Literature DB >> 22915253

Characterizing cell-cell interactions induced spatial organization of cell phenotypes: application to density-dependent protein nucleocytoplasmic distribution.

Fujun Han1, Biliang Zhang.   

Abstract

Cell-cell interactions play an important role in spatial organization (pattern formation) during the development of multicellular organisms. An understanding of these biological roles requires identifying cell phenotypes that are regulated by cell-cell interactions and characterizing the spatial organizations of the phenotypes. However, conventional methods for assaying cell-cell interactions are mainly applicable at a cell population level. These measures are incapable of elucidating the spatial organizations of the phenotypes, resulting in an incomplete view of cell-cell interactions. To overcome this issue, we developed an automated image-based method to investigate cell-cell interactions based on spatial localizations of cells. We demonstrated this method in cultured cells using cell density-dependent nucleocytoplasmic distribution of β-catenin and aryl hydrocarbon receptor as the phenotype. This novel method was validated by comparing with a conventional population-based method, and proved to be more sensitive and reliable. The application of the method characterized how the phenotypes were spatially organized in a population of cultured cells. We further showed that the spatial organization was governed by cell density and was protein-specific. This automated method is very simple, and will be applicable to study cell-cell interactions in different systems from prokaryotic colonies to multicellular organisms. We envision that the ability to extract and interpret how cell-cell interactions determine the spatial organization of a cell phenotype will provide new insights into biology that may be missed by traditional population-averaged studies.

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Year:  2013        PMID: 22915253     DOI: 10.1007/s12013-012-9412-8

Source DB:  PubMed          Journal:  Cell Biochem Biophys        ISSN: 1085-9195            Impact factor:   2.194


  2 in total

1.  Cell Type Classification and Unsupervised Morphological Phenotyping From Low-Resolution Images Using Deep Learning.

Authors:  Kai Yao; Nash D Rochman; Sean X Sun
Journal:  Sci Rep       Date:  2019-09-17       Impact factor: 4.379

2.  Association between NF-κB Activation in Peripheral Blood Mononuclear Cells and Late Skin and Subcutaneous Fibrosis following Radiotherapy.

Authors:  Yapin Su; Yuyu Zhang; Meiting Sun; Wenhui Liu; Fengli Pei; Fujun Han
Journal:  Biomed Res Int       Date:  2020-07-21       Impact factor: 3.411

  2 in total

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