Colocalisation User Manual
Contents
1. Result Option Description REGRESSION ch2 chiL m b SOLUTION CH1 CH2 THRESH SHOW THRESHOLDS The channel thresholds RcoLoc PEARSON S FOR The Pearson s correlation coefficient for the pixels IMAGE ABOVE above both the channel thresholds THRESHOLD R lt THRESHOLD PEARSON S FOR The Pearson s correlation coefficient for the pixels IMAGE BELOW below both the channel thresholds This should be THRESHOLD close to the CORRELATION LIMIT MI M2 MaNDERS orIGINAL The Manders coefficient for the image Ge M1 sumCuH1_cH2cT0 sumCH1 How much of channel 1 intensity occurs where channel 2 has signal MI TM2 MANDERS USING The Manders coefficient for the pixels above the THRESHOLD channel thresholds TM1 suMCH1_cH2cTT sumMCH1 How much of channel 1 intensity occurs where channel 2 is correlated N NUMBER OF The number of pixels in the analysis region optionally COLOCALISED excludes the zero zero pixels VOXELS NZERO NUMBER OF The number of pixels that are zero for both channel 1 COLOCALISED and 2 VOXELS NCH1ctT NUMBER OF The number of pixels that are above the threshold for NCH2cTT COLOCALISED channel 1 2 VOXELS NCoLoc NUMBER OF The number of pixels that are above the threshold for COLOCALISED both channel 1 and channel 2 VOXELS VoL CoLoc VOLUME The percentage of pixels that are above the COLOCALISED thresholds for both channels VoL Coloc NCo oc TotaLPIxELs Note that TotaLPixecs Is the total number of pixels2. The Process Barch window allows you to select an input directory containing all your images You can then specify an optional output directory and the format for the output images The dialogue provides the ability to select from common commands which are then inserted into the text area You can also open your own macros or paste the code from the ImageJ macro recorder The Process button starts the batch processing For each image in the input folder ImageJ will open the image apply the commands save the image to the output folder if present and then close the image Any results windows generated by the plugin commands will remain open Note that if you do not specify an output directory then the images will not be saved after running the command This is useful if your macro command does not alter the input images The following image shows the Process BatcH window configured to run the Stack CoRRELATION ANALYSER ON all the images in the F Temp directory Batch Process Input F Tempi Output Output Format TIFF Add Macro Code Selectfrom list rune stack Correlation Analyser m ethod Default intersect aggregate Test Open Save Process Cancel
3. Options Det results options 4 4 1 Input Parameters The input parameters specify the images to use for the analysis The user can select the two input channels and specify the regions to use for the analysis Parameter Description CHANNEL 1 2 Specify the channel 1 or 2 image If the user selects an image stack then the channel and frame will be requested during run time This is to eliminate optional fields from the main dialogue ROI FoR CHANNEL 1 2 Specify the region that contains the image signal for channel 1 or The methods are described in above in the section titled Stage 1 Identify Channel Signal If None is selected then only the correlation analysis will be relevant since the Manders coefficient will always be 1 CONFINED COMPARTMENT Specify the region used to confine the displacements Only pixels within this region will be analysed The methods are described in above in the section titled Stage 1 Identify Channel Signal INCLUDE ROIs If this option is selected the confined compartment will be expanded to contain all the pixels in ROI 1 and 2 Otherwise the two ROIs will be cropped to the confined compartment This allows the user to separately define the confined region but ensure that all the channels signal is included in the analysis 4 4 2 Displacement Parameters The displacement parameters control the maximum shift applied to one image relative t
4. e Outputs the analysed regions as a new image e Generates a full results table e Saves results text table and images to a directory e Efficient combined image displacement algorithm is fast e Multi threading support for extra speed Note The plugin presented here is a modification of the original CDA plugin available from the ImageJ wiki http imagejdocu tudor lu doku php id plugin analysis confined displacement algorithm determines true and random co localization start The plugin has been altered to add additional features such as support for 16 bit and 3D images additional region selection options correlation analysis saving the results to file and the reimplementation of the core CDA code 4 3 Method The CDA plugin provides colocalisation analysis for 2D and 3D images The process for using the plugin has two stages identification of the regions containing the signal in each channel and running the CDA analysis 4 3 1 Stage 1 Identify Channel Signal To determine if the signal in two channels is colocated the first step is to assign the region that contains the signal in each channel Additionally it is possible to define a region of the image where the signal is expected to occur This is the Confined region which will be used within the Confined Displacement Algorithm This region assignment process is not performed by the CDA plugin However the plugin provides flexibility in that it recognises different methods for
5. than the actual measure An additional factor to consider is that the information in the two channels may be located in only parts of the overall image for example the nucleus of a cell Consequently the translation shifts may move the signal of the channel into an area of the image where it would never naturally occur This could result in a false conclusion that the two channels are colocated in the original image The solution is to define an area within which the pixels can be translated Pixels moving beyond the region are wrapped to re enter from the other side This method thus maintains the underlying image structure This method is the principle behind the Confined Displacement Algorithm CDA Ramirez et al 4 1 1 Example Input The following figure shows the input required for the CDA plugin The input consists of 3 channels of a microscopy image where the red and green channels are stained for different proteins and the blue is stained for DNA The mask images have been produced by performing thresholding using the ImageJ default threshold method Colocalisation analysis can be performed between any of the channels 6 64x5 BF microns 10 6 64x5 48 microns 10 6 64e5 87 microns 10 lo pixels bit rt lO4x92 pixels bit tr lO4x92 pixels amp bit 4 1 2 Example Output The results of colocalisation analysis between the red and green channels The CDA algorithm was run using all displacements up to 25 pixels The
