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Issue 4, 2019
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Spatiotemporal quantification of acoustic cell patterning using Voronoï tessellation

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Abstract

Acoustic patterning using ultrasound standing waves has recently emerged as a potent biotechnology enabling the remote generation of ordered cell systems. This capability has opened up exciting opportunities, for example, in guiding the development of organoid cultures or the organization of complex tissues. The success of these studies is often contingent on the formation of tightly-packed and uniform cell arrays; however, a number of factors can act to disrupt or prevent acoustic patterning. Yet, to the best of our knowledge, there has been no comprehensive assessment of the quality of acoustically-patterned cell populations. In this report we use a mathematical approach, known as Voronoï tessellation, to generate a series of metrics that can be used to measure the effect of cell concentration, pressure amplitude, ultrasound frequency and biomaterial viscosity upon the quality of acoustically-patterned cell systems. Moreover, we extend this approach towards the characterization of spatiotemporal processes, namely, the acoustic patterning of cell suspensions and the migration of patterned, adherent cell clusters. This strategy is simple, unbiased and highly informative, and we anticipate that the methods described here will provide a systematic framework for all stages of acoustic patterning, including the robust quality control of devices, statistical comparison of patterning conditions, the quantitative exploration of parameter limits and the ability to track patterned tissue formation over time.

Graphical abstract: Spatiotemporal quantification of acoustic cell patterning using Voronoï tessellation

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Supplementary files

Article information


Submitted
17 Oct 2018
Accepted
30 Nov 2018
First published
22 Jan 2019

This article is Open Access

Lab Chip, 2019,19, 562-573
Article type
Communication

Spatiotemporal quantification of acoustic cell patterning using Voronoï tessellation

J. P. K. Armstrong, S. A. Maynard, I. J. Pence, A. C. Franklin, B. W. Drinkwater and M. M. Stevens, Lab Chip, 2019, 19, 562
DOI: 10.1039/C8LC01108G

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