Injection Molding Micro‐ and Nanostructures in Thermoplastic Elastomers

Stormonth-Darling, John M.; Saeed, Anwer; Reynolds, Paul; Gadegaard, Nikolaj

doi:10.1002/mame.201600011

Cited by 18 publications

(22 citation statements)

References 24 publications

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“…44,45 Other thermoplastics (e.g. polyurethane 26 ) or metal/thermoplastic and ceramic/thermoplastic composites 46 amenable to forming complex microscale structures using injection moulding could be explored. The customizability of the mulitwell array truly permits screening of biophysical and biochemical environments.…”

Section: Discussionmentioning

confidence: 99%

“…Nanopillars and grooves were injection mouleded in polystyrene (1810 crystal polystyrene, Total, Belgium), as previously described. 26,28 The bottomless plate with individual well dimensions matching that of a standard 96 well plate (0.3 cm 2 culture area) were injection moulded in polystyrene and made in house.…”

Section: Methodsmentioning

confidence: 99%

“…To enable high-throughput production of the engineered substrate, the master stamps are then used to create negative relief replica to be used as mould inlay for injection moulding ( Figure 1C-E). The mould inlay is either in nickel or polymeric material to withstand high temperatures and high pressures required for high fidelity replication of patterns using injection moulding 26 ( Figure 1F). From one mould inlay, hundreds of slides containing the patterns as the original master stamp per hour are made through injection moulding ( Figure 1G).…”

Section: Customization Of the Multiwell Arraymentioning

confidence: 99%

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Customizable, engineered substrates for rapid screening of cellular cues

Huethorst

Cutiongco

Campbell

et al. 2019

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Biophysical cues robustly direct cell responses and are thus important tools for in vitro and translational biomedical applications. High throughput platforms exploring substrates with varying physical properties are therefore valuable, however, currently existing platforms are limited in throughput, the biomaterials used, the capability to segregate between different cues and the assessment of dynamic cellular responses. Here we present a multiwell array (3x8) using a substrate engineered with patterns that present topography or rigidity cues welded to a bottomless plate with a 96-well format. Both the patterns on the engineered substrate and the well plate format can be easily customized, permitting systematic and efficient screening of biophysical cues. Here, we demonstrate three multiwell arrays patterned with a variety of topographical and mechanical cues (nano-grooves, soft pillars and nano pillars) tested with three different cell types. Using the multiwell array, we were able to measure cell functionality using analytical modalities such as live microscopy, qPCR and fluorescent immunochemistry. Cardiomyocytes cultured on 5µm grooves showed less variation in electrophysiology and contractile function. Nanopillars with 127 nm height, 100 nm diameter and 300 nm pitch showed improved chondrogenic maintenance from matrix deposition and chondrogenic gene expression. High aspect ratio pillars with an elastic shear modulus of 16 kPa mimicking the cortical bone altered cell adhesion, morphology, and increased expression of osteogenic genes. We have demonstrated the bespoke, controlled and highthroughput properties of the multiwell array that are currently unparalleled in the field today.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Customization Of the Multiwell Arraymentioning

confidence: 99%

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Customizable, engineered substrates for rapid screening of cellular cues

Huethorst

Cutiongco

Campbell

et al. 2019

Preprint

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“…Cells were grown in growth media comprised of α-MEM with nucleosides and L-glutamine without ascorbic acid and supplemented with 10% FBS and 1% penicillinstreptomycin [17]. Cells were seeded at a density of 4000 cells/cm 2 on injection moulded and surface-texturised polycarbonate substrates [3,18].…”

Section: Supporting Informationmentioning

confidence: 99%

Interactive machine learning for fast and robust cell profiling

Laux

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Gadegaard

et al. 2020

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These authors contributed equally to this work. AbstractProfiling cell morphology is a powerful tool for inferring cell function. However, this technique retains a high barrier to entry. In particular, configuring image processing parameters for optimal cell profiling is susceptible to cognitive biases, and dependent on user experience. Here, we present an interactive machine learning strategy that learns the optimum cell profiling configuration to maximise quality of the cell profiling outcome. The process is guided by the user, from whom a rating of the quality of a cell profiling configuration is obtained. The machine learning algorithm uses this information to automatically recommend the next configuration to examine. We validated our interactive approach against the standard human trial-and-error scheme to optimise an object segmentation task on the standard software CellProfiler. Our approach enabled rapid optimisation of an object segmentation pipeline, which more accurately segmented objects compared to those optimsed through human trial-and-error. Users also attested to the ease of use and reduced cognitive load enabled by our machine learning strategy over the standard approach. We envision that our interactive machine learning strategy can enhance the quality and efficiency of pipeline optimisation to democratise image-based cell profiling.

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“…Surfaces patterned with 120 nm diameter and 100 nm depth nanopits were fabricated on polycarbonate using injection molding 60 . The following nanotopographies were used: surfaces without nanopits (FLAT); nanopits in a square array with 300 nm center-to-center spacing (SQ); nanopits in a square array with approximately 300 nm center-to-center spacing distorted by 50 nm in both x and y directions (NSQ); nanopits in a hexagonal array with 300 nm center-to-center spacing (HEX).…”

Section: Polycarbonate Surfaces With Nanotopographymentioning

confidence: 99%

Predicting phenotype using morphological cell responses to nanotopography

Reynolds

et al. 2018

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AbstractCells respond in complex ways to topographies, making it challenging to identify a direct relationship between surface topography and cell response. A key problem is the lack of informative representations of topographical parameters that translate directly into biological properties. Here, we present a platform to relate the effects of nanotopography on morphology to function. This platform utilizes the ‘morphome’, a multivariate dataset containing single cell measures of focal adhesions, the cytoskeleton, and chromatin. We demonstrate that nanotopography-induced changes in cell phenotype are uniquely encoded by the morphome. The morphome was used to create a Bayesian linear regression model that robustly predicted changes in bone, cartilage, muscle and fibrous tissue gene expression induced by nanotopography. Furthermore, the morphome effectively predicted nanotopography-induced phenotype within a complex co-culture microenvironment. Thus, the morphome enables the cell function-oriented exploration of new topographies, with potential applications in the development of novel surface-patterned biomaterials for tissue implants.

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Injection Molding Micro‐ and Nanostructures in Thermoplastic Elastomers

Cited by 18 publications

References 24 publications

Customizable, engineered substrates for rapid screening of cellular cues

Customizable, engineered substrates for rapid screening of cellular cues

Interactive machine learning for fast and robust cell profiling

Predicting phenotype using morphological cell responses to nanotopography

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