Analyzing complex single-molecule emission patterns with deep learning. (November 2018) - Zhang, Peiyi; Liu, Sheng; Chaurasia, Abhishek; Ma, Donghan; Mlodzianoski, Michael; Culurciello, Eugenio; Huang, Fang
Abstract 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 retrieve 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 deep neural network smNet extracts multiplexed parameters such as 3D position, orientation and wavefront distortion from emission patterns of single molecules.