TITLE: CellProfiler Pipeline to Obtain Pearson&#39;s correlation coefficients for TMEM55B or pRab10 and RILPL1
AUTHORS: Chloe A Hecht, Suzanne Pfeffer

[DESCRIPTION]
We present here a CellProfiler software pipeline to quantify the intensity of endogenous TMEM55B or pRab10 in LRRK2 R1441C or VPS35 D620N MEF cells transfected with a Myc-RILPL1 plasmid detected using an anti-Myc antibody in conjunction with 4X expansion super-resolution microscopy or regular immunofluorescence microscopy. This protocol works in conjunction with Nikon Spinning disk confocal images acquired with Metamorph software (Molecular Devices, LLC) or Zeiss laser scanning confocal microscope acquired using Zen 3.4. Acquired files have a .nd or a .czi format respectively; we use the associated .TIF raw files in this pipeline. Images are maximum intensity projected from the Z-stack acquired during imaging and used for CellProfiler analysis. 

[STEPS]
SECTION: Expansion Microscopy TMEM55B and Myc-RILPL1 Correlation
11. The method involves the following steps:
Step 1 - Import data and extract metadata from file names
Step 2 - Group individual channels from each image 
Step 3 - Measure colocalization
Step 4 - Export the data

SECTION: Expansion Microscopy TMEM55B and Myc-RILPL1 Correlation
12. Select Images module at the upper right and drop images into box as indicated
SECTION: Expansion Microscopy TMEM55B and Myc-RILPL1 Correlation
13. In the Metadata tab. Extract metadata? No.
SECTION: Expansion Microscopy TMEM55B and Myc-RILPL1 Correlation
14. In the NamesAndTypes tab:
SECTION: Expansion Microscopy TMEM55B and Myc-RILPL1 Correlation
14.1. assign a name to: image matching rules
Process as 3D? No
Match All of the following rules: File/Does/Contain/w1405
Name to assign to these images “DAPI”
Select the image type: Grayscale image
Set intensity range from: Image metadata
Add two more channels and repeat the above commands, naming the channels 
File/Does/Contain: w2488  as “RILPL1”
File/Does/Contain: w3561 as “TMEM55b”
Hit “update” so Cellprofiler will process the commands and sort the images based on channel.
SECTION: Expansion Microscopy TMEM55B and Myc-RILPL1 Correlation
15. In the Groups tab: 
Do you want to group your images? No.
SECTION: Expansion Microscopy TMEM55B and Myc-RILPL1 Correlation
16. Add MeasureColocalization module from the “+” sign at the bottom
SECTION: Expansion Microscopy TMEM55B and Myc-RILPL1 Correlation
16.1. Select images to measure: TMEM55b and RILPL1
Set threshold as percentage of maximum intensity for the images: 15 
Select where to measure correlation: across entire image
Run all metrics: No 
Calculate correlation and slope metrics? Yes
Select “No” for the rest of the colocalization measures

 

SECTION: Expansion Microscopy TMEM55B and Myc-RILPL1 Correlation
17. Add the ExportToSpreadsheet module from the + at the bottom
SECTION: Expansion Microscopy TMEM55B and Myc-RILPL1 Correlation
17.1. Select the column delimiter: Tab
Output file location: choose a folder where you want the images to be saved.
Add a prefix to file names? Yes. Add date and experiment identifier. Ie. ExM_
Overwrite existing files without warning? Yes 
 

Note: This allows you to run the pipeline multiple times without having to individually ask the pipeline to rewrite a file.
Add image metadata columns to your object data file? Yes
Add image file and folder names to your object data file? Yes
Representation of Nan/Inf: NaN
Select measurements to export? Yes
Press button to select measurements: under “Image” select: Correlation, FileName, and PathName
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
Create GenePattern GCT file? No
Export all measurement types? No
Export all measurement types? Yes
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
1. The method involves the following steps:
Step 1 - Import data and extract metadata from file names
Step 2 - Group individual channels from each image 
Step 3 - Rescale image intensities
Step 4 - Identify primary objects as RILPL1 positive cells
Step 5 - Measure colocalization
Step 6 - Save identified masked cells
Step 7 - Export to Spreadsheet


SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
2. Select Images module at the upper right and drop images as indicated
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
3. In the Metadata tab. Extract metadata? Yes.
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
3.1. Metadata extraction method: Extract from image file headers
Extract metadata from: All images
Metadata data type: Text
Hit “extract metadata”
Hit “update” to populate the metadata field 
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
4. For .nd files captured using Metamorph:
In the NamesAndTypes tab:
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
4.1. Assign a name to: image matching rules
Process as 3D? No
Match All of the following rules: File/Does/Contain/w1405
Name to assign to these images “DAPI”
Select the image type: Grayscale image
Set intensity range from: Image metadata
Add two more channels and repeat the above commands, naming the channels 
File/Does/Contain: w2488  as “RILPL1”
File/Does/Contain: w3561 as “TMEM55b”
Hit “update” so Cellprofiler will process the commands and sort the images based on channel.
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
5. For both types of images:
In the Groups tab: 
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
5.1. Do you want to group your images? Yes
Metadata category: treatment.
Cellprofiler should sort based on the treatment as identified in the metadata file name expression tab.
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
6. Using the “+” sign at the bottom next to Adjust Modules, add RescaleIntensity module
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
6.1. Select the input image: RILPL1
Name the output image: rescale_RILPL1
Rescaling method: Divide each image by the same value
Divisor value: 0.08 for R1441C MEF or 0.3 for VPS35 D620N MEF
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
6.2. 
 

SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
7. Add IdentifyPrimaryObjects module from the “+” sign at the bottom:
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
7.1. Use advanced settings? Yes
Select the input image: rescale_RILPL1
Name the primary objects to be identified: RILPL1cell
Diameter of objects, in pixel units: 50 - 600 for R1441C MEF or 150-700 for VPS35 D620N MEF

 
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 1.7 for R1441C MEF or 3 for VPS35 D620N MEF
Threshold correction factor .6 for R1441C MEF or 0.4 for VPS35 D620N MEF

 
Lower and upper bounds on threshold? 0 to 1 for R1441C MEF or 0.002 to 1.0 for VPS35 D620N MEF
Log transform before thresholding? No
Method to distinguish clumped objects? Intensity
Method to draw dividing lines between clumped objects? Shape
Automatically calculate size of smoothing filter for declumping? Yes
Automatically calculate size for 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 
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
8. Add MeasureColocalization module from the “+” sign at the bottom
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
8.1. Select images to measure: pRab10 or TMEM55b and RILPL1 
Set threshold as percentage of maximum intensity for the images: 15 
Select where to measure correlation: within objects, RILPL1cell

 
Run all metrics: No 
Calculate correlation and slope metrics? Yes
Select “No” for the rest of the colocalization measures

 

SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
10. Add the ExportToSpreadsheet module from the + at the bottom
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
10.1. Select the column delimiter: Tab
Output file location: choose a folder where you want the images to be saved.
Add a prefix to file names? Yes. Add date and experiment identifier. Ie. RILPL1March_
Overwrite existing files without warning? Yes 
Note: This allows you to run the pipeline multiple times without having to individually ask the pipeline to rewrite a file.
Add image metadata columns to your object data file? Yes
Add image file and folder names to your object data file? Yes
Representation of Nan/Inf: NaN
Select measurements to export? Yes
Press button to select measurements: under “RILPL1cell” select: Correlation, FileName, and PathName
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
Create GenePattern GCT file? No
Export all measurement types? No
Export all measurement types? Yes
Click “Analyze Images”
The pipeline will run and export the data to the folder previously specified
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
4.2. For .czi files acquired using Zen 3.4:
In the NamesAndTypes tab:

SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
4.3. Add two more images using “add another image tab” at the bottom
In the first image block:
Assign a name to: image matching rules
Process as 3D? No
Match All of the following rules
Select the rule criteria: Metadata does Have C matching, 0
Name to assign these images: TMEM55b
Select the image type: Grayscale image
Set intensity range from: Image metadata
In the second image block:
Assign a name to: image matching rules
Process as 3D? No
Match All of the following rules
Select the rule criteria: Metadata does Have C matching, 0
Name to assign these images: RILPL1
Select the image type: Grayscale image
Set intensity range from: Image metadata
In the third image block:
Assign a name to: image matching rules
Process as 3D? No
Match All of the following rules
Select the rule criteria: Metadata does Have C matching, 0
Name to assign these images: DAPI
Select the image type: Grayscale image
Set intensity range from: Image metadata
Image set matching method: order
Hit “update” at the bottom
The chart should populate with values in three channels: TMEM55b, RILPL1, and DAPI
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
9. Add the ConvertObjectstoImage module and the SaveImages module from the + at the bottom
SECTION: Immunofluoresence Microscopy TMEM55B or pRab10 and Myc-RILPL1 Correlation
9.1. In the ConvertObjectstoImage module:
Select the input object: RILPL1cell
Name the output image: IdentifyRILPL1
Select the color format: Color
Select the colormap: default
In the SaveImages module:
Select type of image to save: image
Select the image to save: IdentifyRILPL1
Constructing file names: From image filename
Image name for file prefix: RILPL1
Append a suffix to the image file name? No
Saved file format: tiff
Image bit depth: 8-bit integer
Save with lossless compression? Yes
Output file location: choose a folder where images should be saved
Overwrite existing files without warning? Yes
When to save? Every cycle
Record the file and path information to the saved image? No
Create subfolders in the output folder? No