Analyzing complex single-molecule emission patterns with deep learning. (November 2018)

• Record Type: Journal Article
• Authors: Zhang, Peiyi; Liu, Sheng; Chaurasia, Abhishek; Ma, Donghan; Mlodzianoski, Michael; Culurciello, Eugenio; Huang, Fang
• Is Part Of: Nature methods. Volume 15:Number 11(2018)
• Page Start: 913
• Page End: 916
• Publication Date: 2018-11
• Subjects: Life sciences -- Methodology -- Periodicals; Life sciences -- Research -- Periodicals; Biology -- Methodology -- Periodicals; Biology -- Research -- Periodicals; 570.72
• DOI: 10.1038/s41592-018-0153-5
• Languages: English
• ISSNs: 1548-7091

Abstract

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.