Detail of Cdc42



Project
Title
Time-lapse fluorescence microscopy images of Cdc42 activity in human HT-1080 fibrosarcoma cells obtained from in vivo experiments (total about 4 hour)
Description
NA
Release, Updated
2016-10-03,
2018-11-15
License
CC BY
Kind
Image data based on Experiment
File Formats
Data size
63.7 MB

Organism
H. sapiens ( NCBI:txid9606 )
Strain(s)
HT-1080
Cell Line
-
Gene symbols
Cdc42

Datatype
cell dynamics
Molecular Function (MF)
Biological Process (BP)
cellular protein localization ( GO:0034613 )
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
-

Summary of Methods
See details in Kunida et al. (2012) Journal of Cell Science, 125(10): 2381-2392
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

OMERO Dataset
OMERO Project
Source