6. 4 4 3 Options Parameters 2 c cc0 cccccseesiscensenesseeenssansssacseeeeseneseeetversescsdeosenesetewseeeeess 29 ua KIEREN 30 oh Os YA ad Leien EE 32 6 Stack Correlation Analyser PIUQIN ccccccccssceceececeseeceeecceseeceeeceueceeeseesueseueceesseeseesaaes 33 Oo PIU IA TCT AC C annn eteaadseareusee teseseweteewaneeseedaysc EEE Eien 33 OL UE TEE ee e geg ER SEN EE 34 SEN D Header RECOM DEE 34 2 2 TPO SIN RECO EE 35 02 3 CON ClAlON ROC EE 35 7 slack Colocalisation Analyser Ten BEE 36 ep SA Leila lant Ee 37 CAA e Tel e EE 37 PA ROSU TADC EE 38 EE eet 40 9 Appendix 1 Balen ProceSSIN DE 41 S Ee E L e ere tun Ee O EE 41 O72 SISO 2r Pre Nae INDUC ANS ari a eeh Sege 41 GE Process en Bee ul E ne 41 1 Introduction Digital images can be recorded using different light filters Each filter records an image containing only the information from the wavelengths of light that pass through the filter This produces an image with multiple channels each representing different light Colocalisation analysis is used to determine the amount of an image channel s signal that occurs in the same location as another channel If the channels represent specific objects such as fluorescence tagged proteins colocalisation analysis will quantify whether the different objects are found in the same location The GDSC Colocalisation plugins provide various tools to perform colocalisation analysis The following tools are available St
7. Details of the thresholding methods can be found in section 2 1 Image Thresholding Thresholding can be disabled by selecting None LOG THRESHOLDS Record the thresholds to the ImageJ log window A threshold will be calculated for each combination of timeframe and channel e g tici1 threshold 778 tic2 threshold 617 tic3 threshold 2441 SHOW MASK If selected then the mask image calculated using the thresholds will be shown e g This allows the user to check that the mask is selecting the correct regions for analysis SUBTRACT THRESHOLD Subtract the threshold value from each pixel before calculating the Manders coefficient The coefficient then represents the fraction of intensity above the background that overlaps with the other region PERMUTATIONS Specify the number of displacements to use for CDA analysis This will be a random subset of the entire set of possible displacements unless the Permutations is greater than the entire set MAXIMUM SHIFT Set the maximum displacement distance MINIMUM SHIFT Set the maximum displacement distance SIGNIFICANCE Set the p value used to calculate significance 7 2 Results Table The results table contains details about the CDA analysis The table contains the following information Result Description IMAGE The title of the input image P The p value used to assign significance Mecro
8. been opened by the plugin are closed when the plugin window is closed This includes the result table and output images and charts Clicking the Set REsuLT options Checkbox shows the following pop up dialogue EJ ey Set Results Options w kel V Show channel 1 Show channel 2 Show ROI 1 Show ROI 2 wv Show merged channel Show merged Rol Show merged channel max displacement Show merged ROI max displacement Show M1 plot Show M2 plot w Show R plot Show MI statistics Show M2 statistics Show R statistics Save results Results directory fhome scra p Yalue 0 01 lt Permutations 500 OK Cancel The dialogue contains the following settings Parameter Description SHOW CHANNEL 1 2 Show an image of the signal of channel 1 or 2 This represents the input data used in the analysis SHow ROI 1 2 Show an image of the signal region of channel 1 or 2 This represents the region used in the analysis SHOW MERGED CHANNEL Show a combined RGB image that contains both channels Parameter Description SHOW MERGED ROI Show a combined RGB image that contains both channel regions SHOW MERGED CHANNEL MAX DISPLACEMENT Show a combined RGB image that contains both channels but with one channel shifted at the maximum displacement i e representative of a random pair of channels SHOW MERGED ROI max DISPLACEMENT Show a combined RGB image that contains both channel regi
9. chosen Use this option to speed up the CDA algorithm Setting this value to zero causes all permutations to be computed 4 5 Results Table The results table contains details about the CDA analysis The table contains the following information Result Description Exp ID Contains the experiment ID It has the format coaYYYYMMDD_HHwmss where the date section will be set using the current time Note This is used to name the folder within the Resutts DIRECTORY if results are being auto saved CHANNEL 1 2 The name of the image used for channel 1 or 2 ROI 1 2 The method used to define ROI 1 or 2 If an image is used e g Min DISPLAY VALUE OF Use as mask then the image name will be included CONFINED The method used to define the confined compartment If an image is used e g Min pispLay value Or Use as mask then the image name will be included INCLUDE ROIs If TRUE then the ROI regions were used to expand the confined region D max The maximum displacement distance D RANDOM The distance used to define the random background used to buld the probability distribution SAMPLE The number of displacement samples in the probability distribution Bins The number of bins used to calculate the probability distribution P VALUE The p value used for the significance assessment M1 The Manders coefficient for channel 1 M1 av The sample average for M1 M1 sp The sample sta
10. correlated channels requires definition of the channel signal region definition of the region overlap and then analysis of the signal intensity This can be a labour intensive task if performed manually However it is possible to perform all the steps automatically for a fast initial analysis of multiple channel images The Stack CorreLatTiIOn ANALYSER automates the search for correlated channels within a multi channel image The plugin extracts all the channels and frames and performs e Thresholding to create a mask for each channel frame optionally aggregating the z stack e All vs all channel correlation see 4 3 2 2 within the union intersect of the channel masks e All vs all Manders overlap coefficients See 4 3 2 1 if using the intersect of the channel masks For example a 3 channel image will have a correlation analysis performed for 1 vs 2 1 vs 3 and 2 vs 3 for each frame in the image sequence For images with a z stack this can be performed using the entire stack or for each z slice individually The plugin can be used within the ImageJ scripting tools to rapidly test a large number of images for correlated channels see Appendix 1 Batch Processing This allows the user to identify the best candidate images for further analysis 6 1 Plugin Interface The Stack CorreLation ANALYSER plugin uses the standard ImageJ plugin dialogue Select the image to process and then run the plugin This will present the following window EJ
11. defining the signal regions e Using the ImageJ minimum display value e Using an ImageJ ROI e Using an image mask Each of these options are described in further detail below 4 3 1 1 Using the ImageJ minimum display value ImageJ provides the ability to change the range of pixel values that are displayed on screen Any pixel above the maximum display value will be fully saturated pixels below the minimum display value will be black To change the display range use Imace gt Apjgust gt BriGHtness Contrast This will display a small dialogue that has sliders to control the display range Use the Minimum slider to seta different minimum display value This should be adjusted so that any areas that do not contain a strong signal i e background pixels are black To assist in viewing the channel signal you can adjust the Maximum slider to set the major signal regions to fully saturated This process is outlined using an example The following image shows the default representation of an image using the full range of pixel intensity e Tia Red 6 64e5 87 microns OUAsn 16 bit 56K Minimum AR Maximum Ar Brightness Ar Contrast Step 2 Adjust the Minimum slider to exclude the background leaving only the channel Signal 3067s1_ 04 R30 Ws Red 6645 87 microns 104092 16 bit 566 Minimum Maximum H Brightness All the pixels that are still visible will define the pixels that will be i
