Introduction to Super-resolution Microscopy

microscopy-defraction-limit-v4

While microscopic methods with protein and atom scale resolution – that don’t break the diffraction barrier! – exist (e.g. electron microscopy, atomic force microscopy, x-ray crystallography, etc.), they tend to be more practically difficult than fluorescence-based microscopy (see posts on Fluorescent Probes and Fluorescent Antibodies). Therefore, to give fluorescence microscopy nanometer resolution, several super resolution techniques have been developed that combine clever (1) optical/photophysical and (2) computational-processing tricks to “clean up” the “blurred data.”

Given the fact that the “blurriness” (or point-spread function) of point objects (such as single proteins) can be modeled as a 2D gaussian blur a common theme in many super-resolution fluorescent microscopy techniques (i.e. single molecule localization microscopy) is to isolate subsets of those points so the full blur can be measured and computationally replaced with a single point at the blur’s center. Repeating this process iteratively, one can fully reconstruct protein-level image from a super blurry mess. Examples of such techniques include:

  • Stochastic optical reconstruction microscopy (STORM)
  • Photo-activated localization microscopy (PALM)
  • Photo-activation localization microscopy (FPALM)
     

  • Stimulated emission depletion (STED)
  • Ground state depletion (GSD)
  • Saturated structured illumination microscopy (SSIM)
     

  • Structured illumination microscopy (SIM)
  • Spatially modulated illumination (SMI)

 

REFERENCES: 

  1. Small, A.; Stahlheber, S. Fluorophore localization algorithms for super-resolution microscopy Nature Methods, 2014, 11, 267-279.
  2. Lakowicz, J. R. in Principles of Fluorescence Spectroscopy, Springer, 2006
  3. Skoog, D. A., Holler, F. J. & Nieman, T. A. Principles of Instrumental Analysis (5th ed), Saunders College Publishing, 1998

 

Creative Commons License
This work by Eugene Douglass and Chad Miller is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.

error

Leave a Reply

Your email address will not be published. Required fields are marked *

SpamBot Test * Time limit is exhausted. Please reload CAPTCHA.