TITLE: A CellProfiler computational pipeline to quantify localization of PPM1H on mitochondria
AUTHORS: Ebsy Jaimon, Suzanne R Pfeffer

[DESCRIPTION]
Here we present a CellProfiler (1) software pipeline to quantify the localization of PPM1H on mitochondria.  In this work, we have fragmented the mitochondria by hypotonic swelling to facilitate localization and highlight membrane contacts (2); PPM1H-mApple labeled pixels that coincide with GFP-Mito labeled pixels are scored. Wild-type MEFs expressing PPM1H-mApple and GFP-Mito are treated with oligomycin/antimycin (4 hours) and then incubated for 2 minutes in a hypotonic medium.  Images of live cells are acquired using a spinning disk confocal microscope. The multi-channel Z-stack images are maximum intensity projected and used for CellProfiler analysis.

[STEPS]
SECTION: Batch process images and import files into CellProfiler
1. Use the FIJI macro as described in dx.doi.org/10.17504/protocols.io.3byl4bpo8vo5/v1 to maximum intensity Z project the images. Open the folder with the images that need to be processed, choose the channel and the output folder, run code for maximum intensity Z projection, and save the files as .TIFF.
SECTION: Batch process images and import files into CellProfiler
2. Open CellProfiler software. Go to the Images module, drag and drop the maximum intensity projected .TIFF files as indicated. Select “no filtering” in the filter images option.
SECTION: Batch process images and import files into CellProfiler
3. Go to the Metadata module to extract information describing the images.
SECTION: Segment GFP-Mito objects
6. Steps 6 and 7 help to reduce noise.
SECTION: Segment GFP-Mito objects
6.1. Click on the “+” sign at the bottom left next to Adjust Modules. Under the module categories, click Advanced, and then choose Gaussian filter.
SECTION: Segment GFP-Mito objects
6.2. Select the input image: GFP_Mito
SECTION: Segment GFP-Mito objects
6.3. Name the output image: Gaussian filter_mito
SECTION: Segment GFP-Mito objects
6.4. Sigma: 2 [Note that larger sigmas induce more blurring]
SECTION: Segment GFP-Mito objects
7. Add ImageMath module to the pipeline
SECTION: Segment GFP-Mito objects
7.1. Operation: Subtract 

SECTION: Segment GFP-Mito objects
9. Check by selecting “Start Test Mode” and select the green triangle next to the IdentifyPrimaryObjects module each time a parameter is changed to find the best parameters for each image set. Test using multiple images to ensure that the settings work for other images in the project.
 
 
 

SECTION: Batch process images and import files into CellProfiler
4. Go to the NamesAndTypes module to give a name to each channel.  
SECTION: Batch process images and import files into CellProfiler
4.1. Assign a name to: Images matching rules
SECTION: Batch process images and import files into CellProfiler
4.2. Process as 3D: No
SECTION: Batch process images and import files into CellProfiler
4.3. Match “All” of the following rules
SECTION: Batch process images and import files into CellProfiler
4.4. Select the rule criteria: “File/Does/Contain/w1561”
SECTION: Batch process images and import files into CellProfiler
4.5. Name to assign these images: PPM1H_mApple
SECTION: Batch process images and import files into CellProfiler
4.6. Select the image type: Grayscale image
SECTION: Batch process images and import files into CellProfiler
4.7. Set intensity range from: Image metadata
SECTION: Batch process images and import files into CellProfiler
4.8. Click on Add another image
SECTION: Batch process images and import files into CellProfiler
4.9. Match “All” of the following rules
SECTION: Batch process images and import files into CellProfiler
4.10. Select the rule criteria: “File/Does/Contain/w2488”
SECTION: Batch process images and import files into CellProfiler
4.11. Name to assign these images: GFP_Mito
SECTION: Batch process images and import files into CellProfiler
4.12. Select the image type: Grayscale image
SECTION: Batch process images and import files into CellProfiler
4.13. Set intensity range from: Image metadata
SECTION: Batch process images and import files into CellProfiler
4.14. Click on “update” to populate the names and types field  

