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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.
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Related Experiment Video

Updated: Jun 18, 2026

Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

Published on: April 7, 2014

PAVR: High-Resolution Cellular Imaging via a Physics-Aware Volumetric Reconstruction Framework.

Xuanwen Hua, Keyi Han, Zhi Ling

Biorxiv : the Preprint Server for Biology

|March 23, 2026

Summary

This summary is machine-generated.

PAVR is a new imaging platform that combines physics and AI for fast 3D cell imaging. It overcomes previous limitations, enabling detailed visualization of cellular dynamics without needing extra training data.

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Last Updated: Jun 18, 2026

Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

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Published on: April 7, 2014

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

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Published on: February 23, 2018

Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization

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Published on: October 29, 2019

Area of Science:

Cell Biology
Microscopy
Deep Learning
Biophysics

Background:

Advanced microscopy and deep learning are revolutionizing cell biology.
High-resolution 3D cell imaging is limited by reconstruction speed, artifacts, and training data requirements.

Purpose of the Study:

Introduce PAVR, a physics-aware light-field imaging platform.
Enable fast, end-to-end volumetric reconstruction for cellular imaging.
Overcome limitations of current 3D imaging techniques.

Main Methods:

Developed PAVR, integrating single-shot volumetric acquisition with computational reconstruction.
Trained the system using in silico responses, eliminating the need for external ground-truth data.
Applied the platform to fixed and live mammalian cells, including cardiomyocytes.

Main Results:

Demonstrated multicolor volumetric imaging of subcellular organelles.
Achieved 3D tracking of autofluorescent particles and visualization of organelle dynamics.
Quantified coupled morphological and functional dynamics in cardiomyocytes under drug treatment.

Conclusions:

PAVR provides a scalable hardware-software solution for high-throughput 3D cellular imaging.
Enables quantitative analysis of dynamic cellular systems in research and clinical settings.
Facilitates sample-independent reconstruction across diverse biological applications.