background is challenging.

More importantly, for images in which much of the fluorescence occurs in structures whose intensity is close to that of the background, MCC is very sensitive to the value chosen for the threshold.

Taking the example of the image shown in Fig. The sensitivity of MCC to the estimate of background is disquieting and indicates the importance of an automatic, or at least nonsubjective, reproducible method for determining background. Costes et al. In this approach, PCC is measured for all pixels in the image and then again for pixels for the next lower red and green intensity values on the regression line.

This process is repeated until pixel values are reached for which PCC drops to or below zero. The red and green intensity values on the regression line at this point are then used as the threshold values for identifying background levels in each channel. Only those pixels whose red and green intensity values are both above their respective thresholds are considered to be pixels with colocalized probes. The Costes method for estimating thresholds is a robust and reproducible method that can be easily automated, both speeding processing and eliminating user bias.

In many cases, the Costes method provides a quick and effective method for distinguishing labeled structures from background, thus supporting accurate measurement of MCC. The Costes automatic thresholding method. B : binary versions of images shown in A , after applying Costes threshold. Pixels with positive signals for both probes are shown in white. D : binary versions of images shown in C , after applying Costes threshold. F : individual images from E , with arrows indicating compartments containing both probes.

Comparison of the individual images Fig. As with any image analysis technique, the results of the Costes thresholding method should always be checked visually. Under some circumstances, the Costes procedure can fail to identify a useful threshold.

For example, previous studies have shown that Costes thresholding struggles with images that have very high labeling density or large differences in the number of structures labeled with each probe In our experience, the Costes method is effective for images with high signal-to-background ratios, but in images with low signal levels it frequently identifies a threshold value that is so low that it fails to discriminate labeled structures from background.

Similar results were obtained in an analysis of the distribution of two transferrin probes, shown in Fig. For these examples, it is clear that pixel values corresponding to the point on the regression where the correlation shifts from positive to negative are too low to adequately discriminate labeled from unlabeled cellular structures, leading to meaningless MCC measurements.

Software implementations of the Costes algorithm typically accommodate this problem by including the capability to change the point on the regression used to identify thresholds.

However, in doing this, one negates a primary advantage of the Costes approach: that it removes subjectivity from MCC computations. An alternative nonsubjective approach might be to identify the thresholds as the lowest points on the regression line where the correlation remains significant.

This criterion will result in higher thresholds but is complicated by the problem of significance testing of correlations, a thorny problem that we discuss later. Background correction via Costes automatic thresholding method and median subtraction. B : individual images from A. F : binary versions of images shown in E , after applying Costes threshold. G : binary version of image of Texas Red transferrin shown in E , after applying a threshold of H : binary version of image of Texas Red transferrin shown in E , after applying a threshold of I : pixel intensities along a line in the image of Texas Red transferrin shown in E.

Green, original intensities. Black, local median intensities. Red, intensities after subtraction of local median from original intensities.

Blue, Costes threshold. L : individual images from K. Arrows indicate endosomes labeled for both probes. Regardless of the criterion used to select the thresholds, the Costes approach fails to address a more general problem for thresholding images: background levels vary spatially in many cases so that no one background value is appropriate for the entire image.

Spatial variation in background may result from spatial inhomogeneity in illumination or detection or from out-of-focus fluorescence, which may be appreciable even in confocal fluorescence images. This problem is exemplified in the image of an MDCK cell that has internalized two fluorescent conjugates of transferrin into endosomes shown in Fig.

Costes thresholding results in classification of nearly the entire image as positive for red and green probes Fig. As with the other examples described above, the Costes method identifies thresholds that are too low to distinguish labeled structures from background in this image. However, the problem is not simply one of underestimating the appropriate threshold value. Increasing the threshold distinguishes individual endosomes in the periphery of the cell, but not at the center, a thicker part of the cell Fig.

Increasing the threshold further eliminates background around the endosomes at the center of the cell but eliminates the peripheral endosomes altogether Fig. It is clear that a single threshold value will not satisfactorily identify background throughout this image. In this approach, the image is spatially filtered to remove pixel noise and a background image is constructed in which the value of each pixel in the original image is replaced with the median intensity in the region surrounding the pixel.

