EconPapers    
Economics at your fingertips  
 

Automated single-molecule imaging in living cells

Masato Yasui, Michio Hiroshima, Jun Kozuka, Yasushi Sako () and Masahiro Ueda ()
Additional contact information
Masato Yasui: Laboratory for Cell Signaling Dynamics, RIKEN BDR
Michio Hiroshima: Laboratory for Cell Signaling Dynamics, RIKEN BDR
Jun Kozuka: Laboratory for Cell Signaling Dynamics, RIKEN BDR
Yasushi Sako: Cellular Informatics Laboratory, RIKEN
Masahiro Ueda: Laboratory for Cell Signaling Dynamics, RIKEN BDR

Nature Communications, 2018, vol. 9, issue 1, 1-11

Abstract: Abstract An automated single-molecule imaging system developed for live-cell analyses based on artificial intelligence-assisted microscopy is presented. All significant procedures, i.e., searching for cells suitable for observation, detecting in-focus positions, and performing image acquisition and single-molecule tracking, are fully automated, and numerous highly accurate, efficient, and reproducible single-molecule imaging experiments in living cells can be performed. Here, the apparatus is applied for single-molecule imaging and analysis of epidermal growth factor receptors (EGFRs) in 1600 cells in a 96-well plate within 1 day. Changes in the lateral mobility of EGFRs on the plasma membrane in response to various ligands and drug concentrations are clearly detected in individual cells, and several dynamic and pharmacological parameters are determined, including the diffusion coefficient, oligomer size, and half-maximal effective concentration (EC50). Automated single-molecule imaging for systematic cell signaling analyses is feasible and can be applied to single-molecule screening, thus extensively contributing to biological and pharmacological research.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.nature.com/articles/s41467-018-05524-7 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05524-7

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-018-05524-7

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-19
Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05524-7