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Mapping Absolute DNA Density in Cell Nuclei using Single-molecule Localization Microscopy
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About samples, giving examples: Optimized Single Molecule Localization Microscopy.

Angélique Jimenez1, Karoline Friedl2, Christophe Leterrier1

  • 1Aix Marseille Université, CNRS, INP UMR7051, NeuroCyto, Marseille, France.

Methods (San Diego, Calif.)
|May 13, 2019
PubMed
Summary
This summary is machine-generated.

This study details optimized workflows for Single Molecule Localization Microscopy (SMLM), including Stochastic Optical Reconstruction Microscopy (STORM) and DNA Points Accumulation for imaging in Nanoscale Topography (DNA-PAINT). These methods enhance cellular structure imaging and reduce artifacts for biological discovery.

Keywords:
CytoskeletonDNA-PAINTSMLMSTORMSuper-resolution microscopy

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Area of Science:

  • Cell Biology
  • Microscopy
  • Biophysics

Background:

  • Super-resolution microscopy, particularly Single Molecule Localization Microscopy (SMLM), offers unprecedented insights into cellular architecture at the nanoscale.
  • Achieving SMLM's full potential requires highly optimized protocols for sample preparation, imaging, and data processing.
  • Existing methods often present challenges in obtaining high-quality reconstructions and avoiding artifacts.

Purpose of the Study:

  • To provide detailed, optimized procedures for performing SMLM workflows on fixed biological samples.
  • To guide researchers in achieving high-quality SMLM imaging and data analysis for meaningful biological discoveries.
  • To focus on single-color Stochastic Optical Reconstruction Microscopy (STORM) and multicolor DNA Points Accumulation for imaging in Nanoscale Topography (DNA-PAINT).

Main Methods:

  • Detailed protocols for sample fixation and immunostaining of cellular structures like the cytoskeleton, clathrin-coated pits, and organelles.
  • Guidelines for optimal imaging parameters in SMLM to maximize resolution and signal quality.
  • Strategies for processing SMLM data to enhance reconstruction fidelity and mitigate common artifacts.

Main Results:

  • Demonstration of a comprehensive SMLM workflow applicable to various cellular targets.
  • Establishment of best practices for sample preparation and imaging to yield high-resolution nanoscale data.
  • Identification of key steps and potential pitfalls in SMLM data processing for artifact reduction.

Conclusions:

  • The presented optimized SMLM protocols enable researchers to obtain high-quality super-resolution images of cellular structures.
  • Adherence to these guidelines is crucial for maximizing the potential of SMLM techniques like STORM and DNA-PAINT.
  • This work serves as a valuable resource for researchers aiming to advance nanoscale biological investigations using SMLM.