12. foreground or background This is usually done using a black and white image Only the pixels that are not black will be used ImageJ provides tools to produce a mask from your original image This is done by analysing the image and setting a threshold value for pixels to be included in the foreground e MAGE gt DUPLICATE e IMAGE gt Apgust gt THREHOLD There are many different thresholding methods The Auto THRESHoLD plugin of Gabrial Landini contains several that you can try see http pacific mpi cbg de wiki index php Auto Threshold You can apply all the methods to see which is best for your image However in most cases the default ImageJ method or the Otsu method both provide robust and efficient partitioning of the data The following image shows the result of applying the Otsu method Y3067s1_ 04 R30 ane 0 x ei U F Y3067s1_04_R3D if Otsu Ws Redi 6 645 877 microns 10492 16 bit 566 Ws Otsu Red 10492 pixels 6 bit 26K To assist in the thresholding of multi dimensional images you can use the GDSC gt Stack THRESHOLD plugin This plugin can process a 5D hyperstack Each combination of channel frame are extracted for separate processing The threshold method is then applied to the individual channel Z stack for each time frame in the sequence The methods are the same as those available in the Auto THRESHoLD plugin The results are displayed as a new hyperstack Note In practice it is possible to use a
13. gy Stack Correlation Analyser sc Le x Stack Correlation Analyser Method Otsu Correlation uses union intersect of the masks Intersect Aggregate 2 stack Log thresholds Show mask Subtract threshold Ok Cancel Help 6 1 1 Input Parameters Parameter Description METHOD Specify the thresholding method to use to identify the channel signal foreground from the background Details of the thresholding methods can be found in section 2 1 Image Thresholding INTERSECT If checked then the intersect of the two channel masks will be used to define the pixels used in the analysis Otherwise the union will be used If the intersect is used then a Manders overlap coefficient is calculated for each channel AGGREGATE Z STACKS Combine the z stack of each channel frame combination into a single stack for analysis i e this will perform all vs all analysis between channels using the entire z stack If unchecked then all vs all analysis between channels is performed for each slice of the z stack LOG THRESHOLDS Record the thresholds to the ImageJ log window A threshold will be calculated for each combination of timeframe and channel and optionally z slice e g ticiz1 threshold 778 tic2z1 threshold 617 tic3z1 threshold 2441 SHOW MASK If selected then the mask image calculated using the thresholds will be shown e g Wa DetaultRed 10429 This allows the user to check that the mask
14. in the analysis region including zero zero pixels CuH1ictT VoL Co toc CH2cTT VoL VOLUME ABOVE THRESHOLD COLOCALISED The percentage of pixels above the channel threshold that are also above the threshold for the other channel Result Option Description CoLoc CH1ctTT VoL Cotocn Cotoc NCH1IcTT CH1 Int Cotoc CH2 Int CoLoc INTENSITY COLOCALISED The percentage of channel intensity that is colocalised Cu1 Int Cotocsum CH1 Cotoc sumCH1 CHI1cTT INT Cotoc CH2cTT INT CoLoc INTENSITY ABOVE THRESHOLD COLOCALISED The percentage of channel intensity that is above the channel threshold that is colocalised CHI1ctT Int Coloc sumMCH1_ Co toc suMCH1cTT 4 Confined Displacement Algorithm CDA Plugin The Confined Displacement Algorithm CDA ImageJ plugin allows the identification of significant areas of colocalisation within 2D and 3D images The quantification of colocalisation is performed in two stages Stage 1 identifies locations where the two channels both occur Stage 2 calculates a measure to quantify the cooccurrence of the two channels for example using the percentage of colocated signal or the correlation between the colocated channels However the quantification of colocalisation has no context This results in subjective interpretation as to the significance of the value For example a given colocalisation result may be ve
15. is selecting the correct regions for analysis SUBTRACT THRESHOLD Subtract the threshold value from each pixel before calculating the Manders coefficient The coefficient then represents the fraction of intensity above the background that overlaps with the other region 6 2 Results The Stack CorreLation ANALYSER Writes the results directly to the ImageJ log window 6 2 1 Header Record The first line records the threshold method used for the analysis and the name of the input image e g Stack correlation Default Y3067s1 04 R3D tif 6 2 2 Threshold Record If the Loc THRESHOLDs IS Selected the plugin records the thresholds for each extracted channel e g tic1zl1 threshold 777 tic2z1 threshold 616 tic3z1 threshold 2441 6 2 3 Correlation Record A single line is written for each channel correlation that is calculated Each line uses a comma separated format e g t1 c1z1 c2z1 1182 12 35 0 3120 0 9190 0 7818 t1 c1z1 c321 2692 28 14 0 2281 0 9164 0 7854 t1 c2z1 c3z21 1305 13 64 0 0233 0 1018 0 9006 The fields of the result entry are described in the following table Field Description 1 The timeframe of the image used for the analysis 2 The first channel and z slice of the image used for the analysis If AGGREGATE Z stacks was Selected then this will contain only the channel 3 The second channel and z slice of the image used for the analysis 4 The number of pixels used for
16. scores from displacements above 15 pixels used as the background significance was assessed using a p value of 0 01 A The mask regions of the red and green channel were used to define the presence of each channel The mask region of the blue channel was used to confine the analysis region The output image and mask shows the original pixels in red and green Any overlap is visible as yellow Regions outside the analysis area are white B The individual correlation coefficients scores The line shows the sample average using a bin width of 1 C The probability density of the correlation coefficient scores The measured correlation R 0 31 Is significant given the estimated probability density function C l lo4e92 pixels KOB 3 104892 pixels ROB 3 D e CDA R samples Radial displacement d pixels Save Copy A Sy CDA R POF List Save Copy FOF 0 2 0 0 0 2 0 4 0 6 0 8 1 0 F Mean 0 0293 F ps0 0lr 0 2201 0 2 782 k value is significant icolocalisech std Dew 0 1081 k id 0 0 3101 4 2 Features e Processes 8 bit and 16 bit greyscale images e Processes 2D and 3D images i e image stacks e Selection of channel regions and confined region using a mask image an image minimum display value or the ImageJ ROI e Optional expansion of confined region to include channel regions e Performs CDA shifts within a configurable radius e Calculates Manders coefficients and correlation coefficient
