colocalization - usp · • aberration free – use a planapo objective – spherical aberration...
TRANSCRIPT
References
• Bolte, S. and Cordelieres, F. P. A guided tour into subcellular colocalization analysis in light microscopy. Journal of Microscopy 224: 213-232 (2006).
• Costes, S. V., Daelemans, D., Cho, E. H., Dobbin, Z., Pavlakis, G. and Lockett, S. Automatic and quantitative measurement of protein-protein colocalization in live cells. Biophysical Journal 86: 3993-4003 (2004).
• Manders, E.M.M., Verbeek, F.J., and Aten J.A, Measurement of colocalization of objects in dual-color confocal images. Journal of Microscopy 169: 375-382 (1993).
And many others…..
What is colocalization?
• The presence of signal intensity (two or more labels) in the same pixel (physical/cellular structure)
• Ultimate limit: resolution of the microscope (approx. 200x200x800 nm)
• Colocalization ≠ interaction – FRET – FCS
Steps of a colocalization experiment/study
Image acquisi,on
Image (pre)processing
Visual inspec,on Computa,onal quan,fica,on
RGB merge Sca<er plot Intensity Correla,on Coefficient-‐based (ICCB)
Object-‐based
Requirements on the acquisition side I. - clear spectral separation
FITC and Cy3 excita,on/emission spectra
Cross-‐talk Bleed-‐through
How to avoid? • Use appropriate (spectrally dis,nct) dyes • Serial acquisi,on (especially with the confocals) • Eventually spectral un-‐mixing
Requirements on the acquisition side II. - the proper optical system
• Can be confocal or wide-field • Aberration free – use a PlanApo objective
– Spherical aberration – Chromatic aberration
• Think about the refractive index mismatch (aberrations)
• Use pixel-shift free filters (or correct for it) • Use high NA objectives (resolution, signal intensity) • Check the PSF and the pixel shift
Requirements on the acquisition side III. - setting up the detector
• Important to have Nyquist (2-3x oversampling) but don’t overdo it
• The noise (S/N ratio of the image) is critical so scan slowly/average (confocal), integrate long (wide-field)
• Use the whole dynamic range (no saturation), see that the two channels match to each-other
Visual detection of colocalization
Is green + red always = yellow?
The amount of yellow depends very much on the channel intensi,es – your eyes may cheat.
The scatter plot
Intensi,es of the green channel
Intensi,es of the
red channe
l
Red intensity
Green intensity
Color/intensity of the scatter plot: number of pixels with a given intensity value
• Fast, reliable qualitative, but no quantitative information.
Some examples for colocalization A) Golgi staining (duplicated) B) ER staining (2 Abs) C) MT Plus end (2 target prot) D) Nucl and Mito stain
Intensity correlation coefficient based methods
• Many possible parameters (e.g. Pearson’s) • The choice (best one) is image/application/question
dependent – no general rules • All methods can be calculated for the whole image or for
a ROI • Way of calculation may differ between software (e.g. including 0 value pixels or not in the average
calculation) • Tresholded parameters (manual or automated) • Most software packages calculate all of them
The Pearson’s Coefficient (PC)
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Rr =
Ch1i −Ch1mean( )⋅ Ch2i −Ch2mean( )i∑
Ch1i −Ch1mean( )2 ⋅ Ch2i −Ch2mean( )2i∑
i∑
Interpretation: • The values
• Rr= 1 : perfect colocalization/correlation • Rr= 0 : random (no) colocalization • Rr= -1 : perfect exclusion/anti correlation
• Conceptually “What percentage of variability in one channel is caused by the variability in the other channel” (Squaring Rr and making it a percentage)
Facts about the Pearson’s
Advantage: • Not sensitive to the intensity of a background (e.g. a constant
value) • Not sensitive to the intensity of the overlapping pixels
Disadvantage: • Difficult to interpret • Affected by the addition “presence” of non-colocalizing signals • No information about the individual channels • Affected by noise
The overlap coefficient
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R =
Ch1i ⋅ Ch2ii∑
Ch1i( )2 ⋅ Ch2i( )2i∑
i∑
• Same as the Pearson’s but the mean is not subtracted • The values
• R = 1 : perfect colocalization/correlation • R= 0 : random (no) colocalization
• Meaning: R= 0.5 – 50% of the pixels (objects) overlap
Advantage: • Easier to interpret • Not sensitive to the intensity of the overlapping pixels Disadvantage: • Sensitive to background • No information about the individual channels • Affected by noise
