Detail of 11F-2Y2016M5D23h20m35s58AD_CAM1



Project
Title
Single-molecule images of living cells with epidermal growth factor (EGF) stimulation by automated in-cell single-molecule imaging system (AiSIS)
Description
Single-molecule images of living cells with epidermal growth factor (EGF) stimulation by automated in-cell single-molecule imaging system (AiSIS)
Release, Updated
2022-03-31
License
CC BY
Kind
Image data
File Formats
.avi
Data size
25.0 MB

Organism
Cricetulus griseus ( NCBI:txid10029 )
Strain(s)
-
Cell Line
CHO-K1 ( CLO_0002462 )

Datatype
-
Molecular Function (MF)
epidermal growth factor receptor binding ( GO:0005154 )
Biological Process (BP)
-
Cellular Component (CC)
-
Biological Imaging Method
fluorescence microscopy ( Fbbi:00000246 )
X scale
0.072 micrometer/pixel
Y scale
0.072 micrometer/pixel
Z scale
-
T scale
33 microsecond per time interval

Image Acquisition
Experiment type
-
Microscope type
-
Acquisition mode
-
Contrast method
-
Microscope model
-
Detector model
-
Objective model
-
Filter set
-

Summary of Methods
See details in Hiroshima M, et. al. (2020) Microscopy (Oxf), 69(2):69-78.
Related paper(s)

Michio Hiroshima, Masato Yasui, Masahiro Ueda (2020) Large-scale single-molecule imaging aided by artificial intelligence., Microscopy (Oxford, England), Volume 69, Number 2, pp. 69-78

Published in 2020 Apr 8

(Abstract) Single-molecule imaging analysis has been applied to study the dynamics and kinetics of molecular behaviors and interactions in living cells. In spite of its high potential as a technique to investigate the molecular mechanisms of cellular phenomena, single-molecule imaging analysis has not been extended to a large scale of molecules in cells due to the low measurement throughput as well as required expertise. To overcome these problems, we have automated the imaging processes by using computer operations, robotics and artificial intelligence (AI). AI is an ideal substitute for expertise to obtain high-quality images for quantitative analysis. Our automated in-cell single-molecule imaging system, AiSIS, could analyze 1600 cells in 1 day, which corresponds to approximately 100-fold higher efficiency than manual analysis. The large-scale analysis revealed cell-to-cell heterogeneity in the molecular behavior, which had not been recognized in previous studies. An analysis of the receptor behavior and downstream signaling was accomplished within a significantly reduced time frame and revealed the detailed activation scheme of signal transduction, advancing cell biology research. Furthermore, by combining the high-throughput analysis with our previous finding that a receptor changes its behavioral dynamics depending on the presence of a ligand/agonist or inhibitor/antagonist, we show that AiSIS is applicable to comprehensive pharmacological analysis such as drug screening. This AI-aided automation has wide applications for single-molecule analysis.
(MeSH Terms)

Contact
Michio Hiroshima , RIKEN , Center for Biosystems Dynamics Research , Laboratory for Cell Signaling Dynamics
Contributors
Michio Hiroshima

OMERO Dataset
OMERO Project
Source