17. the analysis This is either the union or intersect of the two channel masks depending on the Intersect parameters 5 The percentage area used in the analysis i e the number of pixels used in the analysis divided by the total number of pixels in the channel 6 The Pearson correlation coefficient for the two channels T The Manders overlap coefficient for the first channel This is only shown if the intersect of the masks is used 8 The Manders overlap coefficient for the second channel This is only shown if the intersect of the masks is used 7 Stack Colocalisation Analyser Plugin The Confined Displacement Algorithm CDA can be used to determine the significance of the colocalisation measures between two channels The CDA Gefor ImageJ see section 4 Confined Displacement Algorithm CDA Plugin provides a feature rich interface for configurable analysis of images using the CDA algorithm This can be a time consuming task when processing many images The Stack CoLoca isation ANALYSER provides a simple implementation of the CDA algorithm for fast automated analysis of multi channel images The plugin requires an image with at least 2 channels The plugin performs the following steps e Selection of the 2 analysis channels e Thresholding to create a mask region for each channel frame aggregating the z stack e Optional thresholding of a third channel to define the analysis region e CDA analysis of channel 1 verses
18. was proposed by Costes et al 2004 Each image is thresholded at its maximum intensity and the correlation below this threshold is measured i e the correlation for the entire signal This will be positive if the two channels are collocated The threshold is then reduced and the colocalisation remeasured By iterating this process a search is performed for the intensity threshold below which there is no correlation The fraction of signal above this threshold is a measure of the colocalisation of the signal in the two image channels An exhaustive search for the thresholds is computationally expensive due to the large number of combinations The Colocalisation Thresholds plugin computes a regression between the two channels This regression is used to approximate the threshold level required in the second image for a set threshold in the first image The plugin performs a converging search for the image thresholds and reports various metrics on the resulting thresholded images 3 1 1 Example Input The following figure shows the input required for the plugin The input consists of 2 channels of a microscopy image where the channels are stained for different proteins De 6 645 867 microns OUAx 3 1 2 Example Output The results of colocalisation threshold analysis A Combined RGB image showing only pixels above the threshold levels The image shows the original channels in red and green the blue channel shows colocalised pixels
19. 3 Measurement of co localization of object in dual colour confocal images Journal of Microscopy 169 375 382 9 Appendix 1 Batch Processing ImageJ can automate processing large numbers of image files with a macro in a single batch process This can be used to run a plugin on multiple input files 9 1 Step 1 Record the macro Open a representative image to be used for the analysis Test the plugin or series of commands using different parameters and ensure the result is as desired Open the ImageJ macro recorder PLucins gt Macro gt REcorb Run the chosen commands on the image configuring the parameters as required ImageJ will record the commands to the Recorder window For example running the Stack CorRRELATION ANALYSER Plugin On an image will produce the following macro command run Stack Correlation Analyser method Default intersect aggregate Pressing the Create button to generate a new macro and save it for use later You can test the macro on a fresh image by using the Macros gt Run Macro command from the Macro window 9 2 Step 2 Prepare input files Place all the image files in a single directory The directory should not contain images that you do not want to process If the macro command modifies the images then you can also create an output directory that will be used to store the modified images 9 3 Step 3 Process Barco command Open the ImageJ Process BatcH window Process gt Barch gt Macro
20. B A plot of channel 1 verses channel 2 The regression line is plotted along with the threshold level for channel 1 vertical line and channel 2 horizontal line C Plot of the correlation above blue and below red points the threshold level for channel 1 verses the threshold level 104192 pixels RGB 37H M CT correlation 30 256x256 pixels 16 bit 128K E 0 S00 L000 1500 2000 2900 3000 Threshold List Save Copy X 3 2 Features e Processes 8 bit and 16 bit greyscale images e Processes 2D and 3D images i e image stacks e Selection of channel regions using the ImageJ ROI e Calculates threshold value for each image below which there is no correlation e Outputs threshold mask images e Generates a full results table e Efficient convergence algorithm is fast Note The plugin presented here is a modification of the original ColocThreshold plugin available from EE Threshold The plugin has been altered to add additional features such as support for 16 bit and 3D images region selection options more results output and the new implementation of the threshold search code 3 3 Method The colocalisation threshold plugin uses a heuristic search for the threshold limits for each channel below which there is no correlation The search is only performed if there is a positive correlation between all the pixels in both channels The method is outlined below 3 3 1 Step 1 Regression Analysis A regres
21. H TOLERANCE Of 0 05 and CoRrRELATION Limit Of O the search will stop if the correlation in within 0 05 to 0 05 CORRELATION LIMIT Define the desired correlation for the pixels below the image thresholds This can be increased from the default of O in an attempt to obtain thresholds that specifiy more strongly correlated channel pixels 3 4 3 Results Parameters The other parameters provide results for the plugin Parameter Description SHOW COLOCALISED PIXELS Specify the tolerance for convergence on the desired correlation for the pixels below the thresholds The search will stop if the correlation is the CorreELaTION Limit SEARCH TOLERANCE E g USING a SEARCH TOLERANCE Of 0 05 and CoRrRELATION Limit Of O the search will stop if the correlation in within 0 05 to 0 05 USE CONSTANT INTENSITY FOR COLOCALISED PIXELS Show the blue channel colocalised pixels of the output RGB image using a constant intensity 255 SHOW SCATTER PLOT Show a scatter plot of the channel 1 intensity verses the channel 2 intensity INCLUDE ZERO ZERO PIXELS IN THE THRESHOLD CALCULATION Include pixels with zero value in both channels in the correlation calculations Ignoring these pixels is useful for images that have been masked so that certain regions have no value CLOSE WINDOWS ON EXIT If checked then all the windows that have been opened by the plugin are closed when the plugin windo
