Combinatorial and high-throughput screening of biomaterials

Evolving biomaterials design from trial and error to intelligent innovation

The design and exploration of biomaterials plays a pivotal role in many fields, including medical and engineering. The prevailing approach to biomaterials discovery relies on orthogonal experiments, with repeated attempts to optimize experimental conditions. This method has proven invaluable in gaining experience, but it is also inefficient and challenging to predict the behavior of complex systems. The advent of high-throughput screening (HTS) techniques has led to a notable enhancement in the efficiency of biomaterials development, enabling researchers to assess a vast array of material combinations within a relatively short timeframe. Nevertheless, the emergence of artificial intelligence (AI) has been the catalyst for a new era in biomaterials design. AI has markedly accelerated the development of new materials by enabling the prediction of material properties through machine learning (ML) and deep learning models, as well as optimizing the design pipeline. This review will present a systematic overview of the development of biomaterials design technology. It will also explore the integration of AI with HTS technology and envisage the potential of AI-driven materials design in biomaterials for the future. STATEMENT OF SIGNIFICANCE: The design and synthesis of biomaterials have undergone substantial shifts, reflecting evolving research paradigms. High-throughput screening has emerged as a broad and efficient alternative to traditional free-form combination methods in biomaterial design. The advent of artificial intelligence (AI) enables personalized biomaterial design and, as a transformative tool in biomaterial development, is poised to redefine the field and offer long-term solutions for its advancement. Building on these advancements, this review systematically summarizes the evolution of biomaterial design, offering insights into the future trajectory of the field.

Regulating Cell-Material Interfacial Interactions through Selective Cellular Resistance

Regulating the behavior of different types of cells at the material-tissue interface is pivotal for inducing tissue regeneration. Traditional methods enhance target cell activity using specific ligands such as peptides and antibodies, which have stability issues within biological environments. Herein, we show that selective cell resistance can be realized by fine-tuning the material surface chemistry, achieving strong cell selectivity superior to that of extracellular matrix peptides. A certain degree of adsorption resistance differentially affects the adhesion of various types of cells on material surfaces. Taking this principle into account, a polyethylene glycol (PEG) grafted surface was meticulously fine-tuned to selectively support endothelial cells (ECs) while resisting smooth muscle cell attachment. Mechanistic studies identified that the difference in myosin II expression is crucial for cell selectivity. An EC-selective polymer coating for cardiovascular devices was fabricated to promote rapid surface endothelialization and prevent neointimal hyperplasia in vivo.

Accelerating the Development of Thin Film Photovoltaic Technologies: An Artificial Intelligence Assisted Methodology Using Spectroscopic and Optoelectronic Techniques

Thin film photovoltaic (TFPV) materials and devices present a high complexity with multiscale, multilayer, and multielement structures and with complex fabrication procedures. To deal with this complexity, the evaluation of their physicochemical properties is critical for generating a model that proposes strategies for their development and optimization. However, this process is time-consuming and requires high expertise. In this context, the adoption of combinatorial analysis (CA) and artificial intelligence (AI) strategies represents a powerful asset for accelerating the development of these complex materials and devices. This work introduces a methodology to facilitate the adoption of AI and CA for the development of TFPV technologies. The methodology covers all the necessary steps from the synthesis of samples for CA to data acquisition, AI-assisted data analysis, and the extraction of relevant information for research acceleration. Each step provides details on the necessary concepts, requirements, and procedures and are illustrated with examples from the literature. Then, the application of the methodology to a complex set of samples from a TFPV production line highlights its ability to rapidly glean significant insights even in intricate scenarios. The proposed methodology can be applied to other types of materials and devices beyond PV and using different characterization techniques.

Biomimetic gradient scaffolds for the tissue engineering and regeneration of rotator cuff enthesis

Rotator cuff tear is one of the most common musculoskeletal disorders, which often results in recurrent shoulder pain and limited movement. Enthesis is a structurally complex and functionally critical interface connecting tendon and bone that plays an essential role in maintaining integrity of the shoulder joint. Despite the availability of advanced surgical procedures for rotator cuff repair, there is a high rate of failure following surgery due to suboptimal enthesis healing and regeneration. Novel strategies based on tissue engineering are gaining popularity in improving tendon-bone interface (TBI) regeneration. Through incorporating physical and biochemical cues into scaffold design which mimics the structure and composition of native enthesis is advantageous to guide specific differentiation of seeding cells and facilitate the formation of functional tissues. In this review, we summarize the current state of research in enthesis tissue engineering highlighting the development and application of biomimetic scaffolds that replicate the gradient TBI. We also discuss the latest techniques for fabricating potential translatable scaffolds such as 3D bioprinting and microfluidic device. While preclinical studies have demonstrated encouraging results of biomimetic gradient scaffolds, the translation of these findings into clinical applications necessitates a comprehensive understanding of their safety and long-term efficacy.

