OBCOL. (Organelle Based CO-Localisation) Users Guide

Transcription

1 OBCOL (Organelle Based CO-Localisation) Users Guide

2 INTRODUCTION OBCOL is an ImageJ plugin designed to autonomously detect objects within an image (or image stack) and analyse them separately as individual objects. It is capable of determining object dimension, colour composition and overlap as well producing a variety of colocalisation statistics.

3 USING OBCOL 1. Image Preparation Format OBCOL works with any file format supported by ImageJ. Removal of Unwanted Content OBCOL analyses all visible data within an image. Any data not to be analysed should be removed. For example: - If there are other cells within the image these should be cropped out. - If only interested in internal objects, fluorescence at the plasma membrane should be cropped out. Filtering Background fluorescence and noise may effect OBCOL s ability to correctly detect the edges of objects within an image. If this is the case images should be filtered prior to analysis. For example 1 pixel Median filter has been applied to this image. Intensity Cutoff To remove background fluorescence and to ensure OBCOL only examines objects of interest the range of channel intensity should be set. By setting an intensity threshold any object/noise/artefact with an intensity below that threshold will be excluded from analysis.

4 To do this: 1. Split the image into separate channels (IMAGE>COLOR>RGB SPLIT). RED GREEN BLUE 2. Select the RED channel and use the THRESHOLD tool to determine the intensity range you are interested in analysing. (IMAGE>ADJUST>THRESHOLD ). Any signal covered by the red mask is above the minimum threshold (61 in this example). For 3D image stacks it is a good idea to examine all slices to ensure the mask is covering all objects of interest and removing all unwanted objects. 3. Click the Reset button in THRESHOLD and close this window. With the RED channel still open apply the threshold in OBCOL (PLUGINS> COLOCALISATION PIPELINE>CUTOFF ). OBCOL will the remove all fluorescence below this intensity.

5 4. Repeat the above steps each channel in the image (OBCOL can process up to 255 channels). 2. Running OBCOL Adding Image Channels to OBCOL After setting the intensity cutoff for each channel have all the image channels open in ImageJ. Make sure they are saved to the hard drive after filtering, intensity cutoffs etc. To add an image to OBCOL analysis open the OBCOL menu (PLUGINS>COLOCALISATION PIPELINE>PIPELINE) and click Add all. All the channels should appear in the dialogue box. Additional channels can be opened and then added separately using the Add single button. One, two, three or more images may be added. Note that the order of the images selected is important. A number of the statistics reported by OBCOL later in the pipeline refer to channels by number, not by name. Therefore adding images in the red, green, blue order will make things easier in the long run. Once all the channels have been added click Finished Selection. Start Pipeline. Analysis options Upon starting the pipeline a window will open (shown below) providing several options to adjust the analysis process.

6 - Watershed tolerance/threshold o Adjusts the watershed algorithm used to separate adjacent objects. Based on the ImageJ plugin Find Maxima see ImageJ documentation for more details. o Briefly, for a local maximum of intensity I, the contiguous region with intensities in the range I-tolerance to I will be selected. A minimum threshold for maxima may also be set here. o Default settings are: Tolerance = 5, Threshold =1. - Local Threshold o Gives the option of using ImageJ s automatic local thresholding (defaults to on). - Pixel Overlap options o These parameters dictate the number of pixels required to be overlapping before an object in another channel are considered joined. o Default to 1 pixel. - Slices in time point. o The image stack may represent multiple time points. Enter the number of slices in a time point, or 0 if the stack is a single time point. Once parameters have been set click OK OBCOL s processing time will vary depending upon image size. A stack of a single cell take on average 5-10 minutes, whilst a single frame will take only a few seconds. 3. OBCOL analysis The Object Viewer Menu Once OBCOL has processed the image data the Coalesced Object Viewer Menu will appear. From this menu you can select several image viewing options as well as the generation of statistical tables.