22. ImageJ Colocalisation Plugins Alex Herbert MRC Genome Damage and Stability Centre School of Life Sciences University of Sussex Science Road Falmer BN1 9RQ a herbert sussex ac uk Table of Contents TF AIO GUI CUO D 3 2 SICK TACS NO MIO NN jase esta tcocnaceaceeacannaduacintnsdeeusensannarsateendeqn aces EEEE E E E 4 Zee MACS TIS SIO HEN D 4 GC PCM NOVA E 5 Ge lee ug 5 3 COlOCAIISAMOM Thresnold PCO UIN E 6 CN BN b Example Oo E 6 CN Bi Singel Ce re EE 7 SPA UN Co EE 8 CCAS Lee EE 8 E DN SIED 12 REGIESSION E VE 8 8 9 2 DIED 2 RTE ln CC 8 3 3 3 Step 3 Iterative search 8 Oe PUIG an ie 9 3 4 1 Input Harammetere uk 10 E SO NCI RN GE EE 10 AS RESE Pll QIN OL E 11 ig PRO SUNS VIO E 13 4 Confined Displacement Algorithm CDA PDlugm 17 d Fee teg le MPU E 18 e EA EXMP OUUU oe hc en rene ee ee ee ee Era eee eer 19 Pred EE 20 NS CUNO creer cere E E E dicen edatnndasesodssncaaneheeatcanatandatasarnesadcenndtcatengeeesien 20 4 3 1 Stage 1 Identify Channel Ggnal 20 4 3 1 1 Using the ImageJ minimum display value 20 4 3 1 2 Using the ImageJ ROL 22 i Moe AY SIG NN ACS TN SN sarct as cain seceizsanesnce E EEE S 23 A232 Stage E 24 e Mandas COSCON EE 24 4 3 2 2 Pearson Correlation Coefficient 25 4 3 2 3 DISPIACEMENNS uk 25 4 3 2 4 Speed INCICASCS EEN 27 e BR te eh Te Et e EE 27 4A 1 laien 2 4 4 2 Displacement ParamMetelss ccccccccceccceseeceeeccseeecseeceaeeceueessseesaeeeseeeseesaeesaees 28
23. ack Threshold Processes an image stack and applies thresholding to create a mask for each channel frame combination This defines the Signal region of each channel Colocalisation Compares two images for correlated pixel intensities If the two Threshold images are correlated a search is performed to identify the threshold below which the two images are not correlated CDA Uses the Confined Displacement Algorithm CDA to compute the significance of colocalisation metrics Manders coefficient and correlation coefficient Stack Correlation Processes a stack image with multiple channels Extracts all the Analyser channels and frames and performs 1 Thresholding to create a mask for each channel frame 2 All vs all channel correlation within the union intersect of the channel masks Stack Colocalisation Processes a stack image with multiple channels Requires three Analyser channels Each frame is processed separately Extracts all the channels collating z stacks and performs 1 Thresholding to create a mask for each channel 2 CDA analysis of channel 1 vs channel 2 within the region defined by channel 3 The stack analyser plugins can be incorporated into ImageJ macros to allow rapid analysis of hundreds of images for correlations 2 Stack Threshold Plugin 2 1 Image Thresholding An initial stage of image analysis is to determine which pixels contain the important image data All the other pixels can th
24. age mask Foreground pixels are white background pixels are black An example of an input and output image are shown below Y3067s1 04 R3 Schi E al GE 7 i Wa Redi 6 645 087 microns 10492 16 bit 56K 13 Otsu Red 10492 pixels 6 bit 26K 2 2 Plugin Interface The Stack THRESHOLD plugin uses the standard ImageJ plugin dialogue Select the image to process and then run the plugin This will present the following window TT Stack Thresho stack Threshold Method Otsu Log thresholds Cancel 2 2 1 Input Parameters Parameter Description METHOD Specify the thresholding method LOG THRESHOLDS Record the thresholds to the ImageJ log window A threshold will be calculated for each combination of timeframe and channel e g tici threshold 778 tic2 threshold 617 tic3 threshold 2441 3 Colocalisation Threshold Plugin The positive correlation between two images indicates that the signal in one channel is observed at the same time as the signal in the other channel This may have biological significance This correlation is expected to be high when the strength of the signal is both channels is high However as the strength of the signal reduces towards background noise it would be expected that the strength of the correlation also reduces Under this assumption it is possible to measure the amount of signal that is correlated A method for determining the threshold level for each channel
25. alue Description NCH1 The number of pixels in channel 1 NCH1cT0O The number of pixels in channel 1 greater than zero Muller The number of pixels in channel 1 greater than the channel 1 threshold NCoLoc The number of pixels where both channels are above the threshold SUMCHL The sum of pixel values in channel 1 suMCH1cTT The sum of pixel values in channel 1 greater than the channel 1 threshold SUMCH1_ CH2cT0 The sum of pixel values in channel 1 where channel 2 is greater than zero SUMCHL_ CH2cTT The sum of pixel values in channel 1 where channel 2 is greater than the channel 2 threshold suMCH1_CoLoc The sum of pixel values in channel 1 where both channels are above the threshold Note With the exception of nCo oc all of these values have corresponding equivalents for Channel 2 The initial result values are used to compute result metrics that are displayed in the result table The following table describes the results and the option used to enable disable the result column s Result Option Description IMAGES Contains the titles of the input images used for the analysis ROI Contains the ROI that was used to select the region for the analysis ZEROZERO Shows whether INCLUDE ZERO ZERO PIXELS IN THE THRESHOLD CALCULATION Was selected RTOTAL PEARSON S FOR The Pearson s correlation for the entire image WHOLE IMAGE M B SHOW LINEAR The linear regression coefficients
26. channel 2 using the mask regions e Analysis using different thresholding methods can be performed in a batch operation During the CDA analysis the total number of displacements is limited to increase speed This is performed by computing a random subset of all possible displacements The subset size can be configured but should be large enough that the sample is representative of the entire set e g 20 The plugin can be used within the ImageJ scripting tools to rapidly test a large number of images for colocated channels See Appendix 1 Batch Processing This allows the user to identify the best candidate images for further analysis 7 1 Plugin Interface The Stack CorreLation ANALYSER plugin uses the standard ImageJ plugin dialogue Select the image to process and then run the plugin This will present the following window eS ee le en eee GA 4 Lk P K MIHK ASL Stack Colocalisation Analyser Channell 1 Channel 2 2 Channel 3 3 Method Otsu Log thresholds Log results Show mask sw Subtract threshold Permutations LOO Minimum shift 9 Maximum shift 16 Significance 0 050 BRUN Ok Cancel Help 7 1 1 Input Parameters Parameter Description CHANNEL 1 2 Select the two channels for CDA analysis CHANNEL 3 Optionally select the channel used to define the confinement region for CDA analysis METHOD Specify the thresholding method to use to identify the channel signal foreground from the background
