A whole new world of 3D microscopy was revealed in June 2020 in the journal Nature Methods, bringing super-resolution to dynamic cellular imaging.
The breakthrough resulted from a collaboration between Prof. Yoav Shechtman and PhD student Elias Nehme of the Faculty of Biomedical Engineering together with Prof. Tomer Michaeli of the Viterbi Faculty of Electrical Engineering.
The scientists met the long-term biological challenge of high speed, volumetric imaging, reaching a separation capacity ten times greater than a standard optical microscope.
“To get depth information from a 2D image we use wavefront design – an optical method that encodes the depth of each molecule in the shape obtained on camera,” says Shechtman, describing the process that led to the super-resolution 3D mapping system which is called DeepSTORM3D. “The problem with this method is that if several molecules are close by, their images overlap with each other and therefore impair spatial and temporal resolution.”
To address the challenge, researchers used deep learning. They developed an artificial neural network which solves complex image processing challenges, and even designs the optical system itself. “The new technology advances us towards realizing one of the great ambitions of biological research – mapping biological processes in living cells in 3D super-resolution,” says Shechtman.
The research was conducted with the support of the Zuckerman Foundation, Google, ERC and ISF grants.