A paradigm for high-throughput screening of cell-selective surfaces coupling orthogonal gradients and machine learning-based cell recognition

The combinational density of immobilized functional molecules on biomaterial surfaces directs cell behaviors. However, limited by the low efficiency of traditional low-throughput experimental methods, investigation and optimization of the combinational density remain daunting challenges. Herein, we report a high-throughput screening set-up to study biomaterial surface functionalization by integrating photo-controlled thiol-ene surface chemistry and machine learning-based label-free cell identification and statistics. Through such a strategy, a specific surface combinational density of polyethylene glycol (PEG) and arginine-glutamic acid-aspartic acid-valine peptide (REDV) leads to high endothelial cell (EC) selectivity against smooth muscle cell (SMC) was identified. The composition was translated as a coating formula to modify medical nickel-titanium alloy surfaces, which was then proved to improve EC competitiveness and induce endothelialization. This work provided a high-throughput method to investigate behaviors of co-cultured cells on biomaterial surfaces modified with combinatorial functional molecules.

A User's Guide to Machine Learning for Polymeric Biomaterials

The development of novel biomaterials is a challenging process, complicated by a design space with high dimensionality. Requirements for performance in the complex biological environment lead to difficult a priori rational design choices and time-consuming empirical trial-and-error experimentation. Modern data science practices, especially artificial intelligence (AI)/machine learning (ML), offer the promise to help accelerate the identification and testing of next-generation biomaterials. However, it can be a daunting task for biomaterial scientists unfamiliar with modern ML techniques to begin incorporating these useful tools into their development pipeline. This Perspective lays the foundation for a basic understanding of ML while providing a step-by-step guide to new users on how to begin implementing these techniques. A tutorial Python script has been developed walking users through the application of an ML pipeline using data from a real biomaterial design challenge based on group's research. This tutorial provides an opportunity for readers to see and experiment with ML and its syntax in Python. The Google Colab notebook can be easily accessed and copied from the following URL: www.gormleylab.com/MLcolab.

Designing of gradient scaffolds and their applications in tissue regeneration

Gradient scaffolds are isotropic/anisotropic three-dimensional structures with gradual transitions in geometry, density, porosity, stiffness, etc., that mimic the biological extracellular matrix. The gradient structures in biological tissues play a major role in various functional and metabolic activities in the body. The designing of gradients in the scaffold can overcome the current challenges in the clinic compared to conventional scaffolds by exhibiting excellent penetration capacity for nutrients & cells, increased cellular adhesion, cell viability & differentiation, improved mechanical stability, and biocompatibility. In this review, the recent advancements in designing gradient scaffolds with desired biomimetic properties, and their implication in tissue regeneration applications have been briefly explained. Furthermore, the gradients in native tissues such as bone, cartilage, neuron, cardiovascular, skin and their specific utility in tissue regeneration have been discussed in detail. The insights from such advances using gradient-based scaffolds can widen the horizon for using gradient biomaterials in tissue regeneration applications.

Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis?

Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic-hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.

DNA Functionalized Spider Silk Nanohydrogels for Specific Cell Attachment and Patterning

Nucleated protein self-assembly of an azido modified spider silk protein was employed in the preparation of nanofibrillar networks with hydrogel-like properties immobilized on coatings of the same protein. Formation of the networks in a mild aqueous environment resulted in thicknesses between 2 and 60 nm, which were controlled only by the protein concentration. Incorporated azido groups in the protein were used to "click" short nucleic acid sequences onto the nanofibrils, which were accessible to specific hybridization-based modifications, as proved by fluorescently labeled DNA complements. A lipid modifier was used for efficient incorporation of DNA into the membrane of nonadherent Jurkat cells. Based on the complementarity of the nucleic acids, highly specific DNA-assisted immobilization of the cells on the nanohydrogels with tunable cell densities was possible. Addressability of the DNA cell-to-surface anchor was demonstrated with a competitive oligonucleotide probe, resulting in a rapid release of 75-95% of cells. In addition, we developed a photolithography-based patterning of arbitrarily shaped microwells, which served to spatially define the formation of the nanohydrogels. After detaching the photoresist and PEG-blocking of the surface, DNA-assisted immobilization of the Jurkat cells on the nanohydrogel microstructures was achieved with high fidelity.

Omics Technologies for High-Throughput-Screening of Cell-Biomaterial Interactions

Recent research effort in biomaterial development has largely focused on engineering bio-instructive materials to stimulate specific cell signaling. Assessing the biological performance of these materials using time-consuming and trial-and-error traditional...

