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Related Concept Videos

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.

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Related Experiment Video

Updated: May 7, 2026

Test Samples for Optimizing STORM Super-Resolution Microscopy
16:52

Test Samples for Optimizing STORM Super-Resolution Microscopy

Published on: September 6, 2013

Test samples for optimizing STORM super-resolution microscopy.

Daniel J Metcalf1, Rebecca Edwards, Neelam Kumarswami

  • 1Analytical Science Division, National Physical Laboratory.

Journal of Visualized Experiments : Jove
|September 24, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces test samples and methods to optimize Super-Resolution Optical Microscopy (STORM) imaging. These tools help users improve image quality and resolution in STORM microscopy.

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Last Updated: May 7, 2026

Test Samples for Optimizing STORM Super-Resolution Microscopy
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Published on: September 6, 2013

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Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy

Published on: March 6, 2018

Area of Science:

  • Microscopy
  • Biotechnology
  • Optical Imaging

Background:

  • Super-resolution microscopy techniques like STORM offer significantly higher resolution than conventional methods.
  • STORM imaging, which builds images molecule-by-molecule, presents unique challenges for optimizing image acquisition.
  • Understanding and overcoming these challenges are crucial for leveraging STORM's full potential.

Purpose of the Study:

  • To provide a standardized methodology for acquiring and processing STORM super-resolution images.
  • To develop test samples that aid in understanding STORM imaging principles and optimizing acquisition parameters.
  • To offer insights into common issues affecting image quality in STORM microscopy.

Main Methods:

  • Preparation of three distinct test samples for STORM microscopy.
  • Detailed methodology for acquiring STORM super-resolution images with resolutions of 30-50 nm.
  • Utilizing the freely available rainSTORM software for image processing and analysis.

Main Results:

  • Demonstration of how test samples and rainSTORM software provide metrics for image quality and resolution assessment.
  • Identification of key parameters for optimizing STORM imaging, including optics, sample preparation, dye choice, buffer conditions, and acquisition settings.
  • Examples of common artifacts like lateral drift and density-related mislocalization that degrade image quality.

Conclusions:

  • The presented test samples and methodology facilitate the optimization of STORM super-resolution microscopy.
  • rainSTORM software and derived metrics are valuable tools for assessing and improving STORM image acquisition.
  • Addressing common problems such as drift and density issues is essential for achieving high-quality STORM images.