A computational pipeline to quantify primary cilia in mouse embryonic fibroblasts with CellProfiler

Select the  Images  module, drag and drop the maximum intensity projected .TIF files as indicated

Select the Metadata  module

In the Metadata module: 

Extract metadata? Yes.

Metadata extraction method: Extract from file/folder names

Metadata source: File name

 Regular expression to extract file name : 

 “regular expression” will have the form: 

^(?P[A-Z)]{2}).*#(?P[0-9]{2}) to extract the cell type and image number from an example file name "WTcells - WTcells #14_max.tif."

 Here,

^ indicates the beginning of the file name

(?P[A-Z]{2}) tells the program to name the captured field “celltype” and recognize two letters that follow

(?P[0-9]{2}) tells the program to name the captured field “imagenumber” and recognize two digits that follow

 

Extract metadata from: All images

 

Add another extraction method

Metadata extraction method: Extract from image file headers       

Extract metadata from: All images

Hit “Extract metadata”

Metadata data type: Text

Hit “update” to populate the metadata field

Go to  Names and types module 

Assign a name to : “Images matching rules” 

Process as 3D : No

Select the rule criteria

Match “All” of the following rules 

“Metadata/Does/Have C matching 0”

Name to assign these images: Arl13b

Select the image type: Grayscale image

Set intensity range from : Image metadata

 

Add another image

 

Match “All” of the following rules 

“Metadata/Does/Have C matching 1”

Name to assign these images: dapi

Select the image type: Grayscale image

Set intensity range from : Image metadata

 

Hit “update” to populate the names and types field

Select  Groups module

 

Do you want to group your images? Yes

Metadata category: celltype

Add another metadata item

Metadata category: imagenumber

 

This groups images based on cell type and image number as identified in the metadata module.

Click on the “+” sign at the bottom next to Adjust Modules. One can choose different modules by double-clicking from the list or by typing in the search box. Under module category, object processing, add  Identifyprimaryobjects module.

 

Use advanced settings? Yes

Select the input image: dapi

Name the primary objects to be identified: nuclei

Typical diameter of objects, in pixel units: 60-200

Note :

This has to be optimized for each image set.

 

Discard objects outside the diameter range? Yes

Discard objects touching the border of the image? No

 

Note:

Check by clicking “Start Test Mode” and hitting the green triangle next to the IdentifyPrimaryObjects module. 

 

Threshold strategy? Global

Thresholding method? Minimum Cross-Entropy

Threshold smoothing scale 1.3488 

Threshold correction factor 1.0

Lower and upper bounds on threshold 0.05 and 0.8

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? No

Size of smoothing filter 30

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

 

Note

 

These parameters will need to be optimized for each image set. Check by clicking “Start Test Mode” and hitting the green triangle next to the IdentifyPrimaryObjects module each time a parameter is changed to find the best parameters for each image set. Green outlines represent valid objects whereas magenta/orange outlines represent invalid objects, as they are either touching the border or outside the diameter range set.

Add  Identifyprimaryobjects module

Use advanced settings? Yes

Select the input image: Arl13b

Name the primary objects to be identified: cilia

Typical diameter of objects, in pixel units: 5-15

Discard objects outside the diameter range? Yes

Discard objects touching the border of the image? Yes

Threshold strategy? Global

Thresholding method? Otsu

Two-class or three-class thresholding? Two classes

Threshold smoothing scale 1.0

Threshold correction factor 0.5

Lower and upper bounds on threshold 0.0 and 1.0

Log transform before thresholding? No

Method to distinguish clumped objects? None

Fill holes in identified objects? After both thresholding and declumping 

Handling of objects if excessive number of objects identified? Continue

 

Note

Check by clicking “Start Test Mode” and hitting the green triangle next to the IdentifyPrimaryObjects module each time a parameter is changed to find the best parameters for each image set.

Add  MeasureObjectSizeShape  module 

Select object sets to measure : cilia, nuclei

Calculate Zernike features?No

Calculate the advanced features? No

Add the ExportToSpreadsheet module from the + at the bottom

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. 

File name prefix: Add experiment identifier

Overwrite existing files without warning? No

Note: While the pipeline is run for optimizing the parameters, choose Yes to avoid being asked to rewrite each file.

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 measurements to export? Yes

Press button to select measurements: 

Select measurements: Choose number under cilia and nuclei

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

Data to export: nuclei

Use the object name for the file name? Yes

Add another data set

Data to export: cilia

Combine these object measurements with those of the previous object? Yes

 

Save the pipeline from File-Save Project and hit  Analyze Images on bottom left.

The pipeline will run and export the data to the folder previously specified. The output file can be opened in Excel software. Distinct columns will indicate number of nuclei and number of cilia in each image.