CellProfiler Pipeline: http://www.cellprofiler.org Version:3 DateRevision:20140124151645 GitHash:0c7fb94 ModuleCount:8 HasImagePlaneDetails:False LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'General notes\x3A\', \'\', "3_channels_pipeline.cppipe contains the instructions for Cell Profiler to perform all of the image dissection steps from the workflow. These break down into 8 \'modules\', which are performed strictly in the descending order shown in the box to the left. Clicking on the title for each module in the list yields its settings page. The \'Module notes\' window above each of these pages contains our unofficial tips on how to modify the settings for each module.", \'\', "The \'?\' buttons for each parameter in the analysis are also an indispensable reference on their functions beyond our notes shown here.", \'\', \'Many features implemented in this pipeline are from version 2.0 of the software, the official manual for which is available at\x3A\', \'http\x3A//www.cellprofiler.org/linked_files/Documentation/cp2_manual_9978.pdf\', \'\', \'The settings are optimized for the example dataset included with the manuscript. We encourage experimentation to accommodate different sets of fluorescent microscope data. It is recommended to keep an unmodified copy of the pipeline file for reference.\', \'\', \'Notes specific to this module\x3A\', \'\', \'The LoadImages module applies a set of rules to manage the images intended for analysis.\', \'\', "To satisfy the current settings provided in this pipeline, image filenames need to use the \'ExperimentName_Well_FieldChannel.tif\' filename convention described in the protocol. This allows the Order setting for the \'Filename selection method\' to work since the files will group alphanumerically into experimentally relevant sets.", \'\', "The pipeline is set for managing data from 3 channels. Therefore \'Number of images in each group\' is set to \'3\'.", \'\', "Sets of related channel images (i.e. same well, same frame number) will alphabetically sort into the order Blue, Green and finally Red. This order is reflected in the numbers entered into the corresponding \'Position of this image in each group\' boxes.", \'\', "Arbitrary names \'Hoechst\', \'GFP\' and \'S780\' are applied to the channels at this stage for reference by later modules in this analysis.", \'\', \'These names can be changed to suit other studies, but later modules in the pipeline with dependencies on the subjects of those names used will need to be updated accordingly.\', \'\', \'Errors created by changing names that have consequences for later steps will be indicated by error symbols in the module list to the left.\', \'\', \'In order to ensure that individual cell data produced by the analysis will relate back to the correct images/wells/conditions, the relevant metadata is extracted from the corresponding filename for each channel image. This is performed with the following Regular Expression patterns tailored for each channel\x3A\', \'\', \'^.*(?P\\\\w?\x5BA-H\x5D)(?P\\\\d{1,2})_(?P\\\\d{1,2})(?P.*).tif\', \'\', \'^.*(?P\\\\w?\x5BA-H\x5D)(?P\\\\d{1,2})_(?P\\\\d{1,2})(?P.*).tif\', \'\', \'^.*(?P\\\\w?\x5BA-H\x5D)(?P\\\\d{1,2})_(?P\\\\d{1,2})(?P.*).tif\', \'\', \'The regular expressions describe the filename format and are therefore almost identical. The three regular expressions used here vary only in that Cell Profiler requires the channel metadata label to be individualised per channel\x3A e.g. ...(?P.*).tif or (?P.*).tif\', \'\', "The LoadImages module therefore imposes a lot of the structure for the whole analysis. To modify the pipeline for studies with more or fewer channels it will be necessary to use the buttons \'Add another image\' or \'Remove this image\', respectively. These are located at the bottom of the module. Be sure to adjust the image position number and number of files per group to accommodate the changes. If adding channels, new arbitrary channel names and corresponding individualised regular expression patterns will also need to be added (refer to the notes above).", \'\', "NB\x3A The \'Input\' folder containing the images for study is set using the \'View output settings\' button."\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True] File type to be loaded:individual images File selection method:Order Number of images in each group?:3 Type the text that the excluded images have in common:Do not use Analyze all subfolders within the selected folder?:None Input image file location:Default Input Folder\x7C Check image sets for unmatched or duplicate files?:Yes Group images by metadata?:No Exclude certain files?:No Specify metadata fields to group by: Select subfolders to analyze: Image count:3 Text that these images have in common (case-sensitive):Channel1- Position of this image in each group:1 Extract metadata from where?:File name Regular expression that finds metadata in the file name:^.*(?P\\\\w?\x5BA-H\x5D)(?P\\\\d{1,2})_(?P\\\\d{1,2})(?P.