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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.
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: May 5, 2026

4D Imaging of Protein Aggregation in Live Cells

4D Imaging of Protein Aggregation in Live Cells

Published on: April 5, 2013

SIMBA: an agentic AI platform for single-molecule multidimensional imaging.

Hongjing Mao^1,2, Harsh Mauny³, Obblivignes KanchanadeviVenkataraman^1,3

¹Molecular Analytics and Photonics (MAP) Lab, North Carolina State University, Raleigh, NC 27606, USA.

Biorxiv : the Preprint Server for Biology

|May 4, 2026

Summary

This summary is machine-generated.

We developed SIMBA, an AI platform for multi-dimensional bioimaging. It unifies single-molecule localization, spectral analysis, and deep learning denoising, simplifying complex microscopy data analysis for researchers.

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Last Updated: May 5, 2026

4D Imaging of Protein Aggregation in Live Cells

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Published on: April 5, 2013

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

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Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Area of Science:

Biophysics
Microscopy
Computational Biology

Background:

Super-resolution fluorescence microscopy advances enable functional, multi-dimensional imaging.
Challenges include fragmented analysis, complex tuning, and limited computational integration.

Purpose of the Study:

Introduce SIMBA, an AI-driven platform for unified single-molecule multi-dimensional bioimaging.
Simplify and enhance the analysis of complex microscopy data.

Main Methods:

Developed SIMBA, an agentic AI platform integrating single-molecule localization, spectral processing, and deep learning denoising.
Utilized large language model agents for user intent interpretation and automated pipeline orchestration.
Implemented supervised learning for spectral analysis and denoising.

Main Results:

SIMBA supports standard single-molecule localization workflows.
Enabled functional mapping of nanoscale heterogeneity via spectral analysis.
Demonstrated effective denoising using supervised learning methods.

Conclusions:

SIMBA offers a new paradigm for intelligent microscopy analysis.
Lowers adoption barriers for multi-dimensional imaging.
Enables scalable, reproducible, and adaptive analysis of complex bioimaging datasets.