27. e correlation of 0 31 is Significant I CDA R PDF TO 0 4 0 6 R Mean 0 0293 std Dev 0 1081 R p 0 05y 0 1844 0 1898 R d 0 0 3101 R Value is significant colocalised 4 3 2 4 Speed Increases When using a large radius there may be many thousands of possible displacements It is possible to compute a random subset of these displacements to increase speed The number of samples can be configured in the plugin options using the Permutations parameter However enough samples must be computed to produce an estimate of the distribution The plugin uses multi threaded code to compute the permutations The number of threads can be configured using the ImageJ options selected from the menu item Enit gt Options gt Memory amp THREADS By default this will be the number of processor cores available on your computer 4 4 Plugin Interface The CDA interface uses a frame within the ImageJ application The plugin has many options but these can be divided into sections controlling parts of the algorithm The different sections are shown in the following image and are described below P CDA Plugin Channel firame options for stacks pop up at run time In put Y306 s1_04 RSD 4fOtsu Ya06 s1_04 RSD 1rOtsu Y306 s1_04 RSD 1fOtsu Include ROIs Maximum radial displacement 25 Random radial displacement 15 Compute sub random samples Diplacement Approx number of samples 1963 Bins for histogram 16 Close windows on exit
28. e distance between Tmax and Tmn IS 1 i e convergence e The Maximum Iterations value is reached In certain cases the final threshold limit may not have the desired correlation This is because convergence or Maximum Iterations OCCUrs during a search where the CorreELaTION LIMIT iS not reached In this case the results are sorted in descending order by the threshold The final limit is set as the threshold value which resulted in e The correlation above the threshold being more than the CorrRELATION LIMIT e The correlation below the threshold is the closest to the target CorRELATION LIMIT In the event that no results satisfy the criteria then the threshold is set using the lowest intensity level sampled from channel 1 The following chart shows how the threshold is updated during a typical search Initially the threshold is reduced until the correlation goes below the Corre ation Limit The threshold is then raised and subsequently raised lowered as the correlation alternates around the CorRRELATION Limit In this instance the Corre ation Limit was not obtained and the threshold converged at 166 500 450 400 350 300 250 200 150 100 50 0 L 2 3 4 D D f g g 10 Threshold Iteration 3 4 Plugin Interface The Colocalisation Threshold plugin interface uses a frame within the ImageJ application The plugin has many options but these can be divided into sections controlling parts of the algorithm The different sections are shown in th
29. e following image and are described below Stax Channel 1 Channel C1 Y306 s1_04 R3D 1tif C2 Y3067s1_04_R3D tif Input Use ROI None Search tolerance 00 Correlation limit loo Show colocaliged pixels Search Use constant intensity for colocalised pixels show Scatter plot Results Include zero zero pixels in threshold calculation Close windows on exit Set results options 3 4 1 Input Parameters The input parameters specify the images to use for the analysis The user can select the two input channels and specify the region to use for the analysis Parameter Description CHANNEL 1 2 Specify the channel 1 or 2 image If the user selects an image stack then the channel and frame will be requested during run time This is to eliminate optional fields from the main dialogue Use ROI Specify the region to use for analysis This uses an Region of Interest ROI that has been drawn on the input image using the ImageJ ROI tools The options are e None s Image 1 ROI e Image 2 ROI 3 4 2 Search Parameters The search parameters control the algorithm used to find the colocalisation thresholds Parameter Description SEARCH TOLERANCE Specify the tolerance for convergence on the desired correlation for the pixels below the thresholds The search will stop if the correlation is the CorreELaTION Limit SEARCH TOLERANCE E g using a SEARC
30. en be ignored resulting in faster processing of the image A simple way to separate the important pixels is to assign a threshold level to the image All pixels below this level are ignored as the background pixels above the level are the foreground It is possible to automatically set a threshold level This is done by analysing the image distribution of pixel intensities image histogram and setting a threshold value that separates a background cluster from the foreground pixels The following image outlines the two peaks that are characteristic of a well defined background and foreground Background Foreground 304 3611 Count 9568 Min 304 Mean 622 799 Max 3611 StdDev 401 200 Mode 345 1064 Bins 256 Bin Width 12 918 There are many different thresholding methods ImageJ also has a built in threshold method available using the command waer gt Apsust gt THREHOLD The Auto THRESHOLD plugin of Gabrial Landini contains several different methods that you can try see http acific mpi cbg de wiki index php Auto Threshold However the threshold methods require the analysis of the image histogram for the channel that you are thresholding ImageJ can perform thresholding on a single image ora 3d image stack It has no method for processing a multi channel image The Stack THRESHOLD plugin can analyse a multi dimensional image and apply thresholding to each channel and time frame in the image The result is presented as a new im
31. localisation threshold Parameter Description are white other pixels are black e g EJ em CT Pixels 1 n j CES 1934205 pixels 8 bit 39K EXHAUSTIVE SEARCH If selected the convergence algorithm is not used Instead all threshold values between the maximum and minimum for channel 1 are analysed using Max iterations to define the step increments The final threshold is set as the highest threshold with a positive correlation for pixels above the threshold and the closest correlation to the Corre ation timit for the pixels below the threshold PLot R VALUES Produce a plot of a the correlation R values above solid line and b below cross points the threshold verses the threshold e g ei e R values w x 600 700 SOL 900 1000 1100 Threshold Save COBY A Max ITERATIONS Specify the maximum iterations to use within the algorithm 3 5 Results Table The results table contains details about the threshold analysis The analysis is performed on pixels that have been classified using different criteria e pixel is greater than a set level for one channel e pixel is greater than the set levels for both channels In each case the level can be set using the a threshold of O i e count any pixel 1 i e the pixel has a value or the threshold set during the analysis phase This allows the following values to be computed Result V
32. ncluded in the analysis Note Adjusting the image display range will not alter the underlying channel intensity data that will be used to perform the analysis 4 3 1 2 Using the ImageJ ROI ImageJ contains tools for drawing regions of interest ROIs These can be based on Shapes ReEcTANGLE Ovar Or PoLyGon or FREEHAND Each tool can be selected from the ImageJ toolbar using one of the first 4 toolbar buttons goja v The RectancLe and Ovar tools are used by simply dragging on the image to define the shape This can be moved by dragging inside the shape and resized by dragging the small white markers at the shape edge The Potycon tool is used by clicking on points in the image that you want to be a polygon apex The polygon is completed by clicking in the small square that defined the start point The polygon can be moved and resized as above The FREEHAND tool is used by clicking to define a start point and then dragging the mouse to trace a shape Releasing the mouse results in ImageJ completing the shape The shape can be moved but not resized The following example shows an image where the FREEHAND ROI tool has been used to define a region Note that since the ROI must be contiguous using this method is not recommended for defining regions with two or more distinct segments of channel intensity 4 3 1 3 Using an image mask A mask is an image of the same dimensions as the original image but with pixel values set as either
