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Simultaneous spatiotemporal super-resolution and multi-parametric fluorescence microscopy.

Jagadish Sankaran1, Harikrushnan Balasubramanian1, Wai Hoh Tang2

  • 1Department of Biological Sciences and NUS Centre for Bio-Imaging Sciences, National University of Singapore, Singapore, Singapore.

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|March 20, 2021
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Summary

This study introduces a novel GPU-accelerated method enabling simultaneous super-resolution imaging of spatial and temporal dynamics. This approach enhances precision in diffusion coefficient estimation and reveals new insights into protein interactions.

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Area of Science:

  • Biophysics
  • Cell Biology
  • Microscopy

Background:

  • Super-resolution microscopy and single-molecule fluorescence spectroscopy traditionally optimize for either spatial or temporal resolution, limiting simultaneous analysis.
  • Achieving high resolution in both domains is crucial for understanding dynamic cellular processes and molecular interactions.

Purpose of the Study:

  • To develop and validate a GPU-supported, camera-based measurement strategy for simultaneous super-resolution of spatial and temporal dynamics.
  • To enable multi-parametric analysis from a single dataset for improved physicochemical parameter estimation.

Main Methods:

  • Implementation of a GPU-supported, camera-based measurement strategy.
  • Simultaneous acquisition of data for super-resolution of spatial structures (~100 nm) and temporal dynamics (~2 ms).
  • Pixel-wise cross-correlation analysis of parameters from the same dataset.

Main Results:

  • Achieved simultaneous high spatial (~100 nm) and temporal (~2 ms) resolution from a single dataset.
  • Improved precision in estimating the diffusion coefficient of Lifeact and corrected structural artifacts.
  • Demonstrated that epidermal growth factor receptor (EGFR) domain partitioning is mainly driven by membrane interactions, not actin interactions.

Conclusions:

  • The developed method allows for robust physicochemical parameter estimation and biological discovery not possible with sequential measurements.
  • Pixel-wise cross-correlation of multi-parametric data from a single dataset is a powerful approach for biological insights.
  • EGFR organization within the cell membrane is primarily influenced by membrane and EGFR-EGFR interactions.