*).tif Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\\\\\/\x5D(?P.*)\x5B\\\\\\\\/\x5D(?P.*)$ Channel count:1 Group the movie frames?:No Grouping method:Interleaved Number of channels per group:2 Load the input as images or objects?:Images Name this loaded image:Hoechst Name this loaded object:Nuclei Retain outlines of loaded objects?:No Name the outline image:NucleiOutlines Channel number:1 Rescale intensities?:No Text that these images have in common (case-sensitive):Channel2- Position of this image in each group:2 Extract metadata from where?:File name Regular expression that finds metadata in the file name:^.*(?P\\\\w?\x5BA-H\x5D)(?P\\\\d{1,2})_(?P\\\\d{1,2})(?P.*).tif Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\\\\\/\x5D(?P.*)\x5B\\\\\\\\/\x5D(?P.*)$ Channel count:1 Group the movie frames?:No Grouping method:Interleaved Number of channels per group:2 Load the input as images or objects?:Images Name this loaded image:GFP Name this loaded object:Nuclei Retain outlines of loaded objects?:No Name the outline image:NucleiOutlines Channel number:1 Rescale intensities?:No Text that these images have in common (case-sensitive): Position of this image in each group:3 Extract metadata from where?:File name Regular expression that finds metadata in the file name:^.*(?P\\\\w?\x5BA-H\x5D)(?P\\\\d{1,2})_(?P\\\\d{1,2})(?P.*).tif Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\\\\\/\x5D(?P.*)\x5B\\\\\\\\/\x5D(?P.*)$ Channel count:1 Group the movie frames?:No Grouping method:Interleaved Number of channels per group:3 Load the input as images or objects?:Images Name this loaded image:S780 Name this loaded object:Nuclei Retain outlines of loaded objects?:No Name the outline image:LoadedImageOutlines Channel number:1 Rescale intensities?:No IdentifyPrimaryObjects:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'This module identifies the nuclei from the DNA image\x3A\', \'\', "The resulting \'PrimaryObjects\' constitute the Nuclei mask for the current image.", \'\', "Our settings here apply the name \'Nuclei\' to the mask and this will be used by later module steps (as mentioned in the notes for the LoadImages module, changing the name applied here will require repetition of the new name in subsequent modules that make use of this reference).", \'\', "Clicking open the \'eye\' icon for this module in the list to the left will open a window showing the Nuclei mask applied to each DNA channel image when the analysis is running.", \'\', \'Refer to this image window to see if the settings need to be optimized for the image data. Poorly optimized settings will yield a mask that does not reflect the appearance of the original nuclei image.\', \'\', \'When viewing the mask, nuclei outlined in yellow and red are excluded for touching the edge of the image or being outside the accepted diameter ranges defined below, respectively.\', \'\', "Key settings to assist the optimization of this module include \'Typical diameter of objects\', \'Threshold correction factor\' and the \'Lower\' bounds on the threshold.", \'\', "The \'Typical diameter of objects\' values of 9 and 70 are optimized for the HCT116 cells imaged in the example dataset using a 20x objective. Varying the cell line and/or magnification may require the range to be adjusted. The \'Min\' value is particularly useful for preventing low pixel number background noise from being mistaken for real nuclei.", \'\', "The \'Threshold correction factor\' value should be increased if the Nuclei mask appears to be over-estimating the extent of the DNA staining. Alternatively, if the Nuclei mask is missing out less brightly stained nuclei, this value should be reduced.", \'\', "Additionally, the \'Lower bounds\' value of 0.0003 may need to increase if the DNA stain images have high background staining. ", \'\', "Changing the \'Thresholding method\' from Otsu may suit data with broadly different characteristics to the images of the example dataset (e.g. staining intensity / background staining / cell confluency / etc.), but the Threshold correction factor value discussed above will need to be re-determined for each different method tried."\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True] Select the input image:Hoechst Name the primary objects to be identified:Nuclei Typical diameter of objects, in pixel units (Min,Max):9,70 Discard objects outside the diameter range?:Yes Try to merge too small objects with nearby larger objects?:No Discard objects touching the border of the image?:Yes Method to distinguish clumped objects:Shape Method to draw dividing lines between clumped objects:Propagate Size of smoothing filter: Suppress local maxima that are closer than this minimum allowed distance:5 Speed up by using lower-resolution image to find local maxima?:Yes Name the outline image:None Fill holes in identified objects?:Yes Automatically calculate size of smoothing filter for declumping?:Yes Automatically