7 - Image Viewing Options o Before generating an output image from analysis OBCOL will ask wether you wish to apply a min pixel filter. o Selecting this option will allow you to exclude objects under a certain number of pixels (removing artefact objects or objects deemed to small for analysis) or over a certain number of pixels. o If you have not already decided on a minimum object size try various cut off using the visualisation options below. Once you have found parameters most representative for the objects you are interested use them for the statistical analysis options. o View All Objects Coloured by Object This option will generate an image/image stack with each object coloured according to its number. Note: If there are more objects present in the image than identification colours available some separate objects will share the same colour. o View All Objects Coloured by Stack This option will generate an image/image stack using the original channel colours (e.g. Red/Green/Blue). This view does not use intensity gradients, colour is either present (256) or absent (0). This allows an easier observation of channel overlap.

8 o View All Objects as Mask This option will generate an image/image stack using the original channel colours in addition to the intensity gradients present in the original input image. To generate this image it is necessary to open the original channels used for OBCOL analysis. A menu box will open to allow you to select the correct images file for each channel. - Viewing individual objects o An important and extremely useful aspect of OBCOL is the ability to select and examine an individual object at any time. o Following the generation of an image via the options above you may click on an object within that image and a new image window will be generated containing information relating only to that object in the coloured by stack viewing format. o A table of statistics for that object will also be generated. o This allows you to visually identify an object in an image and determine its OBJECT ID. This is useful if you wish to analyse a few specific objects. OR to identify and remove specific objects from the data output later. - Mask stats o This option will generate colocalisation statistics for each individual object based on the intensity values found in the original data. As with the View All Objects as Mask option it will be necessary to use the original channels used for OBCOL analysis. Open the pre-obcol analysis image (and split it into separate channels). Select the appropriate image files for each channel in the dialogue window that follows. The option for a min pixel filter to be applied is given. Parameters most representative of the real objects you wish to analyse should be chosen (if not already decided, use the visualisation options mentioned prior to determine these) o Once Min Pixel Filter has been applied OBCOL will generate a table of statistics through ImageJ (this can be selected, copied and pasted into excel) o Values in the table: Blank column ImageJ s reference number for each line in the table

9 ID The identification number assigned by OBCOL to an object in the image Intensity Average (whole object) 1/2/3/etc The average intensity of the channel across the whole object (i.e. including regions of the object where this channel is absent) Numbers denote channel number according to how they were added into OBCOL (typically 1 RED, 2 GREEN, 3 BLUE) Intensity Average (only structures in this Channel) 1/2/3/etc The average intensity of the channel within the object, ignoring regions where its value is zero. Pearson s Coefficient r (1&2/2&3/etc) The Pearson s correlation between the two channels mentioned within the object Calculated using the JACOP colocalisation plugin for ImageJ Other statistics Including Manders coefficients, cytofluorogram and Li s intensity correlation coefficient. Please refer to the JaCoP paper for full details on these statistics S. Bolte & F. P. Cordelières, A guided tour into subcellular colocalization analysis in light microscopy, Journal of Microscopy, Volume 224, Issue 3: View All Object Statistics o This option will generate statistics on the size and channel composition for each individual object. o Once Min Pixel Filter has been applied OBCOL will generate a table of statistics through ImageJ (this can be selected, copied and pasted into excel) o Values in the table: Blank column ImageJ s reference number for each line in the table Object ID The identification number assigned by OBCOL to an object in the image ROIs in the stack 1/2/3 etc The number of regions of this channel found in the object. Pixels in stack(s) 1/2/1&2/3/etc The number of pixels of this channel within the object In the case of a RGB image 7 columns will be created o 1, 2, 1&2, 3, 1&3, 2&3, 1&2&3 (R, G, RG, B, RB, GB, RGB) o For example: Column 1 = number of red pixels Column 1&2 = number of pixels containing both R and G.

10 Num Pixels The total number of pixels in the object Size of Bounding Box 1/2/3 The dimensions (in pixels) of a 3 dimensional box that would contain the object. 1 (x-axis), 2 (y-axis), 3 (z-axis) Middle of Bounding Box Axis 1/2/3 The location of the object within the image (x,y,z). Note that these statistics correspond to the numbers in the master ImageJ window, not the image window, as shown in the figure below. This means that if an object is in the first slice only, then the Middle of Bounding Box Axis 3 will be 0. Final Note: OBCOL may readily utilise segmentations produced by other software if a mask image has been created by that software for each channel. The OBCOL pipeline may then be run on these mask images to create the objects. Overlap statistics, positional information and so forth may then be calculated. By opening the original images, colocalisation statistics may then be calculated for the source images in the objects found in the mask images.