markdown-output/a-cellprofiler-computational-pipeline-to-quantify-dhja34ie.md

# Goal/Experiment:
To quantify the density of mouse striatal dopaminergic processes using a CellProfiler computational pipeline.

# A CellProfiler Computational Pipeline to Quantify the Density of Mouse Striatal Dopaminergic Processes

### DOI
[dx.doi.org/10.17504/protocols.io.x54v92km4l3e/v1](https://dx.doi.org/10.17504/protocols.io.x54v92km4l3e/v1)

### Authors
- **Ebsy Jaimon**
- **Sreeja V Nair**
- **Suzanne R Pfeffer**

**Department of Biochemistry, Stanford University School of Medicine and Aligning Science Across Parkinson's**

### Protocol Citation
_Ebsy Jaimon, Sreeja V Nair, Suzanne R Pfeffer 2024. A CellProfiler computational pipeline to quantify the density of mouse striatal dopaminergic processes. **protocols.io**_

### License
This is an open access protocol distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

### Protocol Status
Working

### Created
July 19, 2024

### Last Modified
July 19, 2024

### Protocol Integer ID
103746

### Keywords
- ASAPCRN
- LRRK2
- Primary cilia
- Dorsal striatum

### Funders Acknowledgement
Aligning Science Across Parkinson's - Grant ID: ASAP-000463

## Abstract
Here, we present a CellProfiler software pipeline to quantify the density and intensity of dopaminergic processes in the mouse striatum. The dopaminergic processes in the striatum are stained using an anti-tyrosine hydroxylase antibody. The same sections are stained using an antibody that recognizes total neuronal NeuN (neuronal nuclear antigen biomarker) for staining normalization. For the examples shown herein, images were acquired using a Zeiss LSM 900 laser scanning confocal microscope with a 63X 1.4 oil immersion objective.

*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. PMID: 34507520 PMCID: PMC8431850.

## Materials
1. `.czi` files from Zeiss Laser Scanning Microscope
2. FIJI/ImageJ
3. CellProfiler software 4.0+ 

## Methods

### Batch Process Images
1. Use the FIJI macro as described in [dx.doi.org/10.17504/protocols.io.3byl4bpo8vo5/v1](https://dx.doi.org/10.17504/protocols.io.3byl4bpo8vo5/v1) to Z-project the `.czi` images from the Zeiss LSM microscope.
2. Open the images that need to be processed, choose the output folder, run the code for maximum intensity Z projection, and save the file as .TIFF.

### Import Files and Extract Metadata
1. Open CellProfiler. Go to the Images module, drag and drop the maximum intensity projected .TIFF files as indicated. Select "no filtering" in the filter images option.
2. Go to the Metadata module:
   - Set _Extract Metadata?_ to Yes.
   - Set _Metadata extraction method_ to Extract from image file headers.
   - Set _Extract metadata from_ to All images.
   - Click _Extract metadata_.
   - Click on Add another extraction method.
   - Set _Metadata extraction method_ to Extract from file/folder names.
   - Set _Metadata source_ to File name.
   - Set Regular expression to extract from file name:
     ```
     ^.*br(?P<brain_number>[0-9]{1,2}).*#(?P<image_number>[0-9]{2})
     ```
        Note these steps:
        - In Regex, ^ indicates the beginning of the file name.
        - The program recognizes and extracts brain and image numbers.
   - Click _update_ to populate the metadata field.
   - Set _Metadata data type_ to Text.
3. Go to the NamesAndTypes module:
   - Assign a name to images 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: TyrosineHydroxylase
   - Set the image type: Grayscale image
   - Click on Add another image and set similar parameters for NeuN staining.

### Density Measurement
1. To binarize the images:
   - Select the input image: TyrosineHydroxylase
   - Name the output image: Thresholded_TyrosineHydroxylase
   - Threshold strategy: Global
   - Thresholding method: Minimum Cross-Entropy
   - Set threshold scales and corrections as required.

Note: Use test settings to ensure the best results.

2. Add `MeasureImageAreaOccupied` module:
   - Measure the area occupied by: Binary image.
   - Select binary images to measure: Thresholded_TyrosineHydroxylase.

### Intensity Measurement
1. To segment Tyrosine Hydroxylase and NeuN:
   - Add IdentifyPrimaryObjects module.
   - Use advanced settings: Yes.
   - Segment objects according to parameters.
   - Adjust modules and test mode settings.

2. Segment NeuN:
   - Add Smooth module, then IdentifyPrimaryObjects for segmentation.
   - Similar advanced settings as previous steps.

3. Add modules MeasureObjectIntensity and MeasureObjectSizeShape to measure the integrated intensity and area of the objects (THoverjects and NeuNobject).

### Export Data
- Add ExportToSpreadsheet module:
  - Select measurements to export under specific categories and criteria.
  - Save the pipeline and run the analysis.

## 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. PMID: 34507520 PMCID: PMC8431850.

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