論文

査読有り
2008年11月

Quantification of Local Morphodynamics and Local GTPase Activity by Edge Evolution Tracking

PLOS COMPUTATIONAL BIOLOGY
  • Yuki Tsukada
  • ,
  • Kazuhiro Aoki
  • ,
  • Takeshi Nakamura
  • ,
  • Yuichi Sakumura
  • ,
  • Michiyuki Matsuda
  • ,
  • Shin Ishii

4
11
開始ページ
e1000223
終了ページ
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1371/journal.pcbi.1000223
出版者・発行元
PUBLIC LIBRARY SCIENCE

Advances in time-lapse fluorescence microscopy have enabled us to directly observe dynamic cellular phenomena. Although the techniques themselves have promoted the understanding of dynamic cellular functions, the vast number of images acquired has generated a need for automated processing tools to extract statistical information. A problem underlying the analysis of time-lapse cell images is the lack of rigorous methods to extract morphodynamic properties. Here, we propose an algorithm called edge evolution tracking (EET) to quantify the relationship between local morphological changes and local fluorescence intensities around a cell edge using time-lapse microscopy images. This algorithm enables us to trace the local edge extension and contraction by defining subdivided edges and their corresponding positions in successive frames. Thus, this algorithm enables the investigation of cross-correlations between local morphological changes and local intensity of fluorescent signals by considering the time shifts. By applying EET to fluorescence resonance energy transfer images of the Rho-family GTPases Rac1, Cdc42, and RhoA, we examined the cross-correlation between the local area difference and GTPase activity. The calculated correlations changed with time-shifts as expected, but surprisingly, the peak of the correlation coefficients appeared with a 6-8 min time shift of morphological changes and preceded the Rac1 or Cdc42 activities. Our method enables the quantification of the dynamics of local morphological change and local protein activity and statistical investigation of the relationship between them by considering time shifts in the relationship. Thus, this algorithm extends the value of time-lapse imaging data to better understand dynamics of cellular function.

リンク情報
DOI
https://doi.org/10.1371/journal.pcbi.1000223
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/19008941
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000261480800015&DestApp=WOS_CPL
ID情報
  • DOI : 10.1371/journal.pcbi.1000223
  • ISSN : 1553-734X
  • eISSN : 1553-7358
  • PubMed ID : 19008941
  • Web of Science ID : WOS:000261480800015

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