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Updated: May 30, 2025

Multiplex Detection of Bacteria in Complex Clinical and Environmental Samples using Oligonucleotide-coupled Fluorescent Microspheres
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Multiplexed Pathogenic Bacteria Detection via a Two-Dimensional Encoded Fluorescent Microsphere System.

Mengjiao Wang1,2, Letian Li1,3, Luyu Wei2

  • 1State Key Laboratory of Marine Food Processing and Safety Control, Dalian Polytechnic University, Dalian 116034, Liaoning, China.

Nano Letters
|January 31, 2025
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Summary
This summary is machine-generated.

This study introduces a new microscopy platform for rapid, amplification-free detection of pathogenic bacteria in food and clinical samples. The advanced system offers sensitive and precise identification, improving food safety and diagnostics.

Keywords:
Clostridium butyricum Argonautecomputer vision technologymultiplexed detectionpathogenic bacteriaprogrammable microscopy imaging

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Area of Science:

  • Biotechnology
  • Microscopy
  • Molecular Diagnostics

Background:

  • Current methods for detecting pathogenic bacteria often require DNA extraction and amplification, which are time-consuming and complex.
  • Multiplex detection of multiple bacterial species simultaneously is challenging, limiting comprehensive analysis in food and clinical settings.

Purpose of the Study:

  • To develop an advanced, amplification-free microscopy imaging platform for multiplex detection of pathogenic bacteria.
  • To enable rapid and sensitive identification of bacterial pathogens directly from food and clinical samples without DNA extraction.

Main Methods:

  • Utilized two-dimensional encoded polystyrene microspheres with spectrally distinct fluorophores and varying particle sizes for multiplexed signal libraries.
  • Employed a tetrahedral DNA-enhanced hybridization chain reaction (TDNA-HCR) for signal amplification and reduced reaction times.
  • Integrated an Argonaute-based decoding system with aptamer-specific recognition for pathogen identification and quantification via DNA cleavage.

Main Results:

  • Achieved a 67% reduction in reaction time due to TDNA-HCR, significantly enhancing signal intensity.
  • Demonstrated sensitive detection of pathogenic bacteria at concentrations as low as 10^2 CFU/mL within 1.5 hours.
  • Developed a computer vision algorithm for processing signals encoded in color, size, and count of fluorescent probes.

Conclusions:

  • The developed platform offers a novel, rapid, and sensitive approach for amplification-free, multiplex bacterial detection.
  • This technology holds significant potential for improving food safety monitoring and clinical diagnostic applications.