Analyzing complex single-molecule emission patterns with deep learning

利用深度学习分析复杂的单分子发射模式

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作者:Peiyi Zhang, Sheng Liu, Abhishek Chaurasia, Donghan Ma, Michael J Mlodzianoski, Eugenio Culurciello, Fang Huang

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.

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