Detail of Fig11_Eevee_S6K_GMM_w3FRET_YFP_s8



Project
Title
Time-lapse FRET-YFP image of differentiated C1C12 cells expressig Eevee-S6K in response to insulin
Description
NA
Release, Updated
2019-11-20
License
CC BY-NC-SA
Kind
Image data based on Experiment
File Formats
Data size
345.0 MB

Organism
Mus musculus ( NCBI:txid10090 )
Strain(s)
C2C12
Cell Line
-

Datatype
single cell dynamics
Molecular Function (MF)
Biological Process (BP)
myotube differentiation ( GO:0014902 )
Cellular Component (CC)
-
Biological Imaging Method
XYZ Scale
XY: 0.645 micrometer/pixel, Z: NA
T scale
5 minutes for each time interval

Image Acquisition
Experiment type
TimeLapse
Microscope type
FluorescenceMicroscope
Acquisition mode
FluorescenceCorrelationSpectroscopy
Contrast method
Fluorescence
Microscope model
Olympus IX83
Detector model
ORCA-R2C10600-10B CCD
Objective model
UPLSAPO10X2
Filter set

Summary of Methods
See details in Inoue et al. (2018) Cell Struct Funct, 43(2): 153-169.
Related paper(s)

Haruki Inoue, Katsuyuki Kunida, Naoki Matsuda, Daisuke Hoshino, Takumi Wada, Hiromi Imamura, Hiroyuki Noji, Shinya Kuroda (2018) Automatic Quantitative Segmentation of Myotubes Reveals Single-cell Dynamics of S6 Kinase Activation., Cell structure and function, Volume 43, Number 2, pp. 153-169

Published in 2018 Aug 31 (Electronic publication in July 26, 2018, midnight )

(Abstract) Automatic cell segmentation is a powerful method for quantifying signaling dynamics at single-cell resolution in live cell fluorescence imaging. Segmentation methods for mononuclear and round shape cells have been developed extensively. However, a segmentation method for elongated polynuclear cells, such as differentiated C2C12 myotubes, has yet to be developed. In addition, myotubes are surrounded by undifferentiated reserve cells, making it difficult to identify background regions and subsequent quantification. Here we developed an automatic quantitative segmentation method for myotubes using watershed segmentation of summed binary images and a two-component Gaussian mixture model. We used time-lapse fluorescence images of differentiated C2C12 cells stably expressing Eevee-S6K, a fluorescence resonance energy transfer (FRET) biosensor of S6 kinase (S6K). Summation of binary images enhanced the contrast between myotubes and reserve cells, permitting detection of a myotube and a myotube center. Using a myotube center instead of a nucleus, individual myotubes could be detected automatically by watershed segmentation. In addition, a background correction using the two-component Gaussian mixture model permitted automatic signal intensity quantification in individual myotubes. Thus, we provide an automatic quantitative segmentation method by combining automatic myotube detection and background correction. Furthermore, this method allowed us to quantify S6K activity in individual myotubes, demonstrating that some of the temporal properties of S6K activity such as peak time and half-life of adaptation show different dose-dependent changes of insulin between cell population and individuals.Key words: time lapse images, cell segmentation, fluorescence resonance energy transfer, C2C12, myotube.
(MeSH Terms)

Contact
Shinya Kurodoa , Graduate School of Frontier Sciences, University of Tokyo , Department of Computational Biology and Medical Sciences , Kuroda Laboratory
Contributors
Haruki Inoue, Katsuyuki Kunida, Naoki Matsuda, Daisuke Hoshino, Takumi Wada, Hiromi Imamura, Hiroyuki Noji, Shinya Kuroda

OMERO Dataset
OMERO Project
Source