This background image is then subtracted pixel-by-pixel from the filtered image to obtain a background-subtracted quotient image. As long as the size and density of objects is such that they occupy less than half of the region size, the median provides an accurate measure of the local background.

Thus the size of the region is critical; it must be large enough to predominantly measure the region surrounding objects but small enough to reflect spatial variation. Since there are some low residual values remaining in pixels of unlabeled regions after median subtraction, a small value is subtracted from the quotient image, producing an image with zero values in unlabeled regions, making it suitable for quantifying MCCs. The effectiveness of median subtraction is demonstrated in the line intensity profiles shown in Fig.

The pixel intensity along a line traced over the image shown in Fig. It can be seen that a background level determined by the Costes threshold indicated in blue fails to discriminate individual endosomes, identifying the entire region of this line as above background. Subtracting the median intensities from the original image results in an intensity profile shown in red , for which a single value can be subtracted to distinguish individual endosomes from background.

Figure 7 J shows binarized versions of the images shown in Fig. Visual inspection of this image shows that this process has more effectively eliminated background from the image, more clearly distinguishing individual transferrin-containing endosomes in both the periphery and perinuclear regions of the cell.

Median subtraction has likewise effectively isolated endosomes from background in the image shown in Fig. A more complex situation is shown in Fig. Whereas the Costes method effectively isolates individual endosomes in the image of diI-LDL, a much larger region of the cytosol is identified as above background in the image of GFP-Rab7 Fig.

Better discrimination of the intracellular compartments is obtained using backgrounds applied after median subtraction Fig. However, one could fairly ask whether median subtraction is appropriate for analyzing the distribution of a protein like GFP-Rab7. Whereas we can confidently ignore cytosolic signals in samples labeled by probes that we know are contained within punctate vesicular compartments, a sizable fraction of peripheral membrane proteins like Rab7 will in fact be located in the cytosol.

By eliminating this diffuse fluorescence from analysis, median subtraction restricts colocalization analysis to the vesicular pool of GFP-Rab7. As with the Costes method described above, one must always evaluate the results of the method used to identify image background and choose the method that is appropriate to the biology of the system. Strictly speaking, neither is superior to the other; both have strengths and weaknesses that, depending on the situation, make one or the other the preferred metric.

MCC is also more useful for data that are poorly suited to the simple, linear model that underlies PCC. For example, in the image shown in Fig. For this study, MCC would be considered the better metric of colocalization because it is independent of signal proportionality. In general, cases in which probes are not proportionally codistributed will yield ambiguous, intermediate PCC values that are hard to interpret.

Rather than indicating a partial colocalization, these intermediate values may indicate a mismatch between the data and the underlying model of PCC. This is important when the probes distribute to different kinds of compartments, as for example, in the case in which all of A is found in compartments containing B , but B is also found in additional compartments lacking A.

Consider the example of a study in which an investigator has fluorescently labeled a protein that associates with vesicles and would like to know if these vesicles associate with microtubules. Thus, with respect to the question of whether the vesicular protein associates with microtubules, the same low PCC value is obtained for essentially opposite experimental outcomes.

This is another example of complex data in which pixel intensities of the two probes are not related by a simple, linear relationship. MCC analysis is also more appropriate for three-dimensional analysis of colocalization, which is required for studies in which probe colocalization varies spatially in a cell. While these variations may be appropriately sampled when they occur within a single focal plane, they are insidious when they occur vertically in a cell since results become completely dependent on the particular focal plane that was captured.

This kind of vertical heterogeneity is apparent in the distributions of internalized IgA and transferrin shown in Figs.

While PCC can be quantified in three-dimensional image volumes, it is poorly suited to the kind of complexity that requires three-dimensional analysis in the first place, as discussed above. Quantification of an overall PCC for a three-dimensional volume of the cell requires delineation of the region-of-interest for each focal plane from the volume and combination of all of the identified voxels into a single array from which PCC is calculated Since MCCs are not influenced by areas from which both probes are excluded, MCC does not require this painful delineation of the region-of-interest.