33. ndard deviation for M1 M1 us The upper and lower limits for the significance assessment M1 RESULT Contains the significance results Can be one of three values Not SIGNIFICANT SIGNIFICANT COLOCATED ANd SIGNIFICANT NOT COLOCATED The same results are shown for the Manders coefficient for channel 2 M2 and for the Pearson correlation coefficient R 5 CDA macro Plugin Test for significant colocalisation within images using the Confined Displacement Algorithm CDA The CDA macro plugin performs the same analysis as the CDA plugin However it uses the standard ImaceJ plugin dialog to get the parameters This allows it to be recorded by the ImaceJ Macro Recorder and supported in ImaceJ macros The plugin interface is shown below Wi e CDA Plugin vi Lei eg Channel l FluorescentCells tif Channel 2 FluorescentCalls tif Dol fer channell None ROlfor channel l image Channel image RGI for channel 2 None ROI for channel 2 image Channel image Confined compartment Use Rol Confined compartment image FluorescentCells tif Include ROIs Maximum radial displacement 30 Random radial displacement 2 TI Compute sub randam samples Bins for histogram 16 Set results options OK Cancel The options and results are identical to the CDA plugin For further details see section 4 Confined Displacement Algorithm CDA Plugin 6 Stack Correlation Analyser Plugin The analysis of a single image for
34. ny image as a mask All non zero pixels will define the region to include in the analysis 4 3 2 Stage 2 Analysis The Confined Displacement Algorithm CDA computes measures of colocalisation for the selected pixels in two channels CH1 and CH2 The regions that define the channels are Known as ROI1 and ROI2 respectively This analysis is performed within a defined area the confined compartment The colocalisation measures are the Manders coefficient and the Pearson correlation coefficient The metrics are computed for all possible translations displacements of one of the images within a set radius The distribution of the scores can be used to determine if the score of the original image is significant 4 3 2 1 Manders Coefficient The Manders coefficient provides a measure of how much of the signal intensity of a channel occurs in the same location as the other channel The overlap between ROI1 and HOLZ defines the region used to calculate the Manders coefficient Manders et al 1993 The coefficient is calculated for each channel as follows M1 Sum of CH1 intensity in overlap region Sum of CH1 intensity in ROIL M2 Sum of CH2 intensity in overlap region Sum of CH2 intensity in ROI2 The values range from O to 1 The Manders coefficient can be interpreted as the fraction of signal that is colocated It does not provide a measure of whether there is a dependency between the strength of the two channel signals 4 3 2 2 Pearson C
35. o the other Increasing the maximum displacement will increase the number of calculations required Parameter Description MAXIMUM RADIAL DISPLACEMENT The maximum displacement distance RANDOM RADIAL DISPLACEMENT The distance that defines the random sample distribution The probability distribution will be calculated using all sample above this level COMPUTE SUB RANDOM SAMPLES All the samples below the Ranpom RaDIAL DISPLACEMENT are not required for the analysis However they are useful for determining the displacement radius since they are plotted in the samples window Use this option if you have configured the Ranpom RADIAL DISPLACEMENT and are re running the analysis to produce different result outputs This will decrease calculation time APPROX NUMBER OF SAMPLES The label shows an estimate of the number of displacements that are required for the analysis It is possible to reduce the number of samples by using the Permutations parameter in the Set Resutts Options dialog see below BINS FOR HISTOGRAM The probability distribution is calculated by binning data to produce a histogram This parameter sets the number of bins to use to cover the range If there are a large number of samples this value can be increased 4 4 3 Options Parameters The other parameters provide options for the CDA plugin If CLosE winpows on exit IS Checked then all the windows that have
36. ons but with one channel shifted at the maximum displacement SHow M1 M2 R por Specify whether to show a plot of the samples for the M1 M2 R metrics SHow M1 M2 R STATISTICS Specify whether to show a probability distribution for the M1 M2 R metrics SAVE RESULTS If selected this will cause the results to be saved to a folder in the Results pirectory The folder will be named coaYYYYMMDD_HHwmss where the date section will be set using the current time The plugin will save the Mercep CHANNEL and MerceD ROI as ImageJ TIFF images and the results that are recorded in the result table in a text file called Resu_ts txt These items allow a user to quickly see the channel data used in the analysis and the results This is useful for quickly scanning a history of CDA analysis All the other results can be recomputed using the plugin if necessary RESULT DIRECTORY Specify the root directory for the results If it does not exist then the plugin will not save any results If an invalid directory is entered the plugin will alert the user with a warning P VALUE Set the p value used to define the limits for the assessment of significance If the score from the unshifted image lies outside of the probability values defined using this p value then the result is labelled as significant PERMUTATIONS The number of displacements to compute If set lower than the total number possible then a subset will be randomly
37. op The threshold method used to generate the mask image FRAME The image frame used in the analysis CH1 2 The two channels used for CDA analysis CH3 The channel used to define the confined region If no region was selected this is set to None N The number of pixels in the overlap between the channel 1 and 2 mask regions AREA The percentage of pixels within the confined region used for Result Description analysis that are inside the overlap between the channel 1 and 2 mask regions M1 2 The thresholded Manders coefficient for channel 1 2 The Manders coefficient is calculated as the fraction of intensity in the channel where the other channel is above the threshold e g M1 sumCH1 Cu2crtT sumCH1 How much of channel 1 foreground intensity occurs where channel 2 is foreground SIG The significance of the Manders coefficient true false The Pearson correlation coefficient calculated using only the pixels from the overlap between the channel 1 and 2 mask regions SIG The significance of the Pearson correlation coefficient true false 8 References Costes et al 2004 Automatic and quantitative measurement of protein protein colocalization in live cells Biophysical Journal 86 3993 4003 Ramirez et al 2010 Confined Displacement Algorithm Determines True and Random Colocalization Journal of Microscopy 239 173 183 E M M Manders F J Verbeek and J A Aten 199
