Summary of 121-Inoue-SingleCellDyn

SSBD:database
SSBD:database URL
Title
Timelapse images and quantification of signal activity of differentiated C2C12 cells expressing Eevee-S6K and mitAT1.03 in individual myotube
Description
-
Relase date
2019-11-20
Updated date
-
License
CC BY-NC-SA
Kind
Image data based on Experiment
Number of Datasets
29 ( Image datasets: 29, Quantitative data datasets: 0 )
Size of Datasets
18.5 GB ( Image datasets: 18.5 GB, Quantitative data datasets: 0 bytes )

Organism(s)
Mus musculus
Strain(s)
C2C12

Datatype
single cell dynamics
Molecular Function (MF)
Biological Process (BP)
myotube differentiation
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
XF3075, XF3079, XF3079, XF3075

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


Dataset List of 121-Inoue-SingleCellDyn

#
Dataset ID
Kind
Size
4D View
SSBD:OMERO
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Download Images
# 4836
Dataset Kind Image data
Dataset Size 4.3 GB
4D view
SSBD:OMERO
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# 4837
Dataset Kind Image data
Dataset Size 4.3 GB
4D view
SSBD:OMERO
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# 4838
Dataset Kind Image data
Dataset Size 515.8 MB
4D view
SSBD:OMERO
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# 4839
Dataset Kind Image data
Dataset Size 515.8 MB
4D view
SSBD:OMERO
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# 4840
Dataset Kind Image data
Dataset Size 515.8 MB
4D view
SSBD:OMERO
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# 4841
Dataset Kind Image data
Dataset Size 515.8 MB
4D view
SSBD:OMERO
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# 4842
Dataset Kind Image data
Dataset Size 515.8 MB
4D view
SSBD:OMERO
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# 4843
Dataset Kind Image data
Dataset Size 515.8 MB
4D view
SSBD:OMERO
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# 4844
Dataset Kind Image data
Dataset Size 515.8 MB
4D view
SSBD:OMERO
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# 4845
Dataset Kind Image data
Dataset Size 515.8 MB
4D view
SSBD:OMERO
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# 4846
Dataset Kind Image data
Dataset Size 345.0 MB
4D view
SSBD:OMERO
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# 4847
Dataset Kind Image data
Dataset Size 345.0 MB
4D view
SSBD:OMERO
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# 4848
Dataset Kind Image data
Dataset Size 345.0 MB
4D view
SSBD:OMERO
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# 4849
Dataset Kind Image data
Dataset Size 345.0 MB
4D view
SSBD:OMERO
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# 4850
Dataset Kind Image data
Dataset Size 345.0 MB
4D view
SSBD:OMERO
Download BDML
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# 4851
Dataset Kind Image data
Dataset Size 345.0 MB
4D view
SSBD:OMERO
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# 4852
Dataset Kind Image data
Dataset Size 345.0 MB
4D view
SSBD:OMERO
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# 4853
Dataset Kind Image data
Dataset Size 345.0 MB
4D view
SSBD:OMERO
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# 4854
Dataset Kind Image data
Dataset Size 345.0 MB
4D view
SSBD:OMERO
Download BDML
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# 4855
Dataset Kind Image data
Dataset Size 345.0 MB
4D view
SSBD:OMERO
Download BDML
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# 4856
Dataset Kind Image data
Dataset Size 345.0 MB
4D view
SSBD:OMERO
Download BDML
Download Image data

# 4857
Dataset Kind Image data
Dataset Size 345.0 MB
4D view
SSBD:OMERO
Download BDML
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# 4858
Dataset Kind Image data
Dataset Size 345.0 MB
4D view
SSBD:OMERO
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# 4859
Dataset Kind Image data
Dataset Size 345.0 MB
4D view
SSBD:OMERO
Download BDML
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# 4860
Dataset Kind Image data
Dataset Size 345.0 MB
4D view
SSBD:OMERO
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# 4861
Dataset Kind Image data
Dataset Size 345.0 MB
4D view
SSBD:OMERO
Download BDML
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# 4862
Dataset Kind Image data
Dataset Size 181.1 MB
4D view
SSBD:OMERO
Download BDML
Download Image data

# 4863
Dataset Kind Image data
Dataset Size 121.1 MB
4D view
SSBD:OMERO
Download BDML
Download Image data

# 4864
Dataset Kind Image data
Dataset Size 121.1 MB
4D view
SSBD:OMERO
Download BDML
Download Image data