5. Go to the Groups module and choose No for Do you want to group your images?
SECTION: Segment PPM1H-mApple objects
10. Add Gaussian filter module to the pipeline
SECTION: Segment PPM1H-mApple objects
11. Add ImageMath module to the pipeline 

SECTION: Segment PPM1H-mApple objects
11.1. Operation: Subtract
SECTION: Segment PPM1H-mApple objects
11.2. Name the output image: ImageAfterMath_ppm1h
SECTION: Segment PPM1H-mApple objects
11.3. Image or measurement? Image
SECTION: Segment PPM1H-mApple objects
11.4. Select the first image: PPM1H_mApple
SECTION: Segment PPM1H-mApple objects
11.5. Multiply the first image by 1.0
SECTION: Segment PPM1H-mApple objects
11.6. Image or measurement? Image
SECTION: Segment PPM1H-mApple objects
11.7. Select the second image: GaussianFilter_ppm1h
SECTION: Segment PPM1H-mApple objects
11.8. Multiply the second image by 1.0
SECTION: Segment PPM1H-mApple objects
11.9. Add another image
SECTION: Segment PPM1H-mApple objects
11.10. Raise the power of the result by 1.0
SECTION: Segment PPM1H-mApple objects
11.11. Multiply the result by 1.0
SECTION: Segment PPM1H-mApple objects
11.12. Add to result 0.0
SECTION: Segment PPM1H-mApple objects
11.13. Set values less than 0 equal to 0? Yes
SECTION: Segment PPM1H-mApple objects
11.14. Set values greater than 1 equal to 1? Yes
SECTION: Segment PPM1H-mApple objects
11.15. Replace invalid values with 0? Yes
SECTION: Segment PPM1H-mApple objects
11.16. Ignore the imaging masks? No  


12.2. Select the input image: ImageAfterMath_ppm1h
12.3. Name the primary objects to be identified: PPM1Hobjects
12.4. Typical diameter of objects, in pixel units (Min, Max): 1,20
12.5. Discard objects outside the diameter range: Yes
12.6. Discard objects touching the border of the image: No
12.7. Threshold strategy: Global
12.8. Thresholding method: Otsu
12.9. Two-class or three-class thresholding? Two classes
12.10. Threshold smoothing scale: 1.2
12.11. Threshold correction factor: 1
12.12. Lower and upper bounds on threshold 0.001 and 1.0
12.13. Log transform before thresholding? No
12.14. Method to distinguish clumped objects? Intensity
12.15. Method to draw dividing lines between clumped objects: Intensity
12.16. Automatically calculate size of smoothing filter for declumping? Yes
12.17. Automatically calculate minimum allowed distance between local maxima? Yes
12.18. Speed up by using lower-resolution image to find local maxima? Yes
12.19. Display accepted local maxima? No
12.20. Fill holes in identified objects? After both thresholding and declumping
12.21. Handling of objects if excessive number of objects identified? Continue  

12.1. Use advanced settings? Yes
SECTION: Segment regions where PPM1H-mApple overlaps with GFP-Mito
14. Add MaskObjects module
SECTION: Segment regions where PPM1H-mApple overlaps with GFP-Mito
14.1. Select objects to be masked: PPM1H objects
SECTION: Segment regions where PPM1H-mApple overlaps with GFP-Mito
14.2. Name the masked objects: Maskedppm1h
SECTION: Segment regions where PPM1H-mApple overlaps with GFP-Mito
14.3. Mask using a region defined by other objects or by binary image? Objects
SECTION: Segment regions where PPM1H-mApple overlaps with GFP-Mito
14.4. Select the masking object: Mitochondriaobjects
SECTION: Segment regions where PPM1H-mApple overlaps with GFP-Mito
14.5. Invert the mask? No
SECTION: Segment regions where PPM1H-mApple overlaps with GFP-Mito
14.6. Handling of objects that are partially masked: Keep overlapping region
SECTION: Segment regions where PPM1H-mApple overlaps with GFP-Mito
14.7. Numbering of resulting objects: Renumber  

 
 

