CaBLAM Repository

A comprehensive repository containing analysis tools, validation data, and research materials for CaBLAM! (Calcium Bioluminescent Activity Monitor), a high-contrast bioluminescent Ca²⁺ indicator derived from an engineered Oplophorus gracilirostris luciferase.

📖 Publication

This repository accompanies the research paper:

Lambert et al., CaBLAM! A high-contrast bioluminescent Ca²⁺ indicator derived from an engineered Oplophorus gracilirostris luciferase

• Preprint: https://www.biorxiv.org/content/10.1101/2023.06.25.546478v3

🗂️ Repository Structure

📊 cablam_in_vivo_analysis/

MATLAB scripts for in vivo mouse data analysis

Contains MATLAB scripts to reproduce figures and statistics from the in vivo mouse experiments. Includes analysis for:

• Infusion experiments
• Long-duration recordings
• Running wheel experiments
• Trial-based analysis

Quick Start:

1. Download demo data from: https://doi.org/10.26300/7sg5-w257
2. Place demo_data folder inside cablam_in_vivo_analysis/
3. Open run_all_in_vivo.m in MATLAB
4. Set path variable and run

Requirements: MATLAB R2024b with Image Processing and Statistics toolboxes

🔬 CFz&FFz_validation_in_N2a/

Python analysis for N2a cell validation experiments

Corresponds to Supplementary Figures 7 and 8, containing:

• n2a_biolum_imaging/: Bioluminescence imaging analysis
  • Background subtraction and trace viewing
  • Luminescence timecourse analysis with Bessel filtering
  • Dot plot generation for statistical comparisons
  • Timecourse figure generation
• substrate_dose_response/: Dose-response curve analysis

Quick Start:

cd CFz&FFz_validation_in_N2a
pip install -r requirements.txt
# Run Jupyter notebooks in notebooks/ directory

🧪 Titration/

Python toolkit for calcium sensor titration analysis

Advanced analysis tool for calcium sensor characterization with realistic uncertainty estimation.

Key Features:

• Direct contrast calculation from low-Ca data points
• Global Hill fits for multiple sensors
• Publication-ready plots with auto-optimized styling
• Comprehensive Excel output with detailed statistics

Quick Start:

cd Titration
pip install matplotlib numpy pandas scipy openpyxl
python3 titration_fit_global.py data/*.csv --out-prefix my_analysis

Example Results:

Sensor	EC50 (nM)	Contrast	Uncertainty
CaMBI	59.6 ± 5.9	4.73	±0.29
CaBLAM	439.3 ± 13.9	83.0	±9.4
CaBLAM_294W	3066.5 ± 94.6	748.1	±27.2

🐟 zebrafish data & code/

Zebrafish experimental data and analysis

Contains experimental data from zebrafish studies with corresponding analysis scripts.

🚀 Getting Started

Prerequisites

For MATLAB analysis:

• MATLAB R2024b or later
• Image Processing Toolbox
• Statistics and Machine Learning Toolbox

For Python analysis:

• Python 3.7+
• See individual requirements.txt files in each directory

Installation

1. Clone the repository:

   git clone <repository-url>
   cd CaBLAM_repo

2. Install Python dependencies:

   # For CFz&FFz validation
   cd CFz&FFz_validation_in_N2a
   pip install -r requirements.txt
   
   # For titration analysis
   cd ../Titration
   pip install matplotlib numpy pandas scipy openpyxl

3. Download demo data:

   • In vivo data: https://doi.org/10.26300/7sg5-w257
   • Place in appropriate directories as described in individual README files

📋 Usage Examples

In Vivo Analysis (MATLAB)

% Set path to cablam_in_vivo_analysis folder
pth = 'C:\Users\user\Desktop\cablam_in_vivo_analysis\';
run('run_all_in_vivo.m');

Titration Analysis (Python)

# Analyze all sensors
python3 titration_fit_global.py data/*.csv --out-prefix all_sensors

# Custom calcium threshold
python3 titration_fit_global.py data/CaBLAM_294W.csv --ca-threshold 50 --out-prefix CaBLAM_294W_50nM

N2a Validation (Python/Jupyter)

# Start Jupyter notebook
jupyter notebook notebooks/
# Open background_subtraction_&_traces_viewer.ipynb

🤝 Contributing

This repository is released under the CC0 1.0 Universal license, allowing unrestricted use, modification, and distribution.

📞 Contact

For questions about the CaBLAM sensor or analysis tools, please refer to the original publication or contact the corresponding authors.

📚 Related Resources

• Demo Data: https://doi.org/10.26300/7sg5-w257
• Preprint: https://www.biorxiv.org/content/10.1101/2023.06.25.546478v3
• Individual README files in each directory for detailed usage instructions

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This repository contains all analysis tools and data necessary to reproduce the results presented in the CaBLAM publication.