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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: Jul 10, 2025

Super-resolution Imaging of the Bacterial Division Machinery

Super-resolution Imaging of the Bacterial Division Machinery

Published on: January 21, 2013

Toward quantitative super-resolution microscopy: molecular maps with statistical guarantees.

Katharina Proksch¹, Frank Werner², Jan Keller-Findeisen³

¹Faculty of Electrical Engineering, Mathematics and Computer Science, Universiteit Twente, Zilverling 2098, Enschede 7500, The Netherlands.

Microscopy (Oxford, England)

|November 21, 2023

Summary

This summary is machine-generated.

This study introduces a new algorithm for super-resolution microscopy to accurately count molecules in cell biology images. It provides reliable molecule counts with statistical guarantees, creating a molecular map with controlled errors.

Keywords:

asymptotic normality counting family-wise error rate multiplicity adjustment

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Last Updated: Jul 10, 2025

Super-resolution Imaging of the Bacterial Division Machinery

Super-resolution Imaging of the Bacterial Division Machinery

Published on: January 21, 2013

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

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

Published on: March 6, 2018

Ground State Depletion Super-resolution Imaging in Mammalian Cells

Ground State Depletion Super-resolution Imaging in Mammalian Cells

Published on: November 5, 2017

Area of Science:

Cell Biology
Biophysics
Microscopy

Background:

Accurate quantification of molecules is crucial in cell biology and medical research.
Fluorescence microscopy, particularly super-resolution techniques, offers high-resolution imaging but requires robust quantification methods.

Purpose of the Study:

To develop and validate a novel algorithm for precise molecule counting using super-resolution microscopy.
To provide statistical guarantees for molecule counts through asymptotic confidence intervals.
To generate a molecular map with uniform error control.

Main Methods:

A consecutive algorithm combining multiscale scanning and generic segmentation for STED microscopy.
Application of a multiscale scanning procedure to identify regions with high probability of containing molecules.
Hybridization with a generic segmentation algorithm to refine regions and reduce redundancy.
Utilizing multiple photon coincidence measurements in confocal mode for brightness and molecule number estimation.

Main Results:

The algorithm successfully quantifies molecule numbers within automatically generated image segments.
Statistical guarantees in the form of asymptotic confidence intervals are provided for molecule counts.
A molecular map with uniform error control is established, validated on simulated and real data.

Conclusions:

The developed algorithm offers a robust method for accurate molecule quantification in super-resolution microscopy.
The approach provides reliable statistical confidence intervals, enhancing the trustworthiness of molecular counts.
This technique advances quantitative cell biology and medical research by enabling precise molecular mapping.