A fluorescent emitter simultaneously transmits its identity, location, and cellular context through its emission pattern. We developed smNet, a deep neural network for multiplexed single-molecule analysis to enable retrieving such information with high accuracy. We demonstrate that smNet can extract three-dimensional molecule location, orientation, and wavefront distortion with precision approaching the theoretical limit and therefore will allow multiplexed measurements through the emission pattern of a single molecule.
The inhomogeneous refractive indices of biological tissues blur and distort single molecule emission patterns generating image artifacts and decreasing the achievable resolution of single molecule localization microscopy (SMLM). To compensate tissue induced aberrations, conventional sensorless adaptive optics methods rely on iterative mirror-changes and image-quality metrics. However, these metrics result in inconsistent, and sometimes opposite, metric responses and thus fundamentally limit the efficacy of these approaches for aberration correction in tissues. Bypassing the previous iterative trial-then-evaluate processes, we developed deep learning driven adaptive optics for SMLM to allow direct inference of wavefront distortion and near real-time compensation. Our trained deep neural network monitors the individual emission patterns from single molecule experiments, infers their shared wavefront distortion, feeds the estimates through a dynamic filter, and drives a deformable mirror to compensate sample induced aberrations. We demonstrated that our method simultaneously estimates and compensates 28 types of wavefront deformation shapes, restores single molecule emission patterns approaching the conditions untouched by the specimen, and improves the resolution and fidelity of 3D SMLM through >130 µm thick tissue specimens, with as few as 3-20 mirror changes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.