Combining deep learning approaches and point spread function engineering for simultaneous 3D position and 3D orientation measurements of fluorescent single molecules.

Point Spread Function (PSF) engineering is an effective method to increase the sensitivity of single-molecule fluorescence images to specific parameters. Classical phase mask optimization approaches have enabled the creation of new PSFs that can achieve, for example, localization precision of a few nanometers axially over a capture range of several microns with bright emitters. However, for complex high-dimensional optimization problems, classical approaches are difficult to implement and can be very time-consuming for computation. The advent of deep learning methods and their application to single-molecule imaging has provided a way to solve these problems. Here, we propose to combine PSF engineering and deep learning approaches to obtain both an optimized phase mask and a neural network structure to obtain the 3D position and 3D orientation of fixed fluorescent molecules. Our approach allows us to obtain an axial localization precision around 30 nanometers, as well as an orientation precision around 5 degrees for orientations and positions over a one micron depth range for a signal-to-noise ratio consistent with what is typical in single-molecule cellular imaging experiments. • Neural nets can be used to determine 3D position and orientation for single molecules. • A new optimal phase mask, the Arrowhead mask, was extracted by machine learning. • The full performance on 3D position and orientation was verified by extensive simulation. • Direct experimental measurements of single molecules in a polymer confirm performance. [ABSTRACT FROM AUTHOR]