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Imaging Biological Samples with Optical Microscopy01:18

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...
Immunofluorescence Microscopy01:12

Immunofluorescence Microscopy

A fluorescence microscope uses fluorescent chromophores called fluorochromes, which can absorb energy from a light source and then emit this energy as visible light. Fluorochromes include naturally fluorescent substances (such as chlorophylls) and fluorescent stains that are added to the specimen to create contrast. Dyes such as Texas red and FITC are examples of fluorochromes. Other examples include the nucleic acid dyes 4’,6’-diamidino-2-phenylindole (DAPI), and acridine orange.
The...

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Related Experiment Video

Updated: May 14, 2026

Improved Visualization and Quantitative Analysis of Drug Effects Using Micropatterned Cells
15:41

Improved Visualization and Quantitative Analysis of Drug Effects Using Micropatterned Cells

Published on: December 2, 2010

ICOBA: A highly customizable iterative imagej macro for optimization of image co-localization batch analysis.

Tyler J Rolland1, Emily R Hudson1, Luke A Graser2

  • 1Department of Physiology & Biophysics at the University at Buffalo and the VA WNY Healthcare System, Buffalo, NY, USA.

Softwarex
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

We developed ICOBA, an ImageJ macro for automated multi-channel co-localization analysis. This tool enhances throughput and reproducibility for protein interaction studies, outperforming manual methods.

Keywords:
Co-localizationICOBAImage analysisImageJ macro

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Last Updated: May 14, 2026

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Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
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Published on: February 15, 2022

Area of Science:

  • Biomedical Research
  • Microscopy
  • Image Analysis

Background:

  • Co-localization analysis is crucial for studying protein interactions.
  • Existing ImageJ/FIJI plugins often lack automated multi-channel capabilities, limiting throughput and introducing bias.

Purpose of the Study:

  • Introduce ICOBA (Iterative Channel Overlay Batch Analysis), a novel ImageJ macro.
  • Streamline and standardize multi-channel co-localization workflows for large datasets.

Main Methods:

  • Developed ICOBA as a freely available ImageJ macro.
  • Utilized ImageJ's recording functionality and customizable scripting.
  • Validated with immunostained cardiac fibroblasts imaged on a Leica DMi8 microscope.

Main Results:

  • ICOBA significantly reduces processing times for single- and two-channel analyses compared to manual methods.
  • Quantitative accuracy is maintained without sacrificing speed.
  • The macro offers flexibility for variable staining and threshold parameters.

Conclusions:

  • ICOBA provides a versatile, reproducible, and flexible solution for high-throughput co-localization.
  • It is suitable for diverse research fields, from basic lab work to advanced tissue imaging.