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Articles by John H. Slater in JoVE
Other articles by John H. Slater on PubMed
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Fabrication of Multifaceted, Micropatterned Surfaces and Image-guided Patterning Using Laser Scanning Lithography
Methods in Cell Biology.
2014 |
Pubmed ID: 24439286 This protocol describes the implementation of laser scanning lithography (LSL) for the fabrication of multifaceted, patterned surfaces and for image-guided patterning. This photothermal-based patterning technique allows for selective removal of desired regions of an alkanethiol self-assembled monolayer on a metal film through raster scanning a focused 532 nm laser using a commercially available laser scanning confocal microscope. Unlike traditional photolithography methods, this technique does not require the use of a physical master and instead utilizes digital "virtual masks" that can be modified "on the fly" allowing for quick pattern modifications. The process to create multifaceted, micropatterned surfaces, surfaces that display pattern arrays of multiple biomolecules with each molecule confined to its own array, is described in detail. The generation of pattern configurations from user-chosen images, image-guided LSL is also described. This protocol outlines LSL in four basic sections. The first section details substrate preparation and includes cleaning of glass coverslips, metal deposition, and alkanethiol functionalization. The second section describes two ways to define pattern configurations, the first through manual input of pattern coordinates and dimensions using Zeiss AIM software and the second via image-guided pattern generation using a custom-written MATLAB script. The third section describes the details of the patterning procedure and postpatterning functionalization with an alkanethiol, protein, and both, and the fourth section covers cell seeding and culture. We end with a general discussion concerning the pitfalls of LSL and present potential improvements that can be made to the technique.
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Modulation of Endothelial Cell Migration Via Manipulation of Adhesion Site Growth Using Nanopatterned Surfaces
ACS Applied Materials & Interfaces.
Feb, 2015 |
Pubmed ID: 25625303 Orthogonally functionalized nanopatterend surfaces presenting discrete domains of fibronectin ranging from 92 to 405 nm were implemented to investigate the influence of limiting adhesion site growth on cell migration. We demonstrate that limiting adhesion site growth to small, immature adhesions using sub-100 nm patterns induced cells to form a significantly increased number of smaller, more densely packed adhesions that displayed few interactions with actin stress fibers. Human umbilical vein endothelial cells exhibiting these traits displayed highly dynamic fluctuations in spreading and a 4.8-fold increase in speed compared to cells on nonpatterned controls. As adhesions were allowed to mature in size in cells cultured on larger nanopatterns, 222 to 405 nm, the dynamic fluctuations in spread area and migration began to slow, yet cells still displayed a 2.1-fold increase in speed compared to controls. As all restrictions on adhesion site growth were lifted using nonpatterned controls, cells formed significantly fewer, less densely packed, larger, mature adhesions that acted as terminating sites for actin stress fibers and significantly slower migration. The results revealed an exponential decay in cell speed with increased adhesion site size, indicating that preventing the formation of large mature adhesions may disrupt cell stability thereby inducing highly migratory behavior.
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Recapitulation and Modulation of the Cellular Architecture of a User-Chosen Cell of Interest Using Cell-Derived, Biomimetic Patterning
ACS Nano.
Jun, 2015 |
Pubmed ID: 25988713 Heterogeneity of cell populations can confound population-averaged measurements and obscure important findings or foster inaccurate conclusions. The ability to generate a homogeneous cell population, at least with respect to a chosen trait, could significantly aid basic biological research and development of high-throughput assays. Accordingly, we developed a high-resolution, image-based patterning strategy to produce arrays of single-cell patterns derived from the morphology or adhesion site arrangement of user-chosen cells of interest (COIs). Cells cultured on both cell-derived patterns displayed a cellular architecture defined by their morphology, adhesive state, cytoskeletal organization, and nuclear properties that quantitatively recapitulated the COIs that defined the patterns. Furthermore, slight modifications to pattern design allowed for suppression of specific actin stress fibers and direct modulation of adhesion site dynamics. This approach to patterning provides a strategy to produce a more homogeneous cell population, decouple the influences of cytoskeletal structure, adhesion dynamics, and intracellular tension on mechanotransduction-mediated processes, and a platform for high-throughput cellular assays.
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Progeny Clustering: A Method to Identify Biological Phenotypes
Scientific Reports.
Aug, 2015 |
Pubmed ID: 26267476 Estimating the optimal number of clusters is a major challenge in applying cluster analysis to any type of dataset, especially to biomedical datasets, which are high-dimensional and complex. Here, we introduce an improved method, Progeny Clustering, which is stability-based and exceptionally efficient in computing, to find the ideal number of clusters. The algorithm employs a novel Progeny Sampling method to reconstruct cluster identity, a co-occurrence probability matrix to assess the clustering stability, and a set of reference datasets to overcome inherent biases in the algorithm and data space. Our method was shown successful and robust when applied to two synthetic datasets (datasets of two-dimensions and ten-dimensions containing eight dimensions of pure noise), two standard biological datasets (the Iris dataset and Rat CNS dataset) and two biological datasets (a cell phenotype dataset and an acute myeloid leukemia (AML) reverse phase protein array (RPPA) dataset). Progeny Clustering outperformed some popular clustering evaluation methods in the ten-dimensional synthetic dataset as well as in the cell phenotype dataset, and it was the only method that successfully discovered clinically meaningful patient groupings in the AML RPPA dataset.
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