In JoVE (1)

Other Publications (2)

Articles by Shayoni Ray in JoVE

Other articles by Shayoni Ray on PubMed

DNA Binding Ability and Hydrogen Peroxide Induced Nuclease Activity of a Novel Cu(II) Complex with Malonate As the Primary Ligand and Protonated 2-amino-4-picoline As the Counterion

The Journal of Physical Chemistry. B. May, 2010  |  Pubmed ID: 20380411

The DNA binding property of a Cu(II) complex, viz., [Cu(mal)(2)](picH)(2).2H(2)O, (mal)(2) = malonic acid, picH = protonated 2-amino-4-picoline, has been investigated in this study. The binding of this complex with plasmid and chromosomal DNA has been characterized by different biophysical techniques. From the absorption and fluorescence spectroscopic studies, it has been observed that the said copper complex binds strongly with pUC19 plasmid and CT DNA with a binding affinity of 2.368 x 10(3) and 4.0 x 10(3) M(-1), respectively, in 10 mM citrate-phosphate buffer, pH 7.4. Spectrofluorimetric studies reveal that the copper complex exhibits partial DNA intercalation as well as partial DNA minor groove binding properties. Consequently, in agarose gel electrophoresis study, it has been observed that the complex alone induces positive supercoiling in plasmid DNA while in the presence of H(2)O(2) it exhibits nuclease activity. The induction of the breakage in DNA backbone depends upon the relative concentrations of H(2)O(2) and copper complex followed by the time of incubation with DNA. Optical DNA melting study, isothermal titration calorimetry, and absorption spectroscopy have been used to characterize the nuclease activity of this complex in the presence of H(2)O(2). Further, (1)H NMR study indicates that Cu(II) in the complex is converted into the Cu(I) state by the reduction of H(2)O(2). Finally, agarose gel electrophoresis study with different radical scavengers concludes that the production of both hydroxyl radicals and reactive oxygen species is responsible for this nuclease activity.

Multiscale Feature Analysis of Salivary Gland Branching Morphogenesis

PloS One. 2012  |  Pubmed ID: 22403724

Pattern formation in developing tissues involves dynamic spatio-temporal changes in cellular organization and subsequent evolution of functional adult structures. Branching morphogenesis is a developmental mechanism by which patterns are generated in many developing organs, which is controlled by underlying molecular pathways. Understanding the relationship between molecular signaling, cellular behavior and resulting morphological change requires quantification and categorization of the cellular behavior. In this study, tissue-level and cellular changes in developing salivary gland in response to disruption of ROCK-mediated signaling by are modeled by building cell-graphs to compute mathematical features capturing structural properties at multiple scales. These features were used to generate multiscale cell-graph signatures of untreated and ROCK signaling disrupted salivary gland organ explants. From confocal images of mouse submandibular salivary gland organ explants in which epithelial and mesenchymal nuclei were marked, a multiscale feature set capturing global structural properties, local structural properties, spectral, and morphological properties of the tissues was derived. Six feature selection algorithms and multiway modeling of the data was performed to identify distinct subsets of cell graph features that can uniquely classify and differentiate between different cell populations. Multiscale cell-graph analysis was most effective in classification of the tissue state. Cellular and tissue organization, as defined by a multiscale subset of cell-graph features, are both quantitatively distinct in epithelial and mesenchymal cell types both in the presence and absence of ROCK inhibitors. Whereas tensor analysis demonstrate that epithelial tissue was affected the most by inhibition of ROCK signaling, significant multiscale changes in mesenchymal tissue organization were identified with this analysis that were not identified in previous biological studies. We here show how to define and calculate a multiscale feature set as an effective computational approach to identify and quantify changes at multiple biological scales and to distinguish between different states in developing tissues.

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