Analyzing complex single-molecule emission patterns with deep learning.

Journal: Nature methods
PMID: 30377349

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.

Authors

  • Peiyi Zhang
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
  • Sheng Liu
    Medical School, Xizang Minzu University, Xianyang, People's Republic of China.
  • Abhishek Chaurasia
    School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA.
  • Donghan Ma
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
  • Michael J Mlodzianoski
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
  • Eugenio Culurciello
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
  • Fang Huang

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