calculate minimum allowed distance between local maxima?:Yes Retain outlines of the identified objects?:No Automatically calculate the threshold using the Otsu method?:Yes Enter Laplacian of Gaussian threshold:.5 Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes Enter LoG filter diameter:5 Handling of objects if excessive number of objects identified:Continue Maximum number of objects:400 Threshold setting version:1 Threshold strategy:Global Thresholding method:Otsu Select the smoothing method for thresholding:Automatic Threshold smoothing scale:1 Threshold correction factor:2.5 Lower and upper bounds on threshold:0.0003,1.0 Approximate fraction of image covered by objects?:0.2 Manual threshold:0.0 Select the measurement to threshold with:None Select binary image:MoG Global Masking objects:From image Two-class or three-class thresholding?:Two classes Minimize the weighted variance or the entropy?:Weighted variance Assign pixels in the middle intensity class to the foreground or the background?:Foreground Method to calculate adaptive window size:Image size Size of adaptive window:10 IdentifySecondaryObjects:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:9|show_window:False|notes:\x5B"This module uses the Nuclei mask as a \'seed\' pattern from which the data in the GFP channel image is used to extend a \'Cell\' mask to the edge of the cell, stopping when no further GFP signal is detected or when a neighbouring cell is encountered.", \'\', \'Optimization and fine adjustment considerations are similar to those for the IdentifyPrimaryObjects module. Here we have selected sensitivity settings that allow detection of cytoplasmic GFP over a broad range. The intention here is that all cells, even those with weak cytoplasmic GFP, will be detected over the background signal of the image.\', \'\', "Clicking open the \'eye\' icon for this module during analysis will display the Cell mask and thereby the predicted shape of the whole cell based on the GFP footprint of each cell.", \'\', "The Cell mask is used in the next module to delineate the cytoplasm. It is also used later in this pipeline to output the size of each cell\'s GFP footprint as a pixel area. This data will be found in the output file \'Cell.csv\'. This was not used in the main manuscript, but included as an example of how to also generate geometric parameters from the image analysis."\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True] Select the input objects:Nuclei Name the objects to be identified:Cell Select the method to identify the secondary objects:Propagation Select the input image:GFP Number of pixels by which to expand the primary objects:10 Regularization factor:0.05 Name the outline image:Do not use Retain outlines of the identified secondary objects?:No Discard secondary objects touching the border of the image?:No Discard the associated primary objects?:No Name the new primary objects:FilteredNuclei Retain outlines of the new primary objects?:No Name the new primary object outlines:FilteredNucleiOutlines Fill holes in identified objects?:Yes Threshold setting version:1 Threshold strategy:Global Thresholding method:Otsu Select the smoothing method for thresholding:No smoothing Threshold smoothing scale:1 Threshold correction factor:1 Lower and upper bounds on threshold:0.0006,1.0 Approximate fraction of image covered by objects?:10% Manual threshold:0 Select the measurement to threshold with:None Select binary image:Do not use Masking objects:From image Two-class or three-class thresholding?:Two classes Minimize the weighted variance or the entropy?:Weighted variance Assign pixels in the middle intensity class to the foreground or the background?:Foreground Method to calculate adaptive window size:Image size Size of adaptive window:10 IdentifyTertiaryObjects:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B"In this module the Nuclei mask is subtracted from the Cell mask to create the \'Cytoplasm\' mask.", \'\', "Clicking open the \'eye\' icon during analysis will display a window that should show the donut-like appearance of the Cytoplasm mask next to the parent Nuclei and Cell masks. Monitoring these windows for all three \'Identify...\' modules (Primary, Secondary and Tertiary) will assist the optimization of settings for new data by visually identifying which of the masks successfully match the corresponding original image data."\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True] Select the larger identified objects:Cell Select the smaller identified objects:Nuclei Name the tertiary objects to be identified:Cytoplasm Name the outline image:CytoplasmOutlines Retain outlines of the tertiary objects?:No Shrink smaller object prior to subtraction?:Yes MeasureObjectIntensity:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'This module measures individual cell fluorescence intensity values for all permutations of the selected masks (Nuclei and Cytoplasm) and the channels named earlier in the LoadImages module (i.e. GFP, Hoechst and S780).