Demonstration of a laser powder bed fusion combinatorial sample for high-throughput microstructure and indentation characterization

High-throughput experiments that use combinatorial samples with rapid measurements can be used to provide process-structure-property information at reduced time, cost, and effort. Developing these tools and methods is essential in additive manufacturing where new process-structure-property information is required on a frequent basis as advances are made in feedstock materials, additive machines, and post-processing. Here we demonstrate the design and use of combinatorial samples produced on a commercial laser powder bed fusion system to study 60 distinct process conditions of nickel superalloy 625: five laser powers and four laser scan speeds in three different conditions. Combinatorial samples were characterized using optical and electron microscopy, x-ray diffraction, and indentation to estimate the porosity, grain size, crystallographic texture, secondary phase precipitation, and hardness. Indentation and porosity results were compared against a regular sample. The smaller-sized regions (3 mm × 4 mm) in the combinatorial sample have a lower hardness compared to a larger regular sample (20 mm × 20 mm) with similar porosity (< 0.03 %). Despite this difference, meaningful trends were identified with the combinatorial sample for grain size, crystallographic texture, and porosity versus laser power and scan speed as well as trends with hardness versus stress-relief condition.

High-Throughput and Combinatorial Approaches for the Development of Multifunctional Polymers

High-throughput (HT) development of new multifunctional polymers is accomplished by the combination of different HT tools established in polymer sciences in the last decade. Important advances are robotic/HT synthesis of polymer libraries, the HT characterization of polymers, and the application of spatially resolved polymer library formats, explicitly microarray and gradient libraries. HT polymer synthesis enables the generation of material libraries with combinatorial design motifs. Polymer composition, molecular weight, macromolecular architecture, etc. may be varied in a systematic, fine-graded manner to obtain libraries with high chemical diversity and sufficient compositional resolution as model systems for the screening of these materials for the functions aimed. HT characterization allows a fast assessment of complementary properties, which are employed to decipher quantitative structure-properties relationships. Moreover, these methods facilitate the HT determination of important surface parameters by spatially resolved characterization methods, including time-of-flight secondary ion mass spectrometry and X-ray photoelectron spectroscopy. Here current methods for the high-throughput robotic synthesis of multifunctional polymers as well as their characterization are presented and advantages as well as present limitations are discussed.

High-Throughput Routes to Biomaterials Discovery

Many existing clinical treatments are limited in their ability to completely restore decreased or lost tissue and organ function, an unenviable situation only further exacerbated by a globally aging population. As a result, the demand for new medical interventions has increased substantially over the past 20 years, with the burgeoning fields of gene therapy, tissue engineering, and regenerative medicine showing promise to offer solutions for full repair or replacement of damaged or aging tissues. Success in these fields, however, inherently relies on biomaterials that are engendered with the ability to provide the necessary biological cues mimicking native extracellular matrixes that support cell fate. Accelerating the development of such "directive" biomaterials requires a shift in current design practices toward those that enable rapid synthesis and characterization of polymeric materials and the coupling of these processes with techniques that enable similarly rapid quantification and optimization of the interactions between these new material systems and target cells and tissues. This manuscript reviews recent advances in combinatorial and high-throughput (HT) technologies applied to polymeric biomaterial synthesis, fabrication, and chemical, physical, and biological screening with targeted end-point applications in the fields of gene therapy, tissue engineering, and regenerative medicine. Limitations of, and future opportunities for, the further application of these research tools and methodologies are also discussed.

High-Throughput Methods in the Discovery and Study of Biomaterials and Materiobiology

The complex interaction of cells with biomaterials (i.e., materiobiology) plays an increasingly pivotal role in the development of novel implants, biomedical devices, and tissue engineering scaffolds to treat diseases, aid in the restoration of bodily functions, construct healthy tissues, or regenerate diseased ones. However, the conventional approaches are incapable of screening the huge amount of potential material parameter combinations to identify the optimal cell responses and involve a combination of serendipity and many series of trial-and-error experiments. For advanced tissue engineering and regenerative medicine, highly efficient and complex bioanalysis platforms are expected to explore the complex interaction of cells with biomaterials using combinatorial approaches that offer desired complex microenvironments during healing, development, and homeostasis. In this review, we first introduce materiobiology and its high-throughput screening (HTS). Then we present an in-depth of the recent progress of 2D/3D HTS platforms (i.e., gradient and microarray) in the principle, preparation, screening for materiobiology, and combination with other advanced technologies. The Compendium for Biomaterial Transcriptomics and high content imaging, computational simulations, and their translation toward commercial and clinical uses are highlighted. In the final section, current challenges and future perspectives are discussed. High-throughput experimentation within the field of materiobiology enables the elucidation of the relationships between biomaterial properties and biological behavior and thereby serves as a potential tool for accelerating the development of high-performance biomaterials.

