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Super-resolution Fluorescence Microscopy

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...

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Related Experiment Video

Updated: May 4, 2026

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Published on: December 10, 2013

Identifying fluorescently labeled single molecules in image stacks using machine learning.

Scott A Rifkin¹

¹Division of Biological Sciences, Section of Ecology, Behavior and Evolution, University of California, San Diego, CA, USA. sarifkin@ucsd.edu

Methods in Molecular Biology (Clifton, N.J.)

|November 9, 2011

Summary

This summary is machine-generated.

New software helps distinguish faint single-molecule signals from background noise in fluorescence microscopy images. This tool aids in accurately locating molecules, such as messenger RNA (mRNA) transcripts, for biological research.

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Last Updated: May 4, 2026

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Area of Science:

Molecular Biology
Biophysics
Bioimaging

Background:

Advanced fluorescence microscopy enables single-molecule visualization within cells and organisms.
Fluorescent signals are often limited to a few pixels, creating a challenge in distinguishing them from background noise.

Purpose of the Study:

To present a MATLAB software suite for analyzing single-molecule visualization data.
To provide a generalizable tool for identifying localized signals in noisy image backgrounds.

Main Methods:

Development of a MATLAB software suite named spotFinding Suite.
Implementation of an analysis pipeline involving gold-standard dataset creation and machine-learning algorithm training.
Application of the algorithm to identify single messenger RNA (mRNA) transcript locations in fluorescence microscopy images.

Main Results:

The software successfully processes fluorescence microscopy images or stacks.
It outputs the precise locations of visualized single molecules.
The developed machine-learning algorithm effectively classifies image features as signal or noise.

Conclusions:

The spotFinding Suite is a versatile tool for single-molecule localization in microscopy.
The software can be adapted for various applications requiring signal detection in noisy environments.
It aids researchers in accurately identifying and analyzing molecular targets within biological specimens.