SECTION: Measure the intensity
15. Add MeasureObjectIntensity module.
SECTION: Measure the intensity
15.1. Select images to measure: PPM1H_mApple
SECTION: Measure the intensity
15.2. Select objects to measure: Choose Maskedppm1h and PPM1Hobjects
SECTION: Measure the intensity
16. Add ExportToSpreadsheet module from the + at the bottom to export the measurements into files that can be opened in Excel. 
SECTION: Measure the intensity
16.1. Select the column delimiter: Tab
SECTION: Measure the intensity
16.2. Output file location: choose a folder where images will be saved
SECTION: Measure the intensity
16.3. Add a prefix to file names? Yes
SECTION: Measure the intensity
16.4. File name prefix: Add experiment identifier
SECTION: Measure the intensity
16.5. Overwrite existing files without warning? No
SECTION: Measure the intensity
16.6. Add image metadata columns to your object data file? Yes
SECTION: Measure the intensity
16.7. Add image file and folder names to your object data file? No
SECTION: Measure the intensity
16.8. Representation of Nan/Inf: NaN
SECTION: Measure the intensity
16.9. Select the measurements to export? Yes
SECTION: Measure the intensity
16.10. Click Press button to select measurements: Under “Maskedppm1h” choose Intensity -> IntegratedIntensity -> PPM1H ->mApple. Under “PPM1Hobjects” choose Intensity -> IntegratedIntensity -> PPM1H -> mApple
SECTION: Measure the intensity
16.11. Calculate the per-image mean values for object measurements? No
SECTION: Measure the intensity
16.12. Calculate the per-image median values for object measurements? No
SECTION: Measure the intensity
16.13. Calculate per-image standard deviation values for object measurements? No
SECTION: Measure the intensity
16.14. Create GenePattern GCT file? No
SECTION: Measure the intensity
16.15. Export all measurement types? No
SECTION: Measure the intensity
16.16. Data to export: Maskedppm1h
SECTION: Measure the intensity
16.17. Use the object name for the file name? Yes
SECTION: Measure the intensity
16.18. Click Add another data set
SECTION: Measure the intensity
16.19. Data to export: PPM1Hobjects
SECTION: Measure the intensity
16.20. Combine these object measurements with those of the previous object? Yes 
SECTION: Segment PPM1H-mApple objects
10.1. Select the input image: PPM1H_mApple
SECTION: Segment PPM1H-mApple objects
10.2. Name the output image: Gaussian filter_ppm1h
SECTION: Segment PPM1H-mApple objects
10.3. Sigma: 10 [Note that larger sigmas induce more blurring]
SECTION: Batch process images and import files into CellProfiler
3.1. In the Metadata module, choose Yes for Extract metadata.
SECTION: Batch process images and import files into CellProfiler
3.2. Metadata extraction method: Extract from image file headers
SECTION: Batch process images and import files into CellProfiler
3.3. Extract metadata from: All images
SECTION: Batch process images and import files into CellProfiler
3.4.  Click “Extract metadata”
SECTION: Batch process images and import files into CellProfiler
3.5. Click on Add another extraction method
SECTION: Batch process images and import files into CellProfiler
3.6. Metadata extraction method: Extract from file/folder names
SECTION: Batch process images and import files into CellProfiler
3.7. Metadata source: File name
SECTION: Batch process images and import files into CellProfiler
3.8. Enter regular expression to extract from file name:
“Regex” will be ^_(?P<image_number>[0-9]{2}) for an example file name “Image_22_w1561      Confocal.TIF_max.tif”. This step helps to extract the image number (22) from the file name.
In Regex, ^ indicates the beginning of the file name and (?P<image_number>[0-9]{2}) tells the program to name the captured field “image_number” and recognize two digits that follow. Click the magnifying glass icon on the right to check the accuracy of regular expression.
SECTION: Batch process images and import files into CellProfiler
3.9.  Metadata data type: Text