Microfluidic High-Throughput Platforms for Discovery of Novel Materials

High-throughput screening is a potent technique to accelerate the discovery and development of new materials. By performing massive synthesis and characterization processes in parallel, it can rapidly discover materials with desired components, structures and functions. Among the various approaches for high-throughput screening, microfluidic platforms have attracted increasing attention. Compared with many current strategies that are generally based on robotic dispensers and automatic microplates, microfluidic platforms can significantly increase the throughput and reduce the consumption of reagents by several orders of magnitude. In this review, we first introduce current advances of the two types of microfluidic high-throughput platforms based on microarrays and microdroplets, respectively. Then the utilization of these platforms for screening different types of materials, including inorganic metals, metal alloys and organic polymers are described in detail. Finally, the challenges and opportunities in this promising field are critically discussed.

Natural Architectures for Tissue Engineering and Regenerative Medicine

The ability to control the interactions between functional biomaterials and biological systems is of great importance for tissue engineering and regenerative medicine. However, the underlying mechanisms defining the interplay between biomaterial properties and the human body are complex. Therefore, a key challenge is to design biomaterials that mimic the in vivo microenvironment. Over millions of years, nature has produced a wide variety of biological materials optimised for distinct functions, ranging from the extracellular matrix (ECM) for structural and biochemical support of cells to the holy lotus with special wettability for self-cleaning effects. Many of these systems found in biology possess unique surface properties recognised to regulate cell behaviour. Integration of such natural surface properties in biomaterials can bring about novel cell responses in vitro and provide greater insights into the processes occurring at the cell-biomaterial interface. Using natural surfaces as templates for bioinspired design can stimulate progress in the field of regenerative medicine, tissue engineering and biomaterials science. This literature review aims to combine the state-of-the-art knowledge in natural and nature-inspired surfaces, with an emphasis on material properties known to affect cell behaviour.

Scientific AI in Materials Science: a Path to a Sustainable and Scalable Paradigm

Recently there has been an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific, technical, and social opportunities that the materials community must prioritize to consistently develop and leverage Scientific AI (SciAI) to provide a credible path towards the advancement of current materials-limited technologies. Here we highlight the intersections of these opportunities with a series of proposed paths forward. The opportunities are roughly sorted from scientific/technical (e.g. development of robust, physically meaningful multiscale material representations) to social (e.g. promoting an AI-ready workforce). The proposed paths forward range from developing new infrastructure and capabilities to deploying them in industry and academia. We provide a brief introduction to AI in materials science and engineering, followed by detailed discussions of each of the opportunities and paths forward.

Screening of perfused combinatorial 3D microenvironments for cell culture

Biomaterials combining biochemical and biophysical cues to establish close-to-extracellular matrix (ECM) models have been explored for cell expansion and differentiation purposes. Multivariate arrays are used as material-saving and rapid-to-analyze platforms, which enable selecting hit-spotted formulations targeting specific cellular responses. However, these systems often lack the ability to emulate dynamic mechanical aspects that occur in specific biological milieus and affect physiological phenomena including stem cells differentiation, tumor progression, or matrix modulation. We report a tailor-made strategy to address the combined effect of flow and biochemical composition of three-dimensional (3D) biomaterials on cellular response. We suggest a simple-to-implement device comprising (i) a perforated platform accommodating miniaturized 3D biomaterials and (ii) a bioreactor that enables the incorporation of the biomaterial-containing array into a disposable perfusion chamber. The system was upscaled to parallelizable setups, increasing the number of analyzed platforms per independent experiment. As a proof-of-concept, porous chitosan scaffolds with 1 mm diameter were functionalized with combinations of 5 ECM and cell-cell contact-mediating proteins, relevant for bone and dental regeneration, corresponding to 32 protein combinatorial formulations. Mesenchymal stem cells adhesion and production of an early osteogenic marker were assessed on-chip on static and under-flow dynamic perfusion conditions. Different hit-spotted biomaterial formulations were detected for the different flow regimes using direct image analysis. Cell-binding proteins still poorly explored as biomaterials components - amelogenin and E-cadherin - were here shown as relevant cell response modulators. Their combination with ECM cell-binding proteins - fibronectin, vitronectin, and type 1 collagen - rendered specific biomaterial combinations with high cell adhesion and ALP production under flow. The developed versatile system may be targeted at widespread tissue regeneration applications, and as a disease model/drug screening platform. STATEMENT OF SIGNIFICANCE: A perfusion system that enables cell culture in arrays of three-dimensional biomaterials under dynamic flow is reported. The effect of 31 cell-binding protein combinations in the adhesion and alkaline phosphatase (ALP) production of mesenchymal stem cells was assessed using a single bioreactor chamber. Flow perfusion was not only assessed as a classical enhancer/accelerator of cell growth and early osteogenic differentiation. We hypothesized that flow may affect cell-protein interactions, and that key components driving cell response may differ under static or dynamic regimes. Indeed, hit-spotted formulations that elicited highest cell attachment and ALP production on static cell culture differed from the ones detected for dynamic flow assays. The impacting role of poorly studied proteins as E-cadherin and amelogenin as biomaterial components was highlighted.

