An automated ImageJ/Fiji and Python pipeline for quantifying the nuclear vs. cytoplasmic distribution of PTBP1 (Polypyrimidine Tract Binding Protein 1) and its RRM2 domain from fluorescence microscopy images of heterokaryon assays.
Nuclear/cytoplasmic ratio distributions across experimental conditions (26,040 cells analyzed)
PTBP1 is an RNA-binding protein with four RNA Recognition Motifs (RRMs) that shuttles between the nucleus and cytoplasm. This project asks whether the RRM2 domain specifically mediates nuclear retention by quantifying the subcellular localization of full-length PTBP1 and domain mutants in heterokaryon assays — a classical approach for testing nucleocytoplasmic shuttling of RNA-binding proteins.
An interactive Dash dashboard allows real-time exploration of PTBP1 localization data — filter by experimental condition, view N/C ratio distributions, and export publication-ready plots.
The workflow consists of two stages:
1. Image segmentation & quantification (ImageJ/Fiji)
- Automated nuclear/cytoplasmic ROI segmentation from fluorescence images
- Per-cell fluorescence intensity extraction
- Batch processing across all 96 images in the dataset
2. Statistical analysis & visualization (Python)
- N/C ratio computation per cell
- Statistical comparisons across experimental conditions (scipy, statsmodels)
- Publication-quality figures (matplotlib, seaborn, Plotly)
- Interactive Dash dashboard for exploratory data analysis
ptbp1-imagej-analysis/
├── data/
│ ├── raw_images/
│ │ ├── BBBC013/images/ # Raw fluorescence microscopy images
│ │ └── BBBC013_v1_platemap_all.txt # Plate map metadata
│ └── processed/ # Analysis outputs (N/C ratios, cell stats)
├── scripts/
│ └── analyze_ptbp1.py # Batch image processing script
├── notebooks/
│ └── analysis.ipynb # Statistical analysis notebook
├── app/
│ └── app.py # Interactive Dash dashboard
├── figures/
│ ├── localization_plots/ # Output plots
│ └── gif/ # Dashboard demo animation
├── directory_structure.json # Data directory manifest
├── requirements.txt # Python dependencies
└── README.md
- Python 3.8+
- ImageJ/Fiji (for image segmentation step)
- Git
git clone https://github.com/ceugenia/ptbp1-imagej-analysis.git
cd ptbp1-imagej-analysis
pip install -r requirements.txtThis pipeline uses the BBBC013 public dataset from the Broad Bioimage Benchmark Collection — fluorescence microscopy images of cells stained for subcellular protein localization. Place the downloaded images in data/raw_images/BBBC013/images/.
The BBBC013 dataset is used here to validate and demonstrate the analytical approach. Results reflect the pipeline's performance rather than specific biological findings about endogenous PTBP1 localization.
Step 1 — Image quantification
Run the batch processing script to extract per-cell fluorescence intensities:
python scripts/analyze_ptbp1.pyOutput CSVs will be written to data/processed/.
Step 2 — Statistical analysis
Open and run the Jupyter notebook for statistical comparisons and figure generation:
jupyter notebook notebooks/analysis.ipynbStep 3 — Interactive dashboard
Launch the Dash app for interactive exploration:
python app/app.pyThen open http://localhost:8050 in your browser.
- Automated analysis of 26,040 cells across 96 fluorescence microscopy images
- Per-cell nuclear/cytoplasmic (N/C) ratio quantification
- Statistical comparisons of localization patterns across experimental conditions
- Initial findings presented at the CSUF Summer Research Symposium (2021)
| Tool | Purpose |
|---|---|
| ImageJ/Fiji | Nuclear/cytoplasmic segmentation |
| OpenCV, scikit-image | Python image processing |
| pandas, numpy | Data wrangling |
| scipy, statsmodels | Statistical testing |
| matplotlib, seaborn, Plotly | Visualization |
| Dash | Interactive dashboard |
Install all Python dependencies with:
pip install -r requirements.txt- Integrate deep learning segmentation (StarDist / Cellpose) for improved nuclear ROI accuracy
- Expand to multi-protein co-localization studies
- Develop a cloud-based pipeline for collaborative use
- Build a public web interface
If you use this pipeline in your work, please cite:
Perez, C.E. (2021). PTBP1 Subcellular Localization ImageJ Analysis Pipeline. GitHub. https://github.com/ceugenia/ptbp1-imagej-analysis
This project is licensed under the MIT License — see the LICENSE file for details.
Developed by Constanza Eugenia Perez (@ceugenia).
Questions or suggestions? Open an issue on GitHub.