38. orrelation Coefficient The Pearson correlation coefficient R provides a measure of dependence between two quantities in this case the two image channels It is calculated as The value ranges from 1 in the case of a perfect positive increasing linear relationship correlation to 1 in the case of a perfect decreasing negative linear relationship anticorrelation As it approaches zero there is less of a relationship closer to uncorrelated The closer the coefficient is to either 1 or 1 the stronger the correlation between the variables In the CDA plugin the Pearson correlation coefficient is calculated using all the pixels within the confined region and not just those pixels within the overlap of the ROIs This allows it to be used when no ROls have been defined Note that since the denominator of the calculation is independent of matched pairs of x and y this value can be pre calculated This allows the CDA plugin to rapidly calculate R for each shift of the image channels 4 3 2 3 Displacements The CDA method involves shifting one image relative to the other and computing the colocalisation metrics All possible displacements within a radius are computed The user must select the maximum radius to use for the displacements The plugin provides charts to help in this process Once all the displacements have been performed the resulting distribution of metrics can then be plotted verses the distance In the case where the t
39. ry good or very bad depending on the underlying distribution of signal intensity within the image In this case the results of many different images can be compared to provide a context for the result under investigation However this analysis is also subjective and it is possible that alternative images are either not available or not representative To solve this issue representative images for comparison can be produced from the Original image This ensures the total channel intensity is the same Randomly shuffling the pixels provides new images but does not maintain a realistic grouping of pixel intensities characteristic of structural content Rotating the image in 90 degree intervals or translating the image with wrapping does maintain the structural content but introduces bias depending on the nature of the transform e g the centre of the rotation or the angle of the translation A solution to this situation is to perform translations in all directions and at all possible distances If the colocalisation measure is calculated for each translation then a probability density function can be computed that shows how the measure varies with distance If the two channels are significantly colocated it would be expected that the original colocalisation measure would be very different from the colocalisation measure of a highly shifted image If there is no true colocalisation then shifting the image will produce colocalisation scores higher and lower
40. sion is computed between the two channels This assumes a linear relationship between the two channels The value of channel 2 can then be expression as ch2 chl m c 3 3 2 Step 2 Define limits The limits for the threshold search are set using the maximum and minimum value of channel 1 These are named Tmax ANd Tmn Using the linear regression the limits for channel 2 are then defined using ch2min Tmin M C ch2max Tmax M C 3 3 3 Step 3 Iterative search The search is performed by continuously updating the current threshold for channel 1 calculating the threshold for channel 2 and the subsequent regression value for all pixels below the thresholds The search is convergent in that it only operates between the current maximum and minimum limit for channel 1 The limits are adjusted based on the results of each iteration and the current threshold is updated to half way between the two limits 1 At any point in the algorithm it is known that there is a positive correlation when T Tmax T is then set as Tmin Tmax Tmin 2 Le halfway between the two limits 2 Calculate the new regression value If the correlation is positive then it is known the threshold must be lowered T max IS updated to T 4 Ifthe correlation is negative then it is Known the threshold must be raised Tmin is updated to T 5 Repeat The iteration stops when either e The correlation is within the Search TOLERANCE Of the CorRELATION LIMIT e Th
41. w is closed This includes the result table and output images and charts SET RESULTS OPTIONS Shows a pop up dialogue with additional options see below Clicking the Set prsur options Checkbox shows the following pop up dialogue wW Show thresholds W Pearson s for whole image Iw Pearson s for image above thresholds Iw Pearson s for image below thresholds should be 0 W Manders original coefficients threshold 0 Iw Manders using thresholds Iw Number of colocalised voxels Iw Volume colocalised Iw Volume above threshold colocalised Iw Intensity colocalised Iw Intensity above threshold colocalised Iw Output ROls Masks Exhaustive search Iw Plot R values Max iterations 50 OK Cancel The dialogue contains settings for configuring the columns included in the result table Further details can be found in the Results Table section This can be useful if you have limited screen resolution and would like to disable some columns The dialogue also contains the following options Parameter Description Output ROIs Masks If an ROI was selected from an input image then this option will output a cropped duplicate of the input images with all the pixels outside the ROI if non rectangular set to zero e g ey kend CT Channel 1 gy wey 1934205 pixels 16 bit 77K In all cases this option will also show a mask image for each channel where all the pixels above the co
42. wo channels contain colocated Signal the distribution will show a fall off with increasing distance as the metrics approach a random background This can be seen in the following chart of the R samples of a weakly correlated image is CDA R samples w X Radial displacement d pixels Save COBY A The fall off will eventually plateau at a level that corresponds to random images with the same underlying structure The plateau should be defined where the mean of the samples is approximately constant To assist in setting this level the plot shows the mean of the samples as a continuous line The mean is calculated by rounding the displacement distance to the nearest whole number and averaging all the samples for each distance The point at which the random plateau begins should be used to define the distribution for calculating the significance This level will vary between images and so requires the user to configure the displacement radius For example the same image is shown below with the region from 15 to 25 pixels used to define the random or background distribution Hi CDA R samples Random The following plot shows the probability distribution of the samples taken from the 15 to 25 pixel displacement distance The 95 confidence interval is shown as the black lines marked by triangles It can be clearly seen that the original metric score lies outside the range expected for a random pair of images In this case th
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