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Colocalization basics


This wiki page gives some basics on colocalization analysis and how it is influenced by image quality. The example below shows that deconvolution minimizes the false contribution of blurring and noise to colocalization results (indicated in yellow as isosurface rendered objects).


The raw 3D widefield image (left) and its deconvolved version (right) have been analyzed with the Colocalization Analyzer. Red and green channels are rendered as a MIP, and colocalization is shown in yellow as isosurface rendered objects based on the thresholded Pearson coefficient. 
Note that the removal of blurring and noise by deconvolution completely changes the colocalization results. Data used by permission from Dr. Johan de Rooij - Hubrecht Institute, The Netherlands
The raw 3D widefield image (left) and its deconvolved version (right) have been analyzed with the Colocalization Analyzer. Red and green channels are rendered as a MIP, and colocalization is shown in yellow as isosurface rendered objects based on the thresholded Pearson coefficient. Note that the removal of blurring and noise by deconvolution completely changes the colocalization results. Data used by permission from Dr. Johan de Rooij - Hubrecht Institute, The Netherlands




Contents


  1. Colocalization coefficients and maps
  2. Most common coefficients
  3. Effects of imaging conditions

Colocalization coefficients and maps


Red and green


Colocalization refers to different data analysis methods to characterize the degree of overlap between two channels in an image (conventionally called R and G channels, or red and green channels, independently of the WaveLength they have actually registered). A typical application in fluorescence microscopy is to study the presence of two labeled targets in the same region of a cell.

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When viewed in an RGB representation, the overlap of red and green displays as yellow. Whenever you see yellow in the images below, you have colocalizing signals.

100% colocalization


We need to define some statistical figures that properly describe situations like these. Our coefficients must say that a situation like the following one is 100% colocalization:

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66 % colocalization


In this case, only 66% of the signal is colocalized.

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Is this case the same 66% as this other one?

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Clearly not. We may need to use figures that are able to distinguish between both situations. In the first case all the green signal is colocalized with the red one, but only 66 % of the red matches the green. In the second case, both signals are 66% colocalized.

We need coefficients that...


...are sensitive to the addition of extra non-colocalized signal:

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...are insensitive to relative variations of intensities (for example if the power of one of the lasers is less than the other's):

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...are insensitive to the presence of a BackGround (due to an acquisition offset or non specific staining, for example):

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Coefficients and maps


The colocalization coefficients are figures that parametrize the degree of colocalization of the full two-channel image. On the other hand, maps parametrize the level of colocalization locally. In a map, a single colocalization value is calculated per VoXel creating a 3D distribution that can be represented in a 3D image.

See Colocalization Map.

Most common coefficients


The Pearson coefficient provides a measure of how well two signals are related by a linear equation, independently of what is the actual relationship. See Pearsons Interpretation.

On the other hand, the Manders coefficients provide information on how one particular channel matches the other one, separating the information. See Colocalization Theory.

Effects of imaging conditions


During image acquisition in a Fluorescence Microscope many things can go wrong, but even in the best experimental conditions there are two sources of degradation that are physically unavoidable:

  1. Photon Noise, due to the intrinsic nature of photons, and
  2. blur caused by diffraction.

Read Image Formation to see how diffraction, resolution and Numerical Aperture are related.

Let us take a closer look at two nearby signals. In the following examples a red and a green feature are not overlaping, but colocalization is very much affected by...

Blur


The blur around the features provides fake colocalizing signal.

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Noise


Apart from this blur, the Photon Noise will cause signal fluctuations that will affect the analysis.

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Background


On top of that, BackGrounds in the two channels will increase the colocalization levels dramatically.

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Color shift


Misalignment of the detectors (Color Shift) will spoil colocalization, or even introduce fake one.

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Cross-talk


If some signal is detected with the wrong detector (Cross Talk) fake colocalization will happen.

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Example


Images below show improved separation of the red and green channel by deconvolution, which prevents false colocalization analysis from being included in the quantification.



Top picture: XY projections and bottom picture: YZ, XZ projections. Original data as acquired in a Nipkow Disk Microscope shown next to the same image after applying Huygens Deconvolution. The red and green signals are clearly separated much more after the restoration. Calculating colocalization in the original data would have caused fake colocalization levels. Data courtesy Dr. Kozubek, Brno, Czech republic, FP6 3DGenome Project
Top picture: XY projections and bottom picture: YZ, XZ projections. Original data as acquired in a Nipkow Disk Microscope shown next to the same image after applying Huygens Deconvolution. The red and green signals are clearly separated much more after the restoration. Calculating colocalization in the original data would have caused fake colocalization levels. Data courtesy Dr. Kozubek, Brno, Czech republic, FP6 3DGenome Project


Read more in Colocalization Theory.

Contact Information

Scientific Volume Imaging B.V.

Laapersveld 63
1213 VB Hilversum
The Netherlands


Phone: +31 (0)35 64216 26
Fax: +31 (0)35 683 7971
E-mail: info at svi.nl

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