Jouchet Pierre, Roy Anish R, Moerner W E
Department of Chemistry, Stanford University, 94305 Stanford CA, USA.
Opt Commun. 2023 Sep 1;542. doi: 10.1016/j.optcom.2023.129589. Epub 2023 May 11.
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.
点扩散函数（PSF）工程是一种提高单分子荧光图像对特定参数敏感度的有效方法。经典的相位掩模优化方法能够创建新的点扩散函数，例如，对于明亮的发射体，在几微米的捕获范围内可实现轴向几纳米的定位精度。然而，对于复杂的高维优化问题，经典方法难以实施且计算耗时。深度学习方法的出现及其在单分子成像中的应用为解决这些问题提供了途径。在此，我们提议将点扩散函数工程与深度学习方法相结合，以获得优化的相位掩模和神经网络结构，从而获取固定荧光分子的三维位置和三维取向。我们的方法使我们能够在与单分子细胞成像实验典型情况一致的信噪比条件下，在一微米深度范围内获得约30纳米的轴向定位精度以及约5度的取向精度。
