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

Grant Ongo,David Juncker,Sébastien G Ricoult,Timothy E Kennedy,Vince Grolmusz

doi:10.1371/journal.pone.0106541

Abstract

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.

Highlights

Gradients are fundamental to many phenomena of biology, from directing axonal navigation during neural development to the differentiation of stem cells in response to an injury [1,2]
Digital gradients rarely extend over 1–2 orders of magnitude (OM), whereas it is believed that the dynamic range of gradients in vivo is between 3–4 OM
Patterned substrate-bound protein gradients are a valuable tool to study a number of biological processes such as neuronal development or regeneration

Summary

Introduction

Gradients are fundamental to many phenomena of biology, from directing axonal navigation during neural development to the differentiation of stem cells in response to an injury [1,2]. Digital gradients rarely extend over 1–2 orders of magnitude (OM), whereas it is believed that the dynamic range of gradients in vivo is between 3–4 OM To overcome these limitations, we previously developed digital nanodot gradients (DNGs), where the spacing between nanodots (200 nm in diameter) was changed in two dimensions to produce a dynamic range exceeding 3 OM. We previously developed digital nanodot gradients (DNGs), where the spacing between nanodots (200 nm in diameter) was changed in two dimensions to produce a dynamic range exceeding 3 OM These designs were implemented using a low-cost, lift-off nanocontact printing method to pattern substrate-bound gradients of proteins and peptides. Gradients were non-continuous and had stepwise changes in density This is most pronounced in low-density regions where the spacing between nanodots exceeded the dimensions of the box, requiring larger box sizes and creating large ‘‘steps’’ at the bottom of the gradient. This may prove problematic as cells may fail to sense a discontinuous gradient if they fall into a region of constant density

Methods

Results

Conclusion