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Imaging Biological Samples with Optical Microscopy

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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.
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Related Experiment Video

Updated: Sep 6, 2025

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

Label-free, non-invasive, and repeatable cell viability bioassay using dynamic full-field optical coherence

Soongho Park¹, Vinay Veluvolu¹, William S Martin¹

¹Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 49 Convent Dr., Bethesda, MD 20814, USA.

Biomedical Optics Express

|July 5, 2022

Summary

This summary is machine-generated.

We developed a new real-time method to assess cell viability using machine learning and intracellular data. This non-invasive technique offers clear quantification, outperforming traditional staining methods.

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Last Updated: Sep 6, 2025

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Viability Assays for Cells in Culture

Viability Assays for Cells in Culture

Published on: January 20, 2014

Area of Science:

Biotechnology
Machine Learning in Biology
Cell Biology

Background:

Cell viability is crucial for disease diagnosis and treatment.
Current cell viability assays, like trypan blue staining, have limitations in clarity and quantification.
Label-free, non-invasive methods are needed for real-time cellular analysis.

Purpose of the Study:

To introduce a novel, real-time method for assaying cellular viability.
To utilize supervised machine learning with label-free intracellular dynamic activity data.
To compare the machine learning approach with traditional trypan blue assays.

Main Methods:

Acquisition of intracellular dynamic activity data in a label-free, non-invasive, and non-destructive manner.
Application of four supervised machine learning models to the acquired data.
Validation of the machine learning models against the trypan blue assay for cell death determination.

Main Results:

The supervised machine learning models achieved a balanced accuracy of 93.92% ± 0.86% in cell death assays.
The developed method provides clear, quantifiable assessment of cell viability.
Demonstrated superior performance compared to the trypan blue assay.

Conclusions:

Supervised machine learning offers a robust and accurate method for real-time cell viability assessment.
This label-free approach overcomes the ambiguity and limitations of staining techniques.
The method holds significant potential for applications in cytology, disease diagnosis, and treatment monitoring.