Measurement of three-dimensional MCCs can be accomplished either manually, by simply dividing colocal fluorescence by total fluorescence from an entire stack of images as described in Refs. If circumstances require measurement of PCC throughout the volume of a cell, a simpler alternative to three-dimensional analysis that frequently yields comparable results is to measure PCC in a projected image of the volume 4.

The projected image may consist either of the sum of all of the images of the volume or the maximum intensity value for each x , y position found throughout the volume. This approach is best limited to cells with limited depth and label density such that structures do not overlap when projected into a single image.

For such cells, projection essentially results in collection of all of the structures from the volume into a single image that is much easier to evaluate.

In the case of the relatively flat cells shown in Fig. MCC analysis is less forgiving of overlap occurring in the projection process and should seldom be applied to projected data, where it is likely to generate spuriously high estimates of overlap. For example, analysis of the three-dimensional volume of the two cells shown in Fig. We emphasize that although we have included projected images as examples in this review, quantifications of projections must always be carefully validated by comparison with results obtained from three-dimensional analysis.

The major drawback of MCC is that it is complicated by the need to be able to reliably identify background levels in an image and thereby identify labeled structures. The surprisingly difficult problem of distinguishing label from background is one that has no single answer; different images require different strategies. For some images a single threshold value derived via the Costes approach may suffice.

Other images may require locally determined background levels. Still others may require more elaborate methods of object discrimination that may be daunting for many cell biologists seeking an answer to what seems like a relatively simple question. In our experience median subtraction is effective for discriminating punctate compartments but less effective for other kinds of structures.

Other studies have demonstrated effective segmentation of biological structures using Laplace 41 , Sobel 24 , or watershed filters The utility of these approaches for any given application will generally require careful evaluation and optimization. For images where background correction is challenging, PCC analysis may still be the preferred method, as it requires no image preprocessing of any kind.

Despite what may seem like a relentless emphasis on problems and complications, this review is not intended to dishearten investigators seeking a rigorous method for analyzing colocalization.

The wide availability of software tools for visualizing and quantifying colocalization has made it extraordinarily easy to conduct colocalization studies, and while it is important to consider the foregoing caveats, identifying and applying the proper technique is actually straightforward.

A general workflow is schematized in Fig. Schematic of a general colocalization analysis workflow. Upon determining that colocalization analysis is appropriate to the biological question, an in investigator first needs to validate the fluorescent probes used to identify molecular localizations.

The image collection system must be optimized to detect the dimmest structures without saturating signal levels of the most heavily labeled structures. The optics of the system should be optimized to provide effective discrimination such that each detector channel sensitively detects one or the other probe without cross-contamination of signal between the two signals. A preliminary step in PCC analysis is to visually evaluate scatterplots of the pixel intensity data to evaluate whether the data follow a simple linear relationship.

With this condition satisfied PCC can be quantified either in single images or for three-dimensional image volumes in designated ROIs that limit analysis to individual cells. If the data fail to follow a simple linear relationship, MCC may be the preferred approach for quantifying colocalization. A preliminary step in MCC analysis is to identify the threshold value necessary to distinguish labeled structures from background.

With this accomplished, MCC can be quantified either in single images or for three-dimensional image volumes in designated ROIs that limit analysis to individual cells. Live Snapper. Thibault Lagache. Alexandre Dufour. Nicolas Chenouard. Fabrice de Chaumont. IcyLyd Very nice plugin regrouping various colocalization methods.

I would love to use it in batch thanks to a protocol block. Very nice to have the statistics missing from the colocalizer! I was wondering if for the object-based colocalization would be possible to also have the colocalized components, in order to display them on the original image? The new update have a problem. I can not use colocalization studio. The plug in says i have to set detection set, but it is unable to set.

The previous version worked very well until now. Changelog Version 1. Description: bug correction: ask the user to select detections when launching the plugin. Description: -implements a manual counting of objects at a distance. Description: -bugs fixed -possibility to define ROI with detections convex hull.

Description: Option to export colocalized detections as ROIs. Description: Compute the maximum of the Ripley's K function. Link Text. Open link in a new tab. No search term specified.