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Related Experiment Video

Updated: Oct 19, 2025

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

Protocol for multicolor three-dimensional dSTORM data analysis using MATLAB-based script package Grafeo.

Kalina Tamara Haas¹, Alexis Peaucelle¹

¹Institut Jean-Pierre Bourgin, INRAE, AgroParisTech, Université Paris-Saclay, 78000 Versailles, France.

|September 20, 2021

Summary

This summary is machine-generated.

This protocol details the Grafeo software for analyzing multicolor super-resolution microscopy data. It employs various spatial analysis methods for advanced visualization and clustering of direct Stochastic Optical Reconstruction Microscopy (dSTORM) datasets.

Keywords:

Bioinformatics Biophysics Developmental biology Microscopy Plant sciences

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Test Samples for Optimizing STORM Super-Resolution Microscopy

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Last Updated: Oct 19, 2025

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

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Published on: September 28, 2019

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Test Samples for Optimizing STORM Super-Resolution Microscopy

Test Samples for Optimizing STORM Super-Resolution Microscopy

Published on: September 6, 2013

Area of Science:

Biophysics
Microscopy techniques
Computational biology

Background:

Direct Stochastic Optical Reconstruction Microscopy (dSTORM) generates high-resolution images.
Analyzing complex multicolor dSTORM data requires sophisticated computational tools.
Existing analysis methods may not fully capture spatial relationships in multicolor dSTORM datasets.

Purpose of the Study:

To present a comprehensive protocol for analyzing multicolor 2D and 3D dSTORM data.
To introduce and demonstrate the capabilities of the MATLAB-based Grafeo software package.
To provide researchers with a robust framework for quantitative spatial analysis of super-resolution microscopy data.

Main Methods:

Utilized MATLAB-based script package Grafeo for data analysis.
Employed pointillist data visualization and analysis frameworks.
Applied methods including nearest neighbors, Voronoi tessellation, Delaunay triangulation, Ripley's functions, and graph-based clustering.

Main Results:

The Grafeo package enables step-by-step analysis of multicolor (1-, 2-, or 3-color) 2D and 3D dSTORM data.
Demonstrated the application of various spatial statistical methods for dSTORM data interpretation.
Provided a reproducible protocol for advanced quantitative analysis of super-resolution microscopy datasets.

Conclusions:

Grafeo offers a powerful and versatile platform for the analysis of complex dSTORM data.
The protocol facilitates deeper insights into the spatial organization of cellular structures at the nanoscale.
This work supports the advancement of quantitative super-resolution microscopy analysis.