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Imaging Biological Samples with Optical Microscopy

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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Design and Implementation of an Automated Illuminating, Culturing, and Sampling System for Microbial Optogenetic Applications

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Automated Open-Hardware Multiwell Imaging Station for Microorganisms Observation.

Alain Gervasi¹, Pierre Cardol¹, Patrick E Meyer²

¹Genetics and Physiology of Microalgae, InBios/Phytosystems, Institut de Botanique, University of Liège, 4000 Liege, Belgium.

|June 24, 2022

Summary

This summary is machine-generated.

Researchers developed an automated microscope system for rapid cell and tissue analysis. This open-hardware device uses 3D printing and computer vision for high-throughput imaging and analysis with minimal human intervention.

Keywords:

IoT bio-automation edge computing microbiology microscopy open-hardware

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Published on: June 26, 2014

Area of Science:

Biotechnology
Microscopy
Automation

Background:

Bright field microscopy is crucial for biological sample analysis.
Current microscopic analyses are often time-consuming and difficult to parallelize.
Limited commercial solutions offer integrated automated sample handling and analysis.

Purpose of the Study:

To develop a stand-alone, automated microscope system.
To enable high-throughput imaging and analysis of biological samples.
To create an open-hardware, modular, and cost-effective device.

Main Methods:

Fabrication of an automated microscope using 3D printed pumps.
Integration of a digital camera for imaging up to 50× optical magnification.
Implementation of computer vision algorithms on a single board computer for image analysis.
Utilizing open-source components like Arduino and Raspberry Pi.

Main Results:

The device can collect and scan up to 24 samples in under 10 minutes.
Automated sample collection, imaging, and analysis are performed with minimal human intervention.
The system integrates multiple functionalities not found in single commercial instruments.
The open-hardware design allows for free reproduction at a competitive price.

Conclusions:

The proposed automated microscope offers a novel solution for efficient biological sample analysis.
The open-hardware approach promotes accessibility and further development in automated microscopy.
This device significantly enhances throughput and reduces manual labor in cell and tissue observation.