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Microbial Biosensors

Microbial Biosensors

Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...

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Updated: May 3, 2026

Fluorescence detection methods for microfluidic droplet platforms

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Published on: December 10, 2011

A microfluidic microalgae detection system for cellular physiological response based on an object detection

Shizheng Zhou^1,2, Tianhui Chen¹, Edgar S Fu³

¹School of Computer Science and Technology, Hainan University, Haikou 570228, China. yanhong@hainanu.edu.cn.

|April 29, 2024

Summary

This summary is machine-generated.

This study introduces a novel computer vision and microfluidics method for high-throughput, single-cell analysis of microalgae, crucial for marine environment monitoring and coral health. The optimized model achieves over 95% accuracy in identifying Symbiodiniaceae cell states and thermal responses.

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Last Updated: May 3, 2026

Fluorescence detection methods for microfluidic droplet platforms

Fluorescence detection methods for microfluidic droplet platforms

Published on: December 10, 2011

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses

In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses

Published on: December 5, 2019

Area of Science:

Marine biology
Microalgal ecology
Computational biology

Background:

Microalgal species composition and physiological status are key marine environmental indicators.
Symbiodiniaceae, symbiotic with corals, are sensitive to environmental changes.
Current population-based methods lack single-cell resolution, missing cellular heterogeneity and state transitions.

Purpose of the Study:

To develop an automated, high-throughput method for label-free, single-cell microalgae identification and physiological state assessment.
To overcome limitations of population-based analyses by focusing on individual cell dynamics.
To investigate the thermal sensitivity and novel transition states of Symbiodiniaceae.

Main Methods:

Integration of computer vision, microfluidics, microscopic image processing, and convolutional neural networks.
Optimization of data handling, training protocols, and model architecture for micron-scale object identification.
Development of a sheathless, automated system for microalgae detection and analysis.

Main Results:

Achieved >95% mean average precision for microalgal multi-classification and physiological state assessment.
Successfully identified a novel microalgal transition state.
Characterized the thermal sensitivity of three Symbiodiniaceae clades, observing cellular heat shock responses at high temperatures.

Conclusions:

The developed method enables precise, high-throughput single-cell analysis of microalgae.
Findings on Symbiodiniaceae thermal sensitivity and transition states offer insights into coral ecosystem health and early warning systems.
This technology advances microalgal research and environmental monitoring capabilities.