Sep 27, 2024
A CellProfiler computational pipeline to quantify localization of PPM1H on mitochondria
- 1Stanford University School of Medicine and Aligning Science Across Parkinson's
Protocol Citation: Ebsy Jaimon, Suzanne R Pfeffer 2024. A CellProfiler computational pipeline to quantify localization of PPM1H on mitochondria. protocols.io https://dx.doi.org/10.17504/protocols.io.j8nlk8qk6l5r/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: September 25, 2024
Last Modified: September 27, 2024
Protocol Integer ID: 108397
Keywords: ASAPCRN, PPM1H phosphatase, mitochondrial localization
Funders Acknowledgement:
Aligning Science Across Parkinson's
Grant ID: ASAP-000463
Abstract
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.
Materials
1) .TIFF files from Spinning disk confocal microscope
2) FIJI/imageJ
3) CellProfiler software 4.04 or later*
*Stirling DR, Swain-Bowden MJ, Lucas AM, Carpenter AE, Cimini BA, Goodman A (2021). CellProfiler 4: improvements in speed, utility and usability. BMC Bioinformatics, 22 (1), 433. PMID: 34507520 PMCID: PMC8431850.
Batch process images and import files into CellProfiler
Batch process images and import files into CellProfiler
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.
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.
Go to the Metadata module to extract information describing the images.
In the Metadata module, choose Yes for Extract metadata.
Metadata extraction method: Extract from image file headers
Extract metadata from: All images
Click “Extract metadata”
Click on Add another extraction method
Metadata extraction method: Extract from file/folder names
Metadata source: File name
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.
Metadata data type: Text
Click on “update” to populate the metadata field
Go to the NamesAndTypes module to give a name to each channel.
Assign a name to: Images matching rules
Process as 3D: No
Match “All” of the following rules
Select the rule criteria: “File/Does/Contain/w1561”
Name to assign these images: PPM1H_mApple
Select the image type: Grayscale image
Set intensity range from: Image metadata
Click on Add another image
Match “All” of the following rules
Select the rule criteria: “File/Does/Contain/w2488”
Name to assign these images: GFP_Mito
Select the image type: Grayscale image
Set intensity range from: Image metadata
Click on “update” to populate the names and types field
Go to the Groups module and choose No for Do you want to group your images?
Segment GFP-Mito objects
Segment GFP-Mito objects
Steps 6 and 7 help to reduce noise.
Click on the “+” sign at the bottom left next to Adjust Modules. Under the module categories, click Advanced, and then choose Gaussian filter.
Select the input image: GFP_Mito
Name the output image: Gaussian filter_mito
Sigma: 2 [Note that larger sigmas induce more blurring]
Add ImageMath module to the pipeline
Operation: Subtract
Name the output image: ImageAfterMath_mito
Image or measurement? Image
Select the first image: GFP-Mito
Multiply the first image by 1.0
Image or measurement? Image
Select the second image: GaussianFilter_mito
Multiply the second image by 1.0
Add another image
Raise the power of the result by 1.0
Multiply the result by 1.0
Add to result 0.0
Set values less than 0 equal to 0? Yes
Set values greater than 1 equal to 1? Yes
Replace invalid values with 0? Yes
Ignore the imaging masks? No
Add IdentifyPrimaryObjects module to the pipeline.
Use advanced settings? Yes
Select the input image: ImageAfterMath_mito
Name the primary objects to be identified: Mitochondriaobjects
Typical diameter of objects, in pixel units (Min, Max): 1,20
Discard objects outside the diameter range: Yes
Discard objects touching the border of the image: No
Threshold strategy: Global
Thresholding method: Otsu
Two-class or three-class thresholding? Two classes
Threshold smoothing scale: 1
Threshold correction factor: 1
Lower and upper bounds on threshold 0.0001 and 1.0
Log transform before thresholding? No
Method to distinguish clumped objects? Intensity
Method to draw dividing lines between clumped objects: Intensity
Automatically calculate size of smoothing filter for declumping? Yes
Automatically calculate minimum allowed distance between local maxima? Yes
Speed up by using lower-resolution image to find local maxima? Yes
Display accepted local maxima? No
Fill holes in identified objects? After both thresholding and declumping
Handling of objects if excessive number of objects identified? Continue
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.
Figure 1: Example of input image (at left) and segmented GFP-Mito objects (at right). Green outlines represent valid objects.
