Affiliation:
1. Department of Biomedical Engineering and Lorry I. Lokey Interdisciplinary Center for Life Sciences and Engineering, Technion–Israel Institute of Technology, Haifa, Israel.
2. Russell Berrie Nanotechnology Institute, Technion–Israel Institute of Technology, Haifa, Israel.
3. Viterby Faculty of Electrical and Computer Engineering, Technion–Israel Institute of Technology, Haifa, Israel.
Abstract
Measuring the 3-dimensional (3D) distance between 2 spots is a common task in microscopy, because it holds information on the degree of colocalization in a variety of biological systems. Often, the 2 spots are labeled with 2 different colors, as each spot represents a different labeled entity. In computational microscopy, neural networks have been employed together with point spread function (PSF) engineering for various imaging challenges, specifically for localization microscopy. This combination enables “end-to-end” design of the optical system’s hardware and software, which is learned simultaneously, optimizing both the image acquisition and reconstruction together. In this work, we employ such a strategy for the task of direct measurement of the 3D distance between 2 emitters, labeled with differently colored fluorescent labels, in a single shot, on a single optical channel. Specifically, we use end-to-end learning to design an optimal wavelength-dependent phase mask that yields an image that is most informative with regards to the 3D distance between the 2 spots, followed by an analyzing net to decode this distance. We utilize the fact that only the distance between the 2 spots is of interest, rather than their absolute positions; importantly, the use of 2 colors, instead of 1, inherently enables subdiffraction distance estimation. We demonstrate our approach experimentally by distance measurement between pairs of fluorescent beads, as well as between 2 fluorescently tagged DNA loci in yeast cells. Our results represent an appealing demonstration of the usefulness of neural nets in task-specific microscopy design and in optical system optimization in general.
Publisher
American Association for the Advancement of Science (AAAS)