PIEZO1-HaloTag Analysis Pipeline

This repository contains a collection of Python scripts and FLIKA plugins for analyzing PIEZO1 protein dynamics in microscopy data, as described in Bertaccini et al. 2024. The tools enable tracking, visualization, and analysis of PIEZO1 protein movement and activity in various cell types. It was created to help the Pathak lab (https://www.pathaklab-uci.com/) analyse TIRF recordings of PIEZO1 using flika (https://flika-org.github.io/).

Overview

The codebase is organized into two main components:

• flika_plugins/: Custom plugins for the FLIKA image processing platform, providing interactive visualization and analysis tools
• flika_scripts/: Command-line Python scripts for batch processing and analysis of PIEZO1 data
• minimal_model/: Test data with results file from running the analysis pipeline

Key Features

• Super-resolution tracking of PIEZO1-HaloTag puncta
• Analysis of protein diffusion and mobility patterns
• Visualization of protein localization and trajectories
• Statistical analysis of protein behavior

Requirements

• Python 3.7+
• FLIKA (https://github.com/flika-org/flika)
• Dependencies:
  • numpy
  • pandas
  • scipy
  • scikit-image
  • trackpy
  • PyQt5

Installation

1. Install FLIKA following the instructions at: https://github.com/flika-org/flika

2. Clone this repository:

git clone https://github.com/gddickinson/pathak_lab.git

3. Install required Python packages:

pip install -r requirements.txt

4. Install the FLIKA plugins:
   • Copy the contents of flika_plugins/ to your FLIKA plugins directory
   • Restart FLIKA to load the new plugins

Usage

The analysis pipeline consists of several steps:

1. Preprocessing (flika_scripts/piezo1_analysis/1_preprocessing/)

   • Convert and prepare microscopy data
   • Bin data by frame

2. Analysis (flika_scripts/piezo1_analysis/2_analysis/)

   • Detect and track PIEZO1 puncta
   • Calculate diffusion coefficients
   • Analyze protein dynamics

3. Postprocessing (flika_scripts/piezo1_analysis/3_postprocessing/)

   • Statistical analysis
   • Data compilation
   • Filtering results

The analysis workflow depends on localization of PIEZO1 puncta using either the flika pynsight plugin (https://github.com/kyleellefsen/pynsight) or other localization software, such as thunderSTORM (https://zitmen.github.io/thunderstorm/), to identify fluorescent 'blobs' in every recording frame. Tracking is carried out using pynsight, and further analysis of point and track data performed on batches of localization files using the scripts. Point and track data can be displayed using the plugins.

Our analysis was run on a PC with the following specifications:

• Processor: Intel(R) Xeon(R) Gold 6134M CPU @ 3.20GHz 3.19 GHz (2 processors)
• Installed RAM: 1.50 TB
• System type: 64-bit operating system

See individual directory READMEs for instructions on each step.

Documentation

• FLIKA Plugins Guide
• Analysis Scripts Guide
• Analysis Pipeline Steps

Contributing

We welcome contributions! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this code in your research, please cite:

Bertaccini et al. (2024). Visualizing PIEZO1 Localization and Activity in hiPSC-Derived Single Cells and Organoids with HaloTag Technology. 
bioRxiv 2023.12.22.573117; doi: https://doi.org/10.1101/2023.12.22.573117

Contact

For questions or issues, please open an issue on GitHub or contact [george.dickinson@gmail.com].