Segment PPM1H-mApple objects
Segment PPM1H-mApple objects
Add Gaussian filter module to the pipeline
Select the input image: PPM1H_mApple
Name the output image: Gaussian filter_ppm1h
Sigma: 10 [Note that larger sigmas induce more blurring]
Add ImageMath module to the pipeline
Operation: Subtract
Name the output image: ImageAfterMath_ppm1h
Image or measurement? Image
Select the first image: PPM1H_mApple
Multiply the first image by 1.0
Image or measurement? Image
Select the second image: GaussianFilter_ppm1h
Multiply the second image by 1.0
Add another image
Raise the power of the result by 1.0
Multiply the result by 1.0
Add to result 0.0
Set values less than 0 equal to 0? Yes
Set values greater than 1 equal to 1? Yes
Replace invalid values with 0? Yes
Ignore the imaging masks? No
Add IdentifyPrimaryObjects module to the pipeline.
Use advanced settings? Yes
Select the input image: ImageAfterMath_ppm1h
Name the primary objects to be identified: PPM1Hobjects
Typical diameter of objects, in pixel units (Min, Max): 1,20
Discard objects outside the diameter range: Yes
Discard objects touching the border of the image: No
Threshold strategy: Global
Thresholding method: Otsu
Two-class or three-class thresholding? Two classes
Threshold smoothing scale: 1.2
Threshold correction factor: 1
Lower and upper bounds on threshold 0.001 and 1.0
Log transform before thresholding? No
Method to distinguish clumped objects? Intensity
Method to draw dividing lines between clumped objects: Intensity
Automatically calculate size of smoothing filter for declumping? Yes
Automatically calculate minimum allowed distance between local maxima? Yes
Speed up by using lower-resolution image to find local maxima? Yes
Display accepted local maxima? No
Fill holes in identified objects? After both thresholding and declumping
Handling of objects if excessive number of objects identified? Continue
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.
Figure 2: Example of input image (at left) and segmented PPM1H-mApple objects (at right). Green outlines represent valid objects.
Segment regions where PPM1H-mApple overlaps with GFP-Mito
Segment regions where PPM1H-mApple overlaps with GFP-Mito
Add MaskObjects module
Select objects to be masked: PPM1H objects
Name the masked objects: Maskedppm1h
Mask using a region defined by other objects or by binary image? Objects
Select the masking object: Mitochondriaobjects
Invert the mask? No
Handling of objects that are partially masked: Keep overlapping region
Numbering of resulting objects: Renumber
Figure 3: Example of overlay of PPM1H-mApple objects (magenta) and GFP-Mito objects (cyan) (at left). Shown at right is the segmentation used to identify overlapping objects. Green outlines are PPM1H-mApple objects overlapping with GFP-Mito objects and magenta outlines are PPM1H-mApple objects that don’t overlap.
Measure the intensity
Measure the intensity
Add MeasureObjectIntensity module.
Select images to measure: PPM1H_mApple
Select objects to measure: Choose Maskedppm1h and PPM1Hobjects
Add ExportToSpreadsheet module from the + at the bottom to export the measurements into files that can be opened in Excel.
Select the column delimiter: Tab
Output file location: choose a folder where images will be saved
Add a prefix to file names? Yes
File name prefix: Add experiment identifier
Overwrite existing files without warning? No
Add image metadata columns to your object data file? Yes
Add image file and folder names to your object data file? No
Representation of Nan/Inf: NaN
Select the measurements to export? Yes
Click Press button to select measurements: Under “Maskedppm1h” choose Intensity -> IntegratedIntensity -> PPM1H ->mApple. Under “PPM1Hobjects” choose Intensity -> IntegratedIntensity -> PPM1H -> mApple
Calculate the per-image mean values for object measurements? No
Calculate the per-image median values for object measurements? No
Calculate per-image standard deviation values for object measurements? No
Create GenePattern GCT file? No
Export all measurement types? No
Data to export: Maskedppm1h
Use the object name for the file name? Yes
Click Add another data set
Data to export: PPM1Hobjects
Combine these object measurements with those of the previous object? Yes
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.
Protocol references
(1) Stirling DR, Swain-Bowden MJ, Lucas AM, Carpenter AE, Cimini BA, Goodman A (2021). CellProfiler 4: improvements in speed, utility and usability. BMC Bioinformatics, 22 (1), 433.
(2) King C, Sengupta P, Seo AY, Lippincott-Schwartz J. ER membranes exhibit phase behavior at sites of organelle contact. Proc Natl Acad Sci U S A. 2020 Mar 31;117(13):7225-7235. doi: 10.1073/pnas.1910854117.