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Analyzing complex single-molecule emission patterns with deep learning
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DOI：10.1038/s41592-018-0153-5.png)
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Abstract
En 中文
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.
Keywords:
LOCALIZATION
MICROSCOPY
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