DAPI Cell Counting and Invasion Analysis Tool

Overview

This tool automates the analysis of microscopy images of DAPI-stained cells on Boyden chamber membranes.

System Requirements

• Operating System: Linux, macOS, or Windows
• Python Version: Python 3.8 or higher
• Required Libraries:
  • OpenCV (cv2)
  • NumPy
  • scikit-image (skimage)
  • Matplotlib
  • Pandas
  • Tkinter (built-in with Python for most OS)

Ensure all dependencies are installed using the requirements.txt file (see below).

Installation Guide

1. Clone the repository:

   git clone https://github.com/GreletLab/DAPI-cell-analysis.git
   cd DAPI-cell-analysis

2. Install the required Python libraries:

   pip install -r requirements.txt

How to Use

1. Load Images:

   • Run the script using Python:

     python DAPI_counting_v1.py

   • Use the GUI to select the folder containing microscopy images.

2. Adjust Threshold:

   • Use the slider in the GUI to adjust the threshold value for cell detection.

3. Analyze Images:

   • The script processes each image, identifies cells based on DAPI staining, and counts the number of cells per field.

4. Export Results:

   • The output is saved as a CSV file containing cell counts and additional metrics for each analyzed image.

Input and Output

• Input: A folder of microscopy images (supported formats: .png, .jpg, .tiff).
• Output: A CSV file summarizing the number of cells per image and their invasion metrics.

Example

1. Place your images in a folder, e.g., ./images/.
2. Run the script and select the folder through the GUI.
3. Adjust the threshold to match the staining intensity.
4. View the results in the exported CSV file, e.g., results.csv.

Typical Run Time

• Per Image: Less than 5 seconds, depending on image resolution.
• Batch Processing: Time scales linearly with the number of images.