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Super-resolution Fluorescence Microscopy

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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...

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Updated: Feb 19, 2026

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

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

Published on: May 30, 2016

Super-Resolution Structured Illumination Microscopy.

Rainer Heintzmann^1,2, Thomas Huser^3,4

¹Leibniz Institute of Photonic Technology , Albert-Einstein Straße 9, 07745 Jena, Germany.

Chemical Reviews

|November 11, 2017

Summary

This summary is machine-generated.

Super-resolved structured illumination microscopy (SR-SIM) offers diffraction-limit-breaking resolution with simple sample prep. This review covers SR-SIM

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Last Updated: Feb 19, 2026

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Super-resolution Imaging of the Cytokinetic Z Ring in Live Bacteria Using Fast 3D-Structured Illumination Microscopy f3D-SIM

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Area of Science:

Biophysics
Optical Microscopy
Cell Biology

Background:

Super-resolved structured illumination microscopy (SR-SIM) is a rapidly advancing fluorescence microscopy technique.
SR-SIM surpasses the optical diffraction limit, enabling higher resolution imaging.
It integrates seamlessly with conventional fluorescence microscopy workflows, requiring minimal sample preparation.

Purpose of the Study:

To review recent advancements in SR-SIM technology and applications.
To provide an overview of SR-SIM implementation, key parameters, and reconstruction challenges.
To discuss future directions and potential of SR-SIM in biological research.

Main Methods:

Review of historical development and current implementations of SR-SIM.
Analysis of parameters critical for successful SR-SIM image reconstruction.
Summary of diverse biological applications enabled by SR-SIM.

Main Results:

SR-SIM allows multicolor imaging, optical sectioning, and live-cell imaging.
Image reconstruction can be complex and prone to artifacts, requiring user expertise.
Recent developments focus on improving reconstruction algorithms and expanding applications.

Conclusions:

SR-SIM is a powerful, versatile tool for high-resolution biological imaging.
Addressing reconstruction challenges is key to broader SR-SIM adoption.
Future developments promise enhanced capabilities and wider accessibility.