Summary of 18-Kunida-MolDynRho

SSBD:database
SSBD:database URL
Title
-
Description
-
Relase date
2016-10-03
Updated date
2018-11-15
License
CC BY
Kind
Image data based on Experiment
Number of Datasets
2 ( Image datasets: 2, Quantitative data datasets: 0 )
Size of Datasets
111.4 MB ( Image datasets: 111.4 MB, Quantitative data datasets: 0 bytes )

Organism(s)
H. sapiens
Strain(s)
HT-1080
Gene symbol(s)
Cdc42, Rac1
Protein name(s)
NA

Datatype
cell dynamics
Molecular Function (MF)
Biological Process (BP)
cellular protein localization
Cellular Component (CC)
-
Biological Imaging Method
-
XYZ Scale
XY: 0.49 micrometer/pixel, Z: 0 micrometer/slice
T scale
2 minute for each time interval

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

Related paper(s)

Katsuyuki Kunida, Michiyuki Matsuda, Kazuhiro Aoki (2012) FRET imaging and statistical signal processing reveal positive and negative feedback loops regulating the morphology of randomly migrating HT-1080 cells., Journal of cell science, Volume 125, Number Pt 10, pp. 2381-92

Published in 2012 May 15 (Electronic publication in Feb. 17, 2012, midnight )

(Abstract) Cell migration plays an important role in many physiological processes. Rho GTPases (Rac1, Cdc42, RhoA) and phosphatidylinositols have been extensively studied in directional cell migration. However, it remains unclear how Rho GTPases and phosphatidylinositols regulate random cell migration in space and time. We have attempted to address this issue using fluorescence resonance energy transfer (FRET) imaging and statistical signal processing. First, we acquired time-lapse images of random migration of HT-1080 fibrosarcoma cells expressing FRET biosensors of Rho GTPases and phosphatidyl inositols. We developed an image-processing algorithm to extract FRET values and velocities at the leading edge of migrating cells. Auto- and cross-correlation analysis suggested the involvement of feedback regulations among Rac1, phosphatidyl inositols and membrane protrusions. To verify the feedback regulations, we employed an acute inhibition of the signaling pathway with pharmaceutical inhibitors. The inhibition of actin polymerization decreased Rac1 activity, indicating the presence of positive feedback from actin polymerization to Rac1. Furthermore, treatment with PI3-kinase inhibitor induced an adaptation of Rac1 activity, i.e. a transient reduction of Rac1 activity followed by recovery to the basal level. In silico modeling that reproduced the adaptation predicted the existence of a negative feedback loop from Rac1 to actin polymerization. Finally, we identified MLCK as the probable controlling factor in the negative feedback. These findings quantitatively demonstrate positive and negative feedback loops that involve actin, Rac1 and MLCK, and account for the ordered patterns of membrane dynamics observed in randomly migrating cells.
(MeSH Terms)

Contact
Kazuhiro Aoki , Kyoto University , Graduate School of Medicine , Imaging Platform for Spatio-Temporal Information
Contributors
Katsuyuki Kunida, Michiyuki Matsuda, Kazuhiro Aoki


Dataset List of 18-Kunida-MolDynRho

#
Dataset ID
Kind
Size
4D View
SSBD:OMERO
Download BDML
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# 793
Datast ID Rac1
Dataset Kind Image data
Dataset Size 47.7 MB
4D view
SSBD:OMERO
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Download Image data

# 794
Datast ID Cdc42
Dataset Kind Image data
Dataset Size 63.7 MB
4D view
SSBD:OMERO
Download BDML
Download Image data