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Nanowire Assisted Mechanotyping of Cellular Metastatic Potential
Authors:Debadrita Paria  Annalisa Convertino  Piyush Raj  Kristine Glunde  Yun Chen  Ishan Barman
Affiliation:1. Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218 USA;2. Institute for Microelectronics and Microsystems, National Research Council, Roma, 00133 Italia;3. Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287 USA;4. Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218 USA

Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, 21218 USA

Center for Cell Dynamics, Johns Hopkins University, Baltimore, MD, 21218 USA

Abstract:Nanotechnology has provided tools for next generation biomedical devices which rely on nanostructure interfaces with living cells. In vitro biomimetic structures have enabled observation of cell response to various mechanical and chemical cues, and there is a growing interest in isolating and harnessing the specific cues that 3D microenvironments can provide without the requirement for such culture and the experimental drawbacks associated with it. Here, a randomly oriented gold coated Si nanowire substrate with patterned hydrophobic–hydrophilic areas for the differentiation of isogenic breast cancer cells of varying metastatic potential is reported. When considering synthetic surfaces for the study of cell-nanotopography interfaces, randomly oriented nanowires more closely resemble the isotropic architecture of a natural extracellular matrix. In the study reported here, the authors show that primary cancer cells preferably attach to the hydrophilic region of randomly oriented nanowire substrate while secondary cancer cells do not adhere. Using machine learning analysis of fluorescence images, cells are found to spread and elongate on the nanowire substrates as compared to a flat substrate, where they mostly remain round. Such platforms can not only be used for developing bioassays but also as stepping stones for tissue printing technologies where cells can be selectively patterned at desired locations.
Keywords:hydrophilic-hydrophobic surfaces  machine learning  metastatic cells  microfabrication  silicon nanowires
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