Fast and robust multiplane single-molecule localization microscopy using a deep neural network

Single-molecule localization microscopy is a widely used technique in biological research for measuring the nanostructures of samples smaller than the diffraction limit. This study uses multifocal plane micro-scopy and addresses the three-dimensional (3D) single-molecule localization problem, where lateral and axial locations of molecules are estimated. However, when multifocal plane microscopy is used, the esti-mation accuracy of 3D localization is easily deteriorated by the small lateral drifts of camera positions. A 3D molecule localization problem was presented along with the lateral drift estimation as a compressed sensing problem. A deep neural network (DNN) was applied to solve this problem accurately and effi-ciently. The results show that the proposed method is robust to lateral drift and achieves an accuracy of 20 nm laterally and 50 nm axially without an explicit drift correction. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This study utilizes multifocal plane microscopy to address the 3D single-molecule localization problem, but small lateral drifts of camera positions can deteriorate the estimation accuracy. A deep neural network is applied to solve this problem accurately and efficiently, achieving an accuracy of 20 nm laterally and 50 nm axially without explicit drift correction.