SECTION: Batch process images and import files into CellProfiler
3.10. Click on “update” to populate the metadata field
SECTION: Segment GFP-Mito objects
7.2. Name the output image: ImageAfterMath_mito
SECTION: Segment GFP-Mito objects
7.3. Image or measurement? Image
SECTION: Segment GFP-Mito objects
7.4. Select the first image: GFP-Mito
SECTION: Segment GFP-Mito objects
8. Add IdentifyPrimaryObjects module to the pipeline.
SECTION: Segment GFP-Mito objects
7.5. Multiply the first image by 1.0
SECTION: Segment GFP-Mito objects
7.6. Image or measurement? Image
SECTION: Segment GFP-Mito objects
7.7. Select the second image: GaussianFilter_mito
SECTION: Segment GFP-Mito objects
7.8. Multiply the second image by 1.0
SECTION: Segment GFP-Mito objects
7.9. Add another image
SECTION: Segment GFP-Mito objects
7.10. Raise the power of the result by 1.0
SECTION: Segment GFP-Mito objects
7.11. Multiply the result by 1.0
SECTION: Segment GFP-Mito objects
7.12. Add to result 0.0
SECTION: Segment GFP-Mito objects
7.13. Set values less than 0 equal to 0? Yes
SECTION: Segment GFP-Mito objects
7.14. Set values greater than 1 equal to 1? Yes
SECTION: Segment GFP-Mito objects
7.15. Replace invalid values with 0? Yes
SECTION: Segment GFP-Mito objects
7.16. Ignore the imaging masks? No
SECTION: Segment GFP-Mito objects
8.1.  Use advanced settings? Yes
SECTION: Segment GFP-Mito objects
8.2. Select the input image: ImageAfterMath_mito
SECTION: Segment GFP-Mito objects
8.3. Name the primary objects to be identified: Mitochondriaobjects
SECTION: Segment GFP-Mito objects
8.4. Typical diameter of objects, in pixel units (Min, Max): 1,20
SECTION: Segment GFP-Mito objects
8.5. Discard objects outside the diameter range: Yes
SECTION: Segment GFP-Mito objects
8.6. Discard objects touching the border of the image: No
SECTION: Segment GFP-Mito objects
8.7. Threshold strategy: Global
SECTION: Segment GFP-Mito objects
8.8. Thresholding method: Otsu
SECTION: Segment GFP-Mito objects
8.9. Two-class or three-class thresholding? Two classes
SECTION: Segment GFP-Mito objects
8.10. Threshold smoothing scale: 1
SECTION: Segment GFP-Mito objects
8.11. Threshold correction factor: 1
SECTION: Segment GFP-Mito objects
8.12. Lower and upper bounds on threshold 0.0001 and 1.0
SECTION: Segment GFP-Mito objects
8.13. Log transform before thresholding? No
SECTION: Segment GFP-Mito objects
8.14. Method to distinguish clumped objects? Intensity
SECTION: Segment GFP-Mito objects
8.17. Automatically calculate minimum allowed distance between local maxima? Yes
SECTION: Segment GFP-Mito objects
8.15. Method to draw dividing lines between clumped objects: Intensity
SECTION: Segment GFP-Mito objects
8.16. Automatically calculate size of smoothing filter for declumping? Yes
SECTION: Segment GFP-Mito objects
8.18. Speed up by using lower-resolution image to find local maxima?  Yes
SECTION: Segment GFP-Mito objects
8.19. Display accepted local maxima? No
SECTION: Segment GFP-Mito objects
8.20. Fill holes in identified objects? After both thresholding and declumping
SECTION: Segment GFP-Mito objects
8.21. Handling of objects if excessive number of objects identified? Continue
SECTION: Segment PPM1H-mApple objects
12. Add IdentifyPrimaryObjects module to the pipeline.
13. Check by selecting “Start Test Mode” and select the green triangle next to the IdentifyPrimaryObjects module each time a parameter is changed to find the best parameters for each image set. Test using multiple images to ensure that the settings work for other images in the project.



 


SECTION: Measure the intensity
17. Save the pipeline from File-Save Project and click on Analyze Images on the bottom left. The pipeline will run and export the data to the folder previously specified. The output .csv file will have distinct columns indicating image number, intensity of PPM1H-mApple labeled pixels that coincide with GFP-Mito labeled pixels, and intensity of all PPM1H-mApple labeled pixels.