Live-Dead-Analysis

🔬 Quantitative Analysis of DAPI+ Cells and Caspase-3 Activation

This repository contains two complementary scripts designed to analyze confocal microscopy data:

1. Estimate the average size of DAPI+ cells from randomly sampled patches. - Use Single Cell Area Calculation
2. Measure DAPI and Caspase-3 (clCASP3) stain coverage and intensity across .lif image series. - Use Stain Area Coverage Analysis
3. Normalize Caspase-3 intensity by DAPI cell size to get per-cell apoptotic burden.

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🧪 1. Estimating Average DAPI+ Cell Area

Script: Single Cell Area Calculation.py

This script:

• Loads a .lif file with DAPI-stained nuclei.
• Extracts non-overlapping random patches from the DAPI channel.
• Segments each patch using the CellSAM deep-learning model.
• Counts objects (nuclei) and total DAPI+ area per patch.
• Saves masks and an Excel summary.

📤 Output:

• Labeled TIFF masks per patch
• Excel file with:
  • Number of DAPI+ cells per patch
  • Total DAPI+ intensity per patch

✅ Final Result:

Use this to compute the average area per DAPI+ nucleus:

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🔍 2. DAPI & Caspase-3 Area Coverage

Script: Stain Area Coverage Analysis.py

This script:

• Loads a .lif file with DAPI and clCASP3 channels.
• Computes max-intensity projections (MIPs).
• Extracts 10 random non-overlapping patches per image series.
• Applies Otsu thresholding on each channel.
• Calculates area (µm²) and intensity of DAPI+ and Caspase-3+ pixels.
• Saves overlays and exports a detailed Excel report.

📤 Output:

• Binary masks for each patch
• RGB overlay images (R = Caspase-3, B = DAPI)
• Excel file with:
  • DAPI and Caspase-3 area and intensity per patch

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📊 3. Final Step: Normalizing Caspase-3 Intensity

With both Excel files:

1. From estimate_dapi_cell_area.py: compute the mean DAPI+ cell area.
2. From stain_area_coverage_analysis.py: use Caspase-3 total intensity per patch.
3. Normalize clCASP3 intensity per cell:

CellSAM is used in this code, find more here: @article{israel2023foundation, title={A Foundation Model for Cell Segmentation}, author={Israel, Uriah and Marks, Markus and Dilip, Rohit and Li, Qilin and Schwartz, Morgan and Pradhan, Elora and Pao, Edward and Li, Shenyi and Pearson-Goulart, Alexander and Perona, Pietro and others}, journal={bioRxiv}, publisher={Cold Spring Harbor Laboratory Preprints}, doi = {10.1101/2023.11.17.567630}, }