Stepping into the omics era: Opportunities and challenges for biomaterials science and engineering

Abstract

The research paradigm in biomaterials science and engineering is evolving from using low-throughput and iterative experimental designs towards high-throughput experimental designs for materials optimization and the evaluation of materials properties. Computational science plays an important role in this transition. With the emergence of the omics approach in the biomaterials field, referred to as materiomics, high-throughput approaches hold the promise of tackling the complexity of materials and understanding correlations between material properties and their effects on complex biological systems. The intrinsic complexity of biological systems is an important factor that is often oversimplified when characterizing biological responses to materials and establishing property-activity relationships. Indeed, in vitro tests designed to predict in vivo performance of a given biomaterial are largely lacking as we are not able to capture the biological complexity of whole tissues in an in vitro model. In this opinion paper, we explain how we reached our opinion that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field.

Statement of significance: In this opinion paper, we postulate that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field.

Keywords: Combinatorial screening; Computational modeling; Converging omics fields; Genomics; High-throughput experimentation; Materiomics; Transcriptomics.

Fig. 1

Combinatorial methods accelerate the discovery of new biomaterials. A library of 112 degradable polymers were fabricated using a strictly alternating A–B type copolymer design with 14 distinct tyrosine-derived diphenols and eight different aliphatic diacids in all possible combinations. The first monomer contained a functionalizable pending chain, for attachment of a series of chemical groups, while the second monomer allowed copolymerization of n different monomers A with m different monomers B, by giving rise to an array of m × n structurally related copolymers. The diphenols introduced a variety of pendent chains into the polymer structure, while the diacids controlled the flexibility and hydrophobicity of the polymer backbone. This systematic approach allows studying numerous polymeric candidate materials for medical applications thereby facilitating the identification of correlations between polymer structure and (biological) properties. Indeed further studies showed structure-property correlations using this combinatorial library and reported predictable changes in glass transition temperature (T_g), surface wettability and cellular response. This library of 112 discreet polymers exhibited predictable correlations between polymer structure and a wide range of polymer properties, including biological properties such as protein surface adsorption and cell proliferation. As an example, the glass transition temperature of all 112 polymers is shown here as function of the structure of the diacid and diphenol [22,23].

Fig. 2

High-throughput platform to assess cell behavior on topographical features. A library of surface features were randomly generated using mathematical algorithms and produced into chips with 2176 different and unique topographies. Cellular responses to these unique surface features are assessed by high content imaging of morphological parameters or the expression of typical proliferation or differentiation markers (such as EDU incorporation or ALP expression). These libraries of topographical features can be broadly applied to reveal cell-surface interaction aiming at improved biomaterial and implant surfaces [50,51]. (Figure adapted from Unadkat et al. and Hulsman et al.)

Fig. 3

Converging materiomics with transcriptomics. This graphical representation illustrates the proposed approach of combating the complexity of biomaterials with the complexity offered by transcriptomic data. On the one hand, the “materiome” assembles the numerous properties that builds up a biomaterial (1). On the other hand, a cell's transcriptome captures the biological response to a biomaterial (2). Increasing the number of materials to be evaluated (1…n) leads to a concomitant increase in the set of properties or materiomes (1…x) and the number of corresponding transcriptomes (1…y). As such, libraries containing a large variety of materials properties on the one hand and corresponding genome-wide gene expression profiles on the other hand can be built. Herein correlations between the effect of the various biomaterial properties and the biological responses reflected in the transcriptional profiles are key to new insights for improved biomaterial design (3).