COMBIgor: Data-Analysis Package for Combinatorial Materials Science

Combinatorial experiments involve synthesis of sample libraries with lateral composition gradients requiring spatially resolved characterization of structure and properties. Because of the maturation of combinatorial methods and their successful application in many fields, the modern combinatorial laboratory produces diverse and complex data sets requiring advanced analysis and visualization techniques. In order to utilize these large data sets to uncover new knowledge, the combinatorial scientist must engage in data science. For data science tasks, most laboratories adopt common-purpose data management and visualization software. However, processing and cross-correlating data from various measurement tools is no small task for such generic programs. Here we describe COMBIgor, a purpose-built open-source software package written in the commercial Igor Pro environment and designed to offer a systematic approach to loading, storing, processing, and visualizing combinatorial data. It includes (1) methods for loading and storing data sets from combinatorial libraries, (2) routines for streamlined data processing, and (3) data-analysis and -visualization features to construct figures. Most importantly, COMBIgor is designed to be easily customized by a laboratory, group, or individual in order to integrate additional instruments and data-processing algorithms. Utilizing the capabilities of COMBIgor can significantly reduce the burden of data management on the combinatorial scientist.

Realization of Spatially Addressable Library by a Novel Combinatorial Approach on Atomic Layer Deposition: A Case Study of Zinc Oxide

Though the synthesis of libraries of multicomponent metal oxide systems is prevalent using the combinatorial approach, the combinatorial approach has been rarely realized in studying simple metal oxides, especially applied to the atomic layer deposition (ALD) technique. In this literature, a novel combinatorial approach technique is utilized within an ALD grown simple metal oxide to synthesize a "spatially addressable combinatorial library". The two key factors in gradients were defined during the ALD process: (1) the process temperature and (2) a nonuniform flow of pulsed gases inside a cross-flow reactor. To validate the feasibility of our novel combinatorial approach, a case study of zinc oxide (ZnO), a simple metal oxide whose properties are well-known, is performed. Because of the induced gradient, the ZnO (002) crystallite size was found to gradually vary across a 100 mm wafer (∼10-20 nm) with a corresponding increase in the normalized Raman E₂/A₁ peak intensity ratio. The findings agree well with the visible grain size observed from scanning electron microscope. The novel combinatorial approach provides a means of systematical interpretation of the combined effect of the two gradients, especially in the analysis of the microstructure of ZnO crystals. Moreover, the combinatorial library reveals that the process temperature, rather than the crystal size, plays the most significant role in determining the electrical conductivity of ZnO.

Graded Protein/PEG Nanopattern Arrays: Well-Defined Gradient Biomaterials to Induce Basic Cellular Behaviors

Gradient biomaterials have shown enormous potential in high-throughput screening of biomaterials and material-induced cell migration. To make the screening process more rapid and precise, improving the regularity of morphological structure and chemical modification on gradient biomaterials have attracted much attention. In this paper, we present a novel fabrication strategy to introduce ordered nanopattern arrays into gradient biomaterials, through combining surface-initiated atom transfer radical polymerization and inclined reactive-ion etching based on colloidal lithography. Graded protein/poly(ethylene glycol) (PEG) nanopattern arrays on a quartz substrate were fabricated and applied to affect the behaviors of cells. Owing to the continuously changed ratio of two different components, the corresponding cell adhesion density along the substrate showed obvious graded distribution after culturing for 24 h. Meanwhile, the cytoskeleton showed obvious polarization after culturing for 7 days, which is parallel with the direction of gradient. Additionally, oriented migration was generated when mouse MC3T3-E1 cells were cultured on the graded protein/PEG nanopattern arrays. On the basis of the ordered and well-defined nanopatterns, the correlation between the extracellular matrix and corresponding expressions generated by different stimuli can be investigated.

From healing wounds to resorbable electronics, levan can fill bioadhesive roles in scores of markets

Levan is a fructose homopolysaccharide which gained attention recently for its unusual combination of properties distinguishing it from other natural biodegradable polysaccharides like chitosan, cellulose or starch. Among the strongest bioadhesives, film-forming levan is garnering interest for its role in some simple solutions to difficult problems. One of these is illustrated by the elegant research using laser-based techniques to construct levan films for healing wounds and burned tissue. Another is the development of bioresorbable electronic implants. Levan has been found in habitats as diverse as salterns and thermal waters to tropical plants and sugar factories. This review of the low viscosity, levan adhesive describes the mechanisms by which it forms bonds and the reasons behind some of its practical and industrial applications. Here we present descriptions from the literature for feasible approaches ready to transition from the laboratory to those searching for answers in fields as varied as medicine, packaging and furniture assembly.