The k overlap coefficients
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k1 =
Ch1i ⋅ Ch2ii∑
Ch1i( )2i∑
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k2 =
Ch1i ⋅ Ch2ii∑
Ch2i( )2i∑
Advantage: • The 2 channels are analyzed separately • Addition of a not colocalized signal will affect only one of the channels Disadvantage: • The parameters scale with the signal increase in the other channel
Obviously:
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R2 = k1⋅ k2
Manders (original) coefficients
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m1 =
Ch1i,coloci∑
Ch1ii∑
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m2 =
Ch2i,coloci∑
Ch2ii∑
• m1 comes from k1 by replacing Ch2i with 0 if Ch2i = 0 and with 1 otherwise. (Similarly for m2) • Alternatively: Ch1i,coloc= Ch1i if Ch2i > 0 • Values: 0 to 1; m1=1 and m2=0.4 for a dye pair means that 100% of Ch1 pixel intensities colocalize with Ch2, but only 40% of Ch2 pixel intensities colocalize with Ch1
Advantage: • Solves the previous scaling problem Disadvantage: • The parameters scale with the signal increase in the other channel
Manders (tresholded) coefficients
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M1 =
Ch1i,coloci∑
Ch1ii∑
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M2 =
Ch2i,coloci∑
Ch2ii∑
• Ch1i,coloc= Ch1i if Ch2i > Treshold • Values: 0 to 1; m1=1 and m2=0.4 for a dye pair means that 100% of Ch1 pixel intensities colocalize with Ch2, but only 40% of Ch2 pixel intensities colocalize with Ch1
Advantage: • Less sensitive to background problems
Additional variant: colocalization coefficients
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c1 =pixelsCh1colocpixelsCh1
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c2 =pixelsCh2colocpixelsCh2
• relative number of colocalizing pixels • Value between 0 – 1 (0 no colocalization, 1 : all pixels colocalize) • like m1 and m2 but all pixels above background count equally (irrespective of their intensity) • m1 and m2 are the “weighted” version • a “tresholded” variant can be also calculated
Setting the treshold
1) Fitting a line (linear) to the scatter plot 2) Set a treshold (Tx and a*Tx +b) 3) Calculate Rr (or r) for the ROI 4) Reduce the treshold 5) Stop when Rr = 0
“Rule of thumb” values
Coefficient Values indica8ng colocaliza8on
Values indica8ng absence of colocaliza8on
Pearson's correla,on coefficient (Rr) From 0.5 to 1.0 From –1.0 to 0.5
Overlap coefficient according to Manders (R)
From 0.6 to 1.0 From 0 to 0.6
Overlap coefficients k1 and k2 Any close values, like 0.5 and 0.6 or 0.8 and 0.9
Any distant values, like 0.5 and 0.9 or 0.2 and 0.7
Colocaliza,on coefficients m1 and m2 More than 0.5 Less than 0.5
Colocaliza,on coefficients M1 and M2 More than 0.5 Less than 0.5
Two examples Mito – ER labeling
Pearson's correlation (Rr)=0.34 Overlap coefficient (R)=0.40 Colocalisation coefficient for red (Mred)=0.96 Colocalisation coefficient for green (Mgreen)=0.4
Pearson's correlation Rr=0.93 Overlap coefficient R=0.94 Colocalisation coefficient (red) Mred=0.99 Colocalisation coefficient (green) Mgreen=0.9
Mito – Mito labeling
Relevance (statistical significance) of the measured parameters
• Image randomization (Costes) - The Ch1 image is compared to 200 “scrambled” Ch2 images - Scrambling: randomly rearranging the blocks (size equals to the PSF) of Ch2
• Image translation – X direction (Van Steensel) – X-Y and Z direction (Fay)
• In all cases the parameter (coloc) is significant if greater the 95% of the randomized images
Object based methods
0. Line profile (for small objects) 1. Segmentation 2. Determining the colocalization
– Colocalized (overlapping) volume – Colocalized (overlapping) area – Centroid distance
Advantage: • Less dependent on intensities (diffuse labeling) • Can be automated
Disadvantage: • Segmentation needed (difficult) • Doesn’t work for diffuse labeling
Links • The JACop plugin: http://imagejdocu.tudor.ludoku.phpid=plugin:analysis:jacop_2.0:just_another_colocalization_plugin:start
• The Olympus interactive tutorial: http://www.olympusfluoview.com/java/colocalization/index.html
• An ImageJ based tutorial: http://www.med.unc.edu/microscopy/resources/learning/colocalization-tutorial/Colocalization%20Overview/
• The Fiji colocalization analysis http://pacific.mpi-cbg.de/wiki/index.php/Colocalization_Analysis
• A PerkinElmer video tutorial http://www.cellularimaging.com/tutorials/colocalization/
And others…..