References Bolte, S. and Cordelieres, F. P. A guided tour into subcellular colocalization analysis in light microscopy.

Transcription

1 Colocalzaton

2 References Bolte, S. and Cordeleres, F. P. A guded tour nto subcellular colocalzaton analyss n lght mcroscopy. Journal of Mcroscopy 224: (2006). Costes, S. V., Daelemans, D., Cho, E. H., Dobbn, Z., Pavlaks, G. and Lockett, S. Automatc and quanttatve measurement of proten-proten colocalzaton n lve cells. Bophyscal Journal 86: (2004). Manders, E.M.M., Verbeek, F.J., and Aten J.A, Measurement of colocalzaton of objects n dual-color confocal mages. Journal of Mcroscopy 169: (1993). And many others..

3 What s colocalzaton? The presence of sgnal ntensty (two or more labels) n the same pxel (physcal/cellular structure) Ultmate lmt: resoluton of the mcroscope (approx. 200x200x800 nm) Colocalzaton nteracton FRET FCS

4 Steps of a colocalzaton experment/study Image acqus,on Image (pre)processng Vsual nspec,on Computa,onal quan,fca,on RGB merge Sca<er plot Intensty Correla,on Coeffcent- based (ICCB) Object- based

5 Requrements on the acquston sde I. - clear spectral separaton Cross- talk Bleed- through FITC and Cy3 excta,on/emsson spectra How to avod? Use approprate (spectrally ds,nct) dyes Seral acqus,on (especally wth the confocals) Eventually spectral un- mxng

6 Requrements on the acquston sde II. - the proper optcal system Can be confocal or wde-feld Aberraton free use a PlanApo objectve Sphercal aberraton Chromatc aberraton Thnk about the refractve ndex msmatch (aberratons) Use pxel-shft free flters (or correct for t) Use hgh NA objectves (resoluton, sgnal ntensty) Check the PSF and the pxel shft

7 Requrements on the acquston sde III. - settng up the detector Important to have Nyqust (2-3x oversamplng) but don t overdo t The nose (S/N rato of the mage) s crtcal so scan slowly/average (confocal), ntegrate long (wdefeld) Use the whole dynamc range (no saturaton), see that the two channels match to each-other

8 Image (pre)processng Background substracton Nose reducton (deconvoluton)

9 Vsual detecton of colocalzaton Is green + red always = yellow? The amount of yellow depends very much on the channel ntens,es your eyes may cheat.

10 The scatter plot Red ntensty Intens,es of the red channel Green ntensty Intens,es of the green channel Color/ntensty of the scatter plot: number of pxels wth a gven ntensty value Fast, relable qualtatve, but no quanttatve nformaton.

11 Some examples for colocalzaton A) Golg stanng (duplcated) B) ER stanng (2 Abs) C) MT Plus end (2 target prot) D) Nucl and Mto stan

12 Intensty correlaton coeffcent based methods Many possble parameters (e.g. Pearson s) The choce (best one) s mage/applcaton/queston dependent no general rules All methods can be calculated for the whole mage or for a ROI Way of calculaton may dffer between software (e.g. ncludng 0 value pxels or not n the average calculaton) Tresholded parameters (manual or automated) Most software packages calculate all of them

13 The Pearson s Coeffcent (PC) R r = ( Ch1 Ch1 mean ) ( Ch2 Ch2 ) mean ( Ch1 Ch1 ) 2 mean Ch2 Ch2 mean ( ) 2 Interpretaton: The values R r = 1 : perfect colocalzaton/correlaton R r = 0 : random (no) colocalzaton R r = -1 : perfect excluson/ant correlaton Conceptually What percentage of varablty n one channel s caused by the varablty n the other channel (Squarng R r and makng t a percentage)

14 Facts about the Pearson s Advantage: Not senstve to the ntensty of a background (e.g. a constant value) Not senstve to the ntensty of the overlappng pxels Dsadvantage: Dffcult to nterpret Affected by the addton presence of non-colocalzng sgnals No nformaton about the ndvdual channels Affected by nose

15 The overlap coeffcent R = Ch1 Ch2 ( Ch1 ) 2 Ch2 ( ) 2 Same as the Pearson s but the mean s not subtracted The values R = 1 : perfect colocalzaton/correlaton R= 0 : random (no) colocalzaton Meanng: R= % of the pxels (objects) overlap Advantage: Easer to nterpret Not senstve to the ntensty of the overlappng pxels Dsadvantage: Senstve to background No nformaton about the ndvdual channels Affected by nose

