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Related Concept Videos

Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...

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Related Experiment Video

Updated: Jun 17, 2026

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
10:20

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

Quantitative study of single molecule location estimation techniques.

Anish V Abraham1, Sripad Ram, Jerry Chao

  • 1Department of Immunology, University of Texas Southwestern Medical Center, 6000 Harry Hines Boulevard, MC9093, Dallas, TX 75390, USA.

Optics Express
|January 7, 2010
PubMed
Summary
This summary is machine-generated.

Maximum likelihood estimation offers superior accuracy for single-molecule localization, especially with low noise. Both nonlinear least squares and maximum likelihood estimators perform well, but maximum likelihood shows robustness in various conditions.

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Last Updated: Jun 17, 2026

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Mapping Absolute DNA Density in Cell Nuclei using Single-molecule Localization Microscopy
10:57

Mapping Absolute DNA Density in Cell Nuclei using Single-molecule Localization Microscopy

Published on: November 11, 2025

Area of Science:

  • Biophysics
  • Microscopy
  • Computational Biology

Background:

  • Accurate single-molecule localization is crucial for quantitative analysis in microscopy.
  • Nonlinear least squares (NLLS) and maximum likelihood estimation (MLE) are common algorithms for this task.

Purpose of the Study:

  • To compare the performance of NLLS and MLE algorithms for single-molecule localization.
  • To evaluate estimator accuracy under varying noise levels and model inaccuracies.

Main Methods:

  • Comparative analysis of NLLS and MLE algorithms.
  • Assessment of localization accuracy using standard deviation of estimates.
  • Evaluation across different noise levels and model misspecifications.

Main Results:

  • Both NLLS and MLE accurately recover single-molecule locations on average.
  • MLE demonstrates higher accuracy than NLLS under low noise and accurate modeling conditions.
  • MLE exhibits robust and consistent accuracy across various model mismatches.

Conclusions:

  • Maximum likelihood estimation is generally preferred for single-molecule localization due to its accuracy and robustness.
  • The choice of estimator may depend on specific experimental conditions, particularly noise levels and model fidelity.
  • User-friendly software tools for localization and accuracy assessment are available.