Analyzing complex single-molecule emission patterns with deep learning

Nat Methods. 2018 Nov;15(11):913-916. doi: 10.1038/s41592-018-0153-5. Epub 2018 Oct 30.

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • COS Cells
  • Chlorocebus aethiops
  • Deep Learning*
  • Microscopy, Fluorescence / methods*
  • Mitochondria / metabolism*
  • Mitochondrial Proteins / analysis*
  • Mitochondrial Proteins / metabolism
  • Neural Networks, Computer*
  • Single-Cell Analysis / methods*

Substances

  • Mitochondrial Proteins