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Long-Axial-Range Double-Helix Point Spread Functions for 3D Volumetric Super-Resolution Imaging. | LitMetric

Long-Axial-Range Double-Helix Point Spread Functions for 3D Volumetric Super-Resolution Imaging.

J Phys Chem B

Department of Chemistry, Rice University, 6100 Main Street, Houston, Texas 77005, United States.

Published: November 2024

AI Article Synopsis

  • Single-molecule localization microscopy (SMLM) enables high-resolution imaging beyond traditional light diffraction limits, particularly in 3D using engineered point spread functions (PSFs).
  • The study addresses challenges in super-resolving structures in thick samples by introducing long-axial-range double-helix (DH)-PSFs, which streamline the imaging and analysis workflows.
  • Experimentation shows that these DH-PSFs, combined with deep learning techniques, enhance image resolution and speed, achieving effective 3D imaging without the need for complex slicing or postprocessing.

Article Abstract

Single-molecule localization microscopy (SMLM) is a powerful tool for observing structures beyond the diffraction limit of light. Combining SMLM with engineered point spread functions (PSFs) enables 3D imaging over an extended axial range, as has been demonstrated for super-resolution imaging of various cellular structures. However, super-resolving structures in 3D in thick samples, such as whole mammalian cells, remains challenging as it typically requires acquisition and postprocessing stitching of multiple slices to cover the entire sample volume or more complex analysis of the data. Here, we demonstrate how the imaging and analysis workflows can be simplified by 3D single-molecule super-resolution imaging with long-axial-range double-helix (DH)-PSFs. First, we experimentally benchmark the localization precisions of short- and long-axial-range DH-PSFs at different signal-to-background ratios by imaging fluorescent beads. The performance of the DH-PSFs in terms of achievable resolution and imaging speed was then quantified for 3D single-molecule super-resolution imaging of mammalian cells by DNA-PAINT imaging of nuclear lamina protein lamin B1 in U-2 OS cells. Furthermore, we demonstrate how the use of a deep-learning-based algorithm allows the localization of dense emitters, drastically improving the achievable imaging speed and resolution. Our data demonstrate that using long-axial-range DH-PSFs offers stitching-free, 3D super-resolution imaging of whole mammalian cells, simplifying the experimental and analysis procedures for obtaining volumetric nanoscale structural information.

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Source
http://dx.doi.org/10.1021/acs.jpcb.4c05141DOI Listing

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