Quantifying the interactions between biomimetic biomaterials - collagen I, collagen IV, laminin 521 and cellulose nanofibrils - by colloidal probe microscopy

Biomaterials of different nature have been and are widely studied for various biomedical applications. In many cases, biomaterial assemblies are designed to mimic biological systems. Although biomaterials have been thoroughly characterized in many aspects, not much quantitative information on the molecular level interactions between different biomaterials is available. That information is very important, on the one hand, to understand the properties of biological systems and, on the other hand, to develop new composite biomaterials for special applications. This work presents a systematic, quantitative analysis of self- and cross-interactions between films of collagen I (Col I), collagen IV (Col IV), laminin (LN-521), and cellulose nanofibrils (CNF), that is, biomaterials of different nature and structure that either exist in biological systems (e.g., extracellular matrices) or have shown potential for 3D cell culture and tissue engineering. Direct surface forces and adhesion between biomaterials-coated spherical microparticles and flat substrates were measured in phosphate-buffered saline using an atomic force microscope and the colloidal probe technique. Different methods (Langmuir-Schaefer deposition, spin-coating, or adsorption) were applied to completely coat the flat substrates and the spherical microparticles with homogeneous biomaterial films. The adhesion between biomaterials films increased with the time that the films were kept in contact. The strongest adhesion was observed between Col IV films, and between Col IV and LN-521 films after 30 s contact time. In contrast, low adhesion was measured between CNF films, as well as between CNF and LN-521 films. Nevertheless, a good adhesion between CNF and collagen films (especially Col I) was observed. These results increase our understanding of the structure of biological systems and can support the design of new matrices or scaffolds where different biomaterials are combined for diverse biological or medical applications.

Automated High-Throughput Synthesis of Protein-Loaded Polyanhydride Nanoparticle Libraries

The development of high-throughput techniques and combinatorial libraries can facilitate rapid synthesis and screening of biomaterial-based nanocarriers for drug and vaccine delivery. This study describes a high-throughput method using an automated robot for synthesizing polyanhydride nanoparticles encapsulating proteins. Polyanhydrides are a class of safe and biodegradable polymers that have been widely used as drug and vaccine delivery vehicles. The robot contains a multiplexed homogenizer and has the capacity to handle parallel streams of monomer or polymer solutions to synthesize polymers and/or nanoparticles. Copolymer libraries were synthesized using the monomers sebacic acid, 1,6-bis( p-carboxyphenoxy)hexane, and 1,8-bis( p-carboxyphenoxy)-3,6-dioxactane and compared to conventionally synthesized copolymers. Nanoparticle libraries of varying copolymer compositions encapsulating the model antigen ovalbumin were synthesized using flash nanoprecipitation. The amount of the surfactant Span 80 was varied to test its effect on protein encapsulation efficiency as well as antigen release kinetics. It was observed that, although the amount of surfactant did not significantly affect protein release rate, its presence enhanced protein encapsulation efficiency. Protein burst and release kinetics from conventionally and combinatorially synthesized nanoparticles were similar even though particles synthesized using the high-throughput technique were smaller. Finally, it was demonstrated that the high-throughput method could be adapted to functionalize the surface of particle libraries to aid in the design and screening of targeted drug and vaccine delivery systems. These results suggest that the new high-throughput method is a viable alternative to conventional methods for synthesizing and screening protein and vaccine delivery vehicles.

Isotopic graphene-isolated-Au-nanocrystals with cellular Raman-silent signals for cancer cell pattern recognition

For cancer diagnosis, technologies must be capable of molecular recognition, and they must possess a built-in pattern recognition component for efficient imaging and discrimination of targeted cancer cells. Surface enhanced Raman scattering (SERS) tags based on plasmonically active nanoparticles hold promise for accurate and efficient cancer cell recognition, owing to ultra-narrow peak and sensitive optical properties. However, a complex fingerprint spectrum increases data analysis difficulty, making it necessary to develop multicolor SERS tags with a simple fingerprint spectrum. To address this, we herein fabricated SERS-encoded nanoparticles (NPs) with stable and simple fingerprint spectrum through synthesis of isotopic cellular Raman-silent graphene-isolated-Au-nanocrystals (GIANs) and conjugation with phospholipid-polyethylene glycol-linked aptamers to target proteins overexpressed on the cancer cell surface. GIANs, which possess the properties of graphitic nanomaterials, such as super-stable optical properties and high Raman cross-section, showed enhanced SERS signals. The 2D-band Raman shift of GIAN, which located in the cellular Raman-silent region, was easily regulated through fabrication of isotopic GIANs without changing their molecular structure. Such GIAN tags demonstrated multiplexed Raman imaging capability, both in vivo and in vitro, with low background interference. Moreover, cell membrane protein (nucleolin, mucin and epithelial cell adhesion molecule)-specific, aptamer-conjugated isotopic GIANs were fabricated and feasibly applied to built-in coding for rapid imaging and pattern recognition of targeted cancer cells. Such isotopic GIAN-aptamer-encoders show high potential for efficient cancer cell identification and diagnosis.