16 The k overlap coeffcents k 1 = Ch1 Ch2 ( Ch1 ) 2 k 2 = Ch1 Ch2 ( Ch2 ) 2 Obvously: R 2 = k 1 k 2 Advantage: The 2 channels are analyzed separately Addton of a not colocalzed sgnal wll affect only one of the channels Dsadvantage: The parameters scale wth the sgnal ncrease n the other channel

17 Manders (orgnal) coeffcents m 1 = Ch1,coloc Ch1 m 2 = Ch2,coloc Ch2 m 1 comes from k 1 by replacng Ch2 wth 0 f Ch2 = 0 and wth 1 otherwse. (Smlarly for m 2 ) Alternatvely: Ch1,coloc = Ch1 f Ch2 > 0 Values: 0 to 1; m 1 =1 and m 2 =0.4 for a dye par means that 100% of Ch1 pxel ntenstes colocalze wth Ch2, but only 40% of Ch2 pxel ntenstes colocalze wth Ch1 Advantage: Solves the prevous scalng problem Dsadvantage: The parameters scale wth the sgnal ncrease n the other channel

18 Manders (tresholded) coeffcents M 1 = Ch1,coloc Ch1 M 2 = Ch2,coloc Ch2 Ch1,coloc = Ch1 f Ch2 > Treshold Values: 0 to 1; m 1 =1 and m 2 =0.4 for a dye par means that 100% of Ch1 pxel ntenstes colocalze wth Ch2, but only 40% of Ch2 pxel ntenstes colocalze wth Ch1 Advantage: Less senstve to background problems

19 Addtonal varant: colocalzaton coeffcents c 1 = pxels Ch1coloc pxels Ch1 c 2 = pxels Ch2coloc pxels Ch2 relatve number of colocalzng pxels Value between 0 1 (0 no colocalzaton, 1 : all pxels colocalze) lke m 1 and m 2 but all pxels above background count equally (rrespectve of ther ntensty) m 1 and m 2 are the weghted verson a tresholded varant can be also calculated

20 Settng the treshold 1) Fttng a lne (lnear) 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

21 Rule of thumb values Coeffcent Values ndca8ng colocalza8on Values ndca8ng absence of colocalza8on Pearson's correla,on coeffcent (R r ) From 0.5 to 1.0 From 1.0 to 0.5 Overlap coeffcent accordng to Manders (R) Overlap coeffcents k1 and k2 From 0.6 to 1.0 From 0 to 0.6 Any close values, lke 0.5 and 0.6 or 0.8 and 0.9 Any dstant values, lke 0.5 and 0.9 or 0.2 and 0.7 Colocalza,on coeffcents m1 and m2 More than 0.5 Less than 0.5 Colocalza,on coeffcents M1 and M2 More than 0.5 Less than 0.5

22 Two examples Mto ER labelng Mto Mto labelng Pearson's correlaton (R r )=0.34 Overlap coeffcent (R)=0.40 Colocalsaton coeffcent for red (Mred)=0.96 Colocalsaton coeffcent for green (Mgreen)=0.4 Pearson's correlaton Rr=0.93 Overlap coeffcent R=0.94 Colocalsaton coeffcent (red) Mred=0.99 Colocalsaton coeffcent (green) Mgreen=0.9

23 Relevance (statstcal sgnfcance) of the measured parameters Image randomzaton (Costes) - The Ch1 mage s compared to 200 scrambled Ch2 mages - Scramblng: randomly rearrangng the blocks (sze equals to the PSF) of Ch2 Image translaton X drecton (Van Steensel) X-Y and Z drecton (Fay) In all cases the parameter (coloc) s sgnfcant f greater the 95% of the randomzed mages

24 Object based methods 0. Lne profle (for small objects) 1. Segmentaton 2. Determnng the colocalzaton Colocalzed (overlappng) volume Colocalzed (overlappng) area Centrod dstance Advantage: Less dependent on ntenstes (dffuse labelng) Can be automated Dsadvantage: Segmentaton needed (dffcult) Doesn t work for dffuse labelng

25 Cells wth CLIP-170/ EB1 (MT plus end) labelng An example

26 The JACop plugn: Lnks The Olympus nteractve tutoral: An ImageJ based tutoral: The Fj colocalzaton analyss A PerknElmer vdeo tutoral And others..