Ordered, random, monotonic and non-monotonic digital nanodot gradients.

PLoS One

Department of Biomedical Engineering, McGill University, Montréal, Québec, Canada; McGill University and Génome Québec Innovation Centre, McGill University, Montréal, Québec, Canada; McGill Program in Neuroengineering, Department of Neurology and Neurosurgery, Montréal Neurological Institute, McGill University, Montréal, Québec, Canada.

Published: March 2016

Cell navigation is directed by inhomogeneous distributions of extracellular cues. It is well known that noise plays a key role in biology and is present in naturally occurring gradients at the micro- and nanoscale, yet it has not been studied with gradients in vitro. Here, we introduce novel algorithms to produce ordered and random gradients of discrete nanodots--called digital nanodot gradients (DNGs)--according to monotonic and non-monotonic density functions. The algorithms generate continuous DNGs, with dot spacing changing in two dimensions along the gradient direction according to arbitrary mathematical functions, with densities ranging from 0.02% to 44.44%. The random gradient algorithm compensates for random nanodot overlap, and the randomness and spatial homogeneity of the DNGs were confirmed with Ripley's K function. An array of 100 DNGs, each 400×400 µm2, comprising a total of 57 million 200×200 nm2 dots was designed and patterned into silicon using electron-beam lithography, then patterned as fluorescently labeled IgGs on glass using lift-off nanocontact printing. DNGs will facilitate the study of the effects of noise and randomness at the micro- and nanoscales on cell migration and growth.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4156346PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0106541PLOS

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