Analyzing complex single molecule emission patterns with deep learning

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Peiyi, Liu, Sheng, Chaurasia, Abhishek, Ma, Donghan, Mlodzianoski, Michael J., Culurciello, Eugenio, Huang, Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6624853/
https://www.ncbi.nlm.nih.gov/pubmed/30377349
http://dx.doi.org/10.1038/s41592-018-0153-5
_version_ 1783434295290888192
author Zhang, Peiyi
Liu, Sheng
Chaurasia, Abhishek
Ma, Donghan
Mlodzianoski, Michael J.
Culurciello, Eugenio
Huang, Fang
author_facet Zhang, Peiyi
Liu, Sheng
Chaurasia, Abhishek
Ma, Donghan
Mlodzianoski, Michael J.
Culurciello, Eugenio
Huang, Fang
author_sort Zhang, Peiyi
collection PubMed
description 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.
format Online
Article
Text
id pubmed-6624853
institution National Center for Biotechnology Information
language English
publishDate 2018
record_format MEDLINE/PubMed
spelling pubmed-66248532019-07-12 Analyzing complex single molecule emission patterns with deep learning Zhang, Peiyi Liu, Sheng Chaurasia, Abhishek Ma, Donghan Mlodzianoski, Michael J. Culurciello, Eugenio Huang, Fang Nat Methods Article 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. 2018-10-30 2018-11 /pmc/articles/PMC6624853/ /pubmed/30377349 http://dx.doi.org/10.1038/s41592-018-0153-5 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Zhang, Peiyi
Liu, Sheng
Chaurasia, Abhishek
Ma, Donghan
Mlodzianoski, Michael J.
Culurciello, Eugenio
Huang, Fang
Analyzing complex single molecule emission patterns with deep learning
title Analyzing complex single molecule emission patterns with deep learning
title_full Analyzing complex single molecule emission patterns with deep learning
title_fullStr Analyzing complex single molecule emission patterns with deep learning
title_full_unstemmed Analyzing complex single molecule emission patterns with deep learning
title_short Analyzing complex single molecule emission patterns with deep learning
title_sort analyzing complex single molecule emission patterns with deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6624853/
https://www.ncbi.nlm.nih.gov/pubmed/30377349
http://dx.doi.org/10.1038/s41592-018-0153-5
work_keys_str_mv AT zhangpeiyi analyzingcomplexsinglemoleculeemissionpatternswithdeeplearning
AT liusheng analyzingcomplexsinglemoleculeemissionpatternswithdeeplearning
AT chaurasiaabhishek analyzingcomplexsinglemoleculeemissionpatternswithdeeplearning
AT madonghan analyzingcomplexsinglemoleculeemissionpatternswithdeeplearning
AT mlodzianoskimichaelj analyzingcomplexsinglemoleculeemissionpatternswithdeeplearning
AT culurcielloeugenio analyzingcomplexsinglemoleculeemissionpatternswithdeeplearning
AT huangfang analyzingcomplexsinglemoleculeemissionpatternswithdeeplearning

Ejemplares similares