\', \'\', \'Note that only those parameters selected in the ExportToSpreadsheet module will be saved in the output data files (determined in last module).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True] Hidden:3 Select an image to measure:Hoechst Select an image to measure:GFP Select an image to measure:S780 Select objects to measure:Nuclei Select objects to measure:Cytoplasm MeasureObjectSizeShape:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:1|show_window:False|notes:\x5B\'This module generates individual cell geometric measurements from the selected masks (i.e. Nuclei and Cell).\', \'\', \'For example, this module enables the selection of area measurements in the ExportToSpreadsheet module. The current pipeline saves the pixel area measurements for the whole cell and the corresponding nuclei. These can be found in the Cell.csv and Nuclei.csv files, respectively.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True] Select objects to measure:Nuclei Select objects to measure:Cell Calculate the Zernike features?:No CalculateMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'This module is used to calculate the nucleus/cytoplasm ratio of GFP intensity for each cell.\', \'\', \'This module is dependent on the pipeline having established Nuclei and Cell masks and the corresponding intensity values from the GFP channel data.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True] Name the output measurement:GFP_Ratio Operation:Divide Select the numerator measurement type:Object Select the numerator objects:Nuclei Select the numerator measurement:Intensity_MeanIntensity_GFP Multiply the above operand by:1 Raise the power of above operand by:1 Select the denominator measurement type:Object Select the denominator objects:Cytoplasm Select the denominator measurement:Intensity_MeanIntensity_GFP Multiply the above operand by:1 Raise the power of above operand by:1 Take log10 of result?:No Multiply the result by:1 Raise the power of result by:1 Add to the result:0 Constrain the result to a lower bound?:No Enter the lower bound:0 Constrain the result to an upper bound?:No Enter the upper bound:1 ExportToSpreadsheet:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:9|show_window:False|notes:\x5B\'The final step of the image analysis pipeline is to select the parameters needed in the output files.\', \'\', "Clicking the button \'Press to select measurements\' will open a new window which shows each of the .csv files that will be produced by the analysis. These include a file for each of the image dissection masks (Cell, Nuclei and Cytoplasm).", \'\', "Clicking through the \'tree\' structure for each file allows selection of the parameters to be saved in that output file.", \'Please note that parameters to be saved cannot be selected post-analysis.\', \'\', \'Changing the original settings supplied here in the 3_channels_pipeline.cppipe file will change the column structure of the resulting output files. This may mean that the supplied Perl scripts need to be modified to accommodate these changes (see notes in the scripts themselves).\', \'\', "The manuscript focusses on the data held in the \'Nuclei.csv\' output file. With the ExportToSpreadsheet module it is possible to browse and select additional parameters from all the masks/files generated by this pipeline. The order of the individual cell data in each output file for any one given analysis will be the same, so columns of data can be manually transferred between files using a program such as Excel. This allows simple cross-referencing of parameters that might end up in separate files."\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True] Select the column delimiter:Comma (",") Add image metadata columns to your object data file?:Yes Limit output to a size that is allowed in Excel?:No Select the measurements to export:Yes Calculate the per-image mean values for object measurements?:No Calculate the per-image median values for object measurements?:No Calculate the per-image standard deviation values for object measurements?:No Output file location:Default Output Folder\x7CNone Create a GenePattern GCT file?:No Select source of sample row name:Metadata Select the image to use as the identifier:None Select the metadata to use as the identifier:None Export all measurement types?:Yes Press button to select measurements to export:Cell\x7CAreaShape_Area,Cytoplasm\x7CIntensity_MeanIntensity_GFP,Image\x7CMetadata_WellRow,Image\x7CMetadata_Well,Image\x7CMetadata_FieldNumber,Image\x7CMetadata_WellColumn,Nuclei\x7CIntensity_MeanIntensity_GFP,Nuclei\x7CIntensity_MeanIntensity_S780,Nuclei\x7CIntensity_IntegratedIntensity_Hoechst,Nuclei\x7CAreaShape_Area,Nuclei\x7CMath_GFP_Ratio Representation of Nan/Inf:NaN Data to export:Do not use Combine these object measurements with those of the previous object?:No File name:DATA.csv Use the object name for the file name?:Yes