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Discovery of synergistic material-topography combinations to achieve immunomodulatory osteoinductive biomaterials using a novel in vitro screening method: The ChemoTopoChip.

Authors :
Burroughs L
Amer MH
Vassey M
Koch B
Figueredo GP
Mukonoweshuro B
Mikulskis P
Vasilevich A
Vermeulen S
Dryden IL
Winkler DA
Ghaemmaghami AM
Rose FRAJ
de Boer J
Alexander MR
Source :
Biomaterials [Biomaterials] 2021 Apr; Vol. 271, pp. 120740. Date of Electronic Publication: 2021 Mar 01.
Publication Year :
2021

Abstract

Human mesenchymal stem cells (hMSCs) are widely represented in regenerative medicine clinical strategies due to their compatibility with autologous implantation. Effective bone regeneration involves crosstalk between macrophages and hMSCs, with macrophages playing a key role in the recruitment and differentiation of hMSCs. However, engineered biomaterials able to simultaneously direct hMSC fate and modulate macrophage phenotype have not yet been identified. A novel combinatorial chemistry-topography screening platform, the ChemoTopoChip, is used here to identify materials suitable for bone regeneration by screening 1008 combinations in each experiment for human immortalized mesenchymal stem cell (hiMSCs) and human macrophage response. The osteoinduction achieved in hiMSCs cultured on the "hit" materials in basal media is comparable to that seen when cells are cultured in osteogenic media, illustrating that these materials offer a materials-induced alternative to osteo-inductive supplements in bone-regeneration. Some of these same chemistry-microtopography combinations also exhibit immunomodulatory stimuli, polarizing macrophages towards a pro-healing phenotype. Maximum control of cell response is achieved when both chemistry and topography are recruited to instruct the required cell phenotype, combining synergistically. The large combinatorial library allows us for the first time to probe the relative cell-instructive roles of microtopography and material chemistry which we find to provide similar ranges of cell modulation for both cues. Machine learning is used to generate structure-activity relationships that identify key chemical and topographical features enhancing the response of both cell types, providing a basis for a better understanding of cell response to micro topographically patterned polymers.<br /> (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Details

Language :
English
ISSN :
1878-5905
Volume :
271
Database :
MEDLINE
Journal :
Biomaterials
Publication Type :
Academic Journal
Accession number :
33714019
Full Text :
https://doi.org/10.1016/j.biomaterials.2021.120740