An Oxygen-Tolerant PET-RAFT Polymerization for Screening Structure-Activity Relationships

The complexity of polymer-protein interactions makes rational design of the best polymer architecture for any given biointerface extremely challenging, and the high throughput synthesis and screening of polymers has emerged as an attractive alternative. A porphyrin-catalysed photoinduced electron/energy transfer-reversible addition-fragmentation chain-transfer (PET-RAFT) polymerisation was adapted to enable high throughput synthesis of complex polymer architectures in dimethyl sulfoxide (DMSO) on low-volume well plates in the presence of air. The polymerisation system shows remarkable oxygen tolerance, and excellent control of functional 3- and 4-arm star polymers. We then apply this method to investigate the effect of polymer structure on protein binding, in this case to the lectin concanavalin A (ConA). Such an approach could be applied to screen the structure-activity relationships for any number of polymer-protein interactions.

Mechanistic applications of click chemistry for pharmaceutical drug discovery and drug delivery

The concept of click chemistry (CC), first introduced by K.B. Sharpless, has been widely adopted for use in drug discovery, novel drug delivery systems (DDS), polymer chemistry, and material sciences. In this review, we outline novel aspects of CC related to drug discovery and drug delivery, with a brief overview of molecular mechanisms underlying each click reaction commonly used by researchers, and the main patents that paved the way for further diverse medicinal applications. We also describe recent progress in drug discovery and polymeric and carbon material-based drug delivery for potential pharmaceutical applications and advancements based on the CC approach, and discuss some intrinsic limitations of this popular conjugation reaction. The use of CC is likely to significantly advance drug discovery and bioconjugation development.

Gradient Coating of Polydopamine via CDR

Surfaces with gradient properties are of central importance for a number of chemical and biological processes. Here, we report rapid generation of a polydopamine (PDA) gradient on hydrophobic surfaces by a simple, low cost, and general technology, cyclic draining-replenishing (CDR). Due to the unique surface chemistry of PDA, it enables continuous and precise control of surface wettability and subsequent deposition of organic and inorganic compounds. Using kanamycin as a model compound, we show that the gradient PDA membrane potentially can be used to prepare minimum inhibitory concentration (MIC) test strips for quantifying resistance of antimicrobial agents from microorganisms. Because CDR is experimentally simple, scalable, fast, and does not require specialized reagents or instruments, we envision this platform can be easily adopted to create a variety of functional surfaces.

Miniaturized platform for high-throughput screening of stem cells

Over the past decades stem cells have gained great interest in clinical research, tissue engineering and regenerative medicine, due to their ability of self-renewal and potential to differentiate into the various cell types of the organism. The long-term maintenance of these unique properties and the control of stem cell differentiation in vitro, however, remains challenging, thus limiting their applicability in these fields. High-throughput screening (HTS) of stem cells is widely used by the researchers in order to gain more insight in the underlying mechanisms of stem cell fate as well as identifying compounds and factors maintaining stemness. However, limited availability and expandability of stem cells restricts the use of microtiter plates for HTS of stem cells emitting the urge for miniaturized platforms. This review highlights recent advances in the development of miniaturized platforms for HTS of stem cells and presents novel designs of miniaturized HTS systems.

Ordered Micro/Nanostructures with Geometric Gradient: From Integrated Wettability "Library" to Anisotropic Wetting Surface

Geometric gradients within ordered micro/nanostructures exhibit unique wetting properties. Well-defined and ordered microsphere arrays with geometric gradient (OMAGG) are successfully fabricated through combining colloidal lithography and inclined reactive ion etching (RIE). During the inclined RIE, the graded etching rates in vertical direction of etcher chamber are the key to generating a geometric gradient. The OMAGG can be used as an effective mask for the preparation of micro/nanostructure arrays with geometric gradient by selective RIE. Through this strategy, a well-defined wettability "library" with graded silicon cone arrays is fabricated, and the possibility of screening one desired "book" from the designated wettability "library" is demonstrated. Meanwhile, the silicon cone arrays with geometric gradient (SCAGG) can be applied to control the wetting behavior of water after being modified by hydrophilic or hydrophobic chemical groups. Based on this result, a temperature-responsive wetting substrate is fabricated by modifying poly n-isopropyl acrylamide (PNIPAM) on the SCAGG. These wettability gradients have great potential in tissue engineering, microfluidic devices, and integrated sensors.

High throughput exploration of process-property linkages in Al-6061 using instrumented spherical microindentation and microstructurally graded samples

Recent spherical nanoindentation protocols have proven robust at capturing the local elastic-plastic response of polycrystalline metal samples at length scales much smaller than the grain size. In this work, we extend these protocols to length scales that include multiple grains to recover microindentation stress-strain curves. These new protocols are first established in this paper and then demonstrated for Al-6061 by comparing the measured indentation stress-strain curves with the corresponding measurements from uniaxial tension tests. More specifically, the scaling factors between the uniaxial yield strength and the indentation yield strength was determined to be about 1.9, which is significantly lower than the value of 2.8 used commonly in literature. The reasons for this difference are discussed. Second, the benefits of these new protocols in facilitating high throughput exploration of process-property relationships are demonstrated through a simple case study.

Roughness gradients on zirconia for rapid screening of cell-surface interactions: Fabrication, characterization and application

Roughness is one of the key parameters for successful osseointegration of dental implants. The understanding of how roughness affects cell response is thus crucial to improve implant performance. Surface gradients, which allow rapid and systematic investigations of cell-surface interactions, have the potential to facilitate this task. In this study, a novel method aiming to produce roughness gradients at the surface of zirconia using hydrofluoric acid etching was implemented. The topography was exhaustively characterized at the microscale and nanoscale by white light interferometry and atomic force microscopy, including the analysis of amplitude, spatial, hybrid, functional, and fractal parameters. A rapid screening of the influence of roughness on human mesenchymal stem cell morphology was conducted and potential correlations between roughness parameters and cell morphology were investigated. The roughness gradient induced significant changes in cell area (p < 0.001), aspect ratio (p = 0.01), and solidity (p = 0.026). Nanoroughness parameters were linearly correlated to cell solidity (p < 0.005), while microroughness parameters appeared nonlinearly correlated to cell area, highlighting the importance of multiscale optimization of implant topography to induce the desired cell response. The gradient method proposed here drastically reduces the efforts and resources necessary to study cell-surface interactions and provides results directly transferable to industry. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 104A: 2502-2514, 2016.

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

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.

Combinatorial Low-Volume Synthesis of Well-Defined Polymers by Enzyme Degassing

The synthesis of well-defined polymers in a low-volume, combinatorial fashion has long been a goal in polymer chemistry. Here, we report the preparation of a wide range of highly controlled homo and block co-polymers by Enz-RAFT (enzyme-assisted reversible addition-fragmentation chain transfer) polymerization in microtiter plates in the open atmosphere. The addition of 1 μm glucose oxidase (GOx) to water/solvent mixtures enables polymerization reactions to proceed in extremely low volumes (40 μL) and low radical concentrations. This procedure provides excellent control and high conversions across a range of monomer families and molecular weights, thus avoiding the need to purify for screening applications. This simple technique enables combinatorial polymer synthesis in microtiter plates on the benchtop without the need of highly specialized synthesizers and at much lower volumes than is currently possible by any other technique.

3D Biomaterial Microarrays for Regenerative Medicine: Current State-of-the-Art, Emerging Directions and Future Trends

Three dimensional (3D) biomaterial microarrays hold enormous promise for regenerative medicine because of their ability to accelerate the design and fabrication of biomimetic materials. Such tissue-like biomaterials can provide an appropriate microenvironment for stimulating and controlling stem cell differentiation into tissue-specific lineages. The use of 3D biomaterial microarrays can, if optimized correctly, result in a more than 1000-fold reduction in biomaterials and cells consumption when engineering optimal materials combinations, which makes these miniaturized systems very attractive for tissue engineering and drug screening applications.

Material Cues as Potent Regulators of Epigenetics and Stem Cell Function

Biophysical signals act as potent regulators of stem cell function, lineage commitment, and epigenetic status. In recent years, synthetic biomaterials have been used to study a wide range of outside-in signaling events, and it is now well appreciated that material cues modulate the epigenome. Here, we review the role of extracellular signals in guiding stem cell behavior via epigenetic regulation, and we stress the role of physicochemical material properties as an often-overlooked modulator of intracellular signaling. We also highlight promising new research tools for ongoing interrogation of the stem cell-material interface.

A combinatorial approach towards the design of nanofibrous scaffolds for chondrogenesis

The extracellular matrix (ECM) is a three-dimensional (3D) structure composed of proteinaceous fibres that provide physical and biological cues to direct cell behaviour. Here, we build a library of hybrid collagen-polymer fibrous scaffolds with nanoscale dimensions and screen them for their ability to grow chondrocytes for cartilage repair. Poly