Analyzing complex single molecule emission patterns with deep learning
| Autor: | Michael J. Mlodzianoski, Sheng Liu, Abhishek Chaurasia, Fang Huang, Donghan Ma, Peiyi Zhang, Eugenio Culurciello |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Context (language use) 01 natural sciences Biochemistry Multiplexing Article 010309 optics Mitochondrial Proteins 03 medical and health sciences Deep Learning Distortion 0103 physical sciences Chlorocebus aethiops Animals Molecular Biology Common emitter Wavefront Physics Artificial neural network business.industry Orientation (computer vision) Deep learning Cell Biology Mitochondria 030104 developmental biology Microscopy Fluorescence COS Cells Artificial intelligence Neural Networks Computer Single-Cell Analysis business Biological system Biotechnology |
| Zdroj: | Nature methods |
| ISSN: | 1548-7105 1548-7091 |
| Popis: | 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. Editor’s summary The deep neural network smNet enables extraction of multiplexed parameters such as 3D position, orientation and wavefront distortion from emission patterns of single molecules. |
| Databáze: | OpenAIRE |
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