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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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Related Experiment Video

Updated: Oct 12, 2025

Author Spotlight: Introducing the Tile/SED/Array Interface for Rapid Field of View Positioning in Tissue Imaging

Author Spotlight: Introducing the Tile/SED/Array Interface for Rapid Field of View Positioning in Tissue Imaging

Published on: September 15, 2023

MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging.

Denis Schapiro^1,2,3,4, Artem Sokolov^1,2,5, Clarence Yapp^1,2,6

¹Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA.

|November 26, 2021

Summary

This summary is machine-generated.

We developed MCMICRO, an open-source computational pipeline for analyzing highly multiplexed tissue images. This pipeline transforms whole-slide images into single-cell data, addressing significant computational challenges in spatial biology research.

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Last Updated: Oct 12, 2025

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Published on: September 15, 2023

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Area of Science:

Computational Biology
Bioinformatics
Pathology

Background:

Highly multiplexed tissue imaging enables detailed molecular analysis of single cells within their spatial context.
Analyzing large, multichannel tissue images presents significant computational challenges for reproducible research.

Purpose of the Study:

To introduce MCMICRO, a modular, open-source computational pipeline for processing multiplexed tissue images.
To provide a robust solution for transforming whole-slide images into single-cell data.

Main Methods:

Development of a sequential computational pipeline (MCMICRO).
Application of the pipeline to diverse tissue and tumor image datasets.
Demonstration across multiple imaging platforms.

Main Results:

MCMICRO successfully processes whole-slide images into single-cell data.
The pipeline is validated on various tissue types and imaging modalities.
Demonstrates the feasibility of reproducible computational analysis for large-scale imaging data.

Conclusions:

MCMICRO offers a foundational tool for the analysis of highly multiplexed tissue imaging data.
The open-source nature facilitates further development in tissue imaging software.
Enables robust single-cell analysis in spatial context, advancing biological insights.