BioluminescenceAssay

This repository contains the code and resources for the paper "Ultrasensitive and Long-lasting Luminescence Cascade Sensor for Point of Care Viral Pathogen Detection".

Abstract

Bioluminescence holds notable promise as a modality in diagnostics due to its high signal-to-noise ratio and absence of incident radiation. However, challenges arise from rapid signal decay and reduced enzyme activity when linked to targeting molecules, limiting its reliability in point-of-care diagnostic applications. Here, we introduce LUCAS, an enzyme cascade system capable of detecting analytes with ultrahigh sensitivity and prolonged bioluminescence. By employing an enzyme that retains its activity when conjugated to an antibody, our assay achieves more than a 500-fold increase in bioluminescence signal and maintains an 8-fold improvement in signal persistence compared to conventional bioluminescence assays. Implemented on the fully automated LUCAS, our system facilitates rapid (< 23 min) sample-to-answer analysis of viruses without an external power supply. Its accuracy surpasses 94% in the qualitative classification of 177 viral-infected patient samples and 50 viral-spiked serum samples, various pathogens including the respiratory virus SARS-CoV-2, and blood-borne pathogens such as HIV, HBV, and HCV as clinical models. The decentralized, rapid, sensitive, specific, and cost-effective nature of LUCAS positions it as a viable diagnostic tool for low-resource environments.

System Requirements

Software Dependencies

• Python: Version 3.7 or higher
• Libraries:
  • opencv-python
  • numpy
  • scipy
  • matplotlib
  • pandas
  • flask
  • serial

Operating Systems

• Windows: Tested on Windows 10
• Linux: Tested on Ubuntu 20.04
• macOS: Tested on macOS Big Sur

Hardware Requirements

• Standard Desktop or Laptop: No special hardware requirements
• Optional: Raspberry Pi 4 for portable operation
• Optional: CMOS sensor for bioluminescence detection

Installation Guide

Instructions

1. Clone the Repository:

   git clone https://github.com/shafieelab/BioluminescenceAssay.git
   cd BioluminescenceAssay

2. Set Up Python Environment:

   • Create and activate a virtual environment (optional but recommended):

     python3 -m venv env
     source env/bin/activate  # On Windows use `env\Scripts\activate`

3. Install Dependencies:

   pip install -r requirements.txt

Typical Install Time

• Approximately 5-10 minutes on a standard desktop computer.

Python

Instructions to Run on Sample Data using Python

1. Navigate to the PythonScript Directory:

   cd PythonScript

2. Run the Demo Script:

   python IntensityCalcCode.py

Pseudo Code

Algorithm BrightnessDetection
    Input: img
    Output: yellow_brightness

    // Step 1: Define Region of Interest
    top ← 250
    right ← 1150
    height ← 900
    width ← 1600
    img_roi ← crop_image(img, top, right, height, width)

    // Step 2: Convert to HSV
    hsv_img ← convert_to_hsv(img_roi)

    // Step 3: Define Yellow Boundaries
    lower_bound ← [20, 50, 50]
    upper_bound ← [50, 255, 255]

    // Step 4: Create Mask for Yellow
    mask ← in_range(hsv_img, lower_bound, upper_bound)

    // Step 5: Remove Noise from Mask
    kernel ← create_kernel(7, 7)
    mask ← morphological_close(mask, kernel)
    mask ← morphological_open(mask, kernel)

    // Step 6: Segment Yellow Regions
    segmented_img ← bitwise_and(hsv_img, hsv_img, mask)

    // Step 7: Calculate Brightness
    yellow_brightness ← sum(segmented_img[:, :, 2])

    return yellow_brightness
End Algorithm

Expected Output

• The demo script will process the sample data and display bioluminescence intensity values.

Expected Run Time

• Approximately 2-3 seconds on a standard desktop computer for ~20 images. Runtime varies with number of images.