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Fast DNA-PAINT imaging using a deep neural network.

Kaarjel K Narayanasamy1,2, Johanna V Rahm2, Siddharth Tourani3

  • 1Department of Functional Neuroanatomy, Institute for Anatomy and Cell Biology, Heidelberg University, Heidelberg, Germany.

Nature Communications
|August 27, 2022
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DeepSTORM, a neural network, accelerates DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) super-resolution microscopy. This method enables rapid, multi-color imaging of large biological samples in just one minute.

Area of Science:

  • Biophysics
  • Microscopy
  • Computational Biology

Background:

  • DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) is a super-resolution microscopy technique.
  • Current DNA-PAINT methods are limited by slow image acquisition due to the need for extensive single-emitter data.
  • Efficient multi-target and large-sample imaging remains a challenge in super-resolution microscopy.

Purpose of the Study:

  • To develop a deep learning approach to accelerate DNA-PAINT image acquisition.
  • To enable fast, multi-color super-resolution imaging of biological structures.
  • To demonstrate the applicability of the method to neuronal tissue and large samples.

Main Methods:

  • Training a neural network, DeepSTORM, to predict fluorophore positions from high emitter density DNA-PAINT data.

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  • Utilizing predicted fluorophore positions to reconstruct super-resolution images.
  • Applying the method to multi-color imaging of semi-thin neuronal tissue and large-scale biological samples.

    • Main Results:

    • Achieved super-resolution image acquisition in as little as one minute, a significant speed improvement over conventional DNA-PAINT.
    • Successfully demonstrated multi-color super-resolution imaging of structure-conserved neuronal tissue.
    • Validated the method's capability for imaging large biological samples with high resolution.

    Conclusions:

    • DeepSTORM significantly accelerates DNA-PAINT super-resolution microscopy by enabling rapid image acquisition.
    • This deep learning approach facilitates fast, multi-color imaging of biological samples, including neuronal tissues.
    • The developed method is adaptable to various single-molecule imaging techniques, enhancing their speed and applicability.