TITLE: Immunostaining and quantification of intracellular accessible cholesterol using ALOD4-mNeon in Human Fibroblasts
AUTHORS: Sreeja V Nair, Ebsy Jaimon, Suzanne R Pfeffer

[DESCRIPTION]
Here we present a protocol for quantitative immunostaining of intracellular accessible cholesterol using ALOD4-mNeon in human fibroblasts. 

[GUIDELINES]
Be sure to use freshly prepared ALOD4-mNeon (within 14 days of preparation).  Store at 4°C and do not freeze.

[STEPS]
SECTION: ALOD4 Staining and image acquisition
1. This section describes plating and staining of cells and image acquisition.
SECTION: ALOD4 Staining and image acquisition
2. Seed 0.2 X 106 cells onto 3-4 collagen-coated 12 mm coverslips, in each well of a six-well plate
SECTION: ALOD4 Staining and image acquisition
3. 16-18 hr after plating, wash the cells once with 1X PBS 

SECTION: ALOD4 Staining and image acquisition
4. Transfer each coverslip to a well of a 24-well plate containing 500µl 4 % (v/v) PFA per well; fix the cells for 15 min at Room temperature

SECTION: ALOD4 Staining and image acquisition
5. Wash cells 3X with 1X PBS
SECTION: ALOD4 Staining and image acquisition
6. Permeabilize the cells by addition of 0.1% Saponin in 1X PBS for 5 min at Room temperature

SECTION: ALOD4 Staining and image acquisition
7. Wash cells 3X with 1X PBS
SECTION: ALOD4 Staining and image acquisition
8. Block fixative by addition of 1% BSA in 1X PBS for 30 min at Room temperature

SECTION: ALOD4 Staining and image acquisition
9. Dilute ALOD4-mNeon to 4µM in 1% BSA/1X PBS

SECTION: ALOD4 Staining and image acquisition
10. Add 50 µl, 4µM ALOD4 per coverslip 
SECTION: ALOD4 Staining and image acquisition
11. Incubate cells for 60 min at Room temperature 

SECTION: ALOD4 Staining and image acquisition
12. Wash cells 3X in 1X PBS and mount coverslips onto clean glass slides with 4 µl Mowiol. Air-dry coverslips overnight or at least 4-5 hours; store in dark at Room temperature

SECTION: ALOD4 Staining and image acquisition
13. Images below were acquired using a Zeiss laser scanning microscope.
SECTION: Batch process images for maximum intensity projection and background subtraction
14. Images need to be batch processed for maximum intensity projection and uniform background subtraction.  Process the images for CellProfiler analysis as described in dx.doi.org/10.17504/protocols.io.3byl4bpo8vo5/v1
FOOTNOTE: For Zeiss images, the user needs to specify the folder to save the processed images and run lines 26, 27, and 29. Processing includes maximum intensity projection and background subtraction. Choose a value for background subtraction based on the images analyzed.
SECTION: Import files and Segment Cells
15. In CellProfiler, select the Images module, drag and drop the background subtracted and maximum intensity projected, .TIF files created in Step 14 above.  Here we subtracted a value of 50 to remove the background noise.
SECTION: Import files and Segment Cells
16. Select the Metadata module

In the Metadata module: 
Extract metadata? Yes. 

Metadata extraction method: Extract from image file headers 

Extract metadata from: All images 

Hit “Extract metadata”  

Click Add another extraction method 

Metadata extraction method: Extract from file/folder names 

Metadata source: File name 

Regular expression to extract from file name: “Regex” will be as follows: ^GM(?P[0-9]{4}) .*#(?P[0-9]{2}) for an example file name “GM01607.czi #01_max.tif”. 
This step helps to extract the cell line, and position from each file name.  In Regex, ^ indicates the beginning of the file name (?P[0-9]{4}) tells the program to name the captured field “Cell line” and recognize five digits that follow .* indicates any character (?P[0-9]{2}) tells the program to name the captured field “position” and recognize two digits that follow.

Metadata data type: Text 

Hit “update” to populate the metadata field
SECTION: Import files and Segment Cells
17. Go to NamesAndTypes module

In the NamesAndTypes 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: ALOD4 

Select the image type: Grayscale image
 
Set intensity range from: Image metadata 

Hit “update” to populate the names and types field
SECTION: Import files and Segment Cells
18. Go to Groups module 

In the Groups module:
Do you want to group your images? Yes 

Metadata category: Celltype 

Metadata category: position  

This groups images based on Cell type, and position as identified in the metadata module.  
SECTION: Import files and Segment Cells
19. Segmentation of cells:
Click on the “+” sign at the bottom next to Adjust Modules. Under the module category, Image processing, Add RescaleIntensitymodule.

Select the input image: ALOD4 

Name the output image: RescaleIntensity_ALOD4 

Rescaling method: Divide each image by the same value 

Divisor value: 0.001 

Rescaling the intensity of images makes it easier to segment cells in the step below.
SECTION: Import files and Segment Cells
20. Add Identifyprimaryobjects module 

In the Identifyprimaryobjects module:
Use advanced settings? Yes 

Select the input image: RescaleIntensity_ALOD4 

Name the primary objects to be identified: cells 

Typical diameter of objects, in pixel units: 95 - 800 

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: 1.6 

Threshold correction factor 1.0 

Lower and upper bounds on threshold 0 and 1.0 

Log transform before thresholding? Yes 

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.
SECTION: Import files and Segment Cells
21. Add OverlayOutlines module

In the OverlayOutlines module: 
Display outlines on a blank image: No 

Select image on which to display outlines: ALOD4 

Name the output image: celloutline 

Outline display mode: Color 

How to outline: Thick 

Select objects to display: cells 

Select outline color: Maraschino
 


 

SECTION: Measure ALOD4 intensity
22. Add MeasureObjectIntensity module 
In the MeasureObjectIntensity module:
Select images to measure: ALOD4 

Select objects to measure: cells 

This module measures ALOD4 intensity in segmented cells.
SECTION: Measure ALOD4 intensity
23. Add MeasureObjectSizeShape module 

In the MeasureObjectSizeShape module:
Select object sets to measure: cells 

Calculate Zernike feature? No 

Calculate the advanced features? No
SECTION: Measure ALOD4 intensity
24. Add 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? Yes 

Representation of Nan/Inf: NaN 

Select measurements to export? Yes
 
Press button to select measurements: Under “cells” choose AreaShape -> Area, and Intensity -> Integrated Intensity.

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: cells Use the object name for the file name? 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 using Excel software. Columns indicate Cell line, image number, area of each cell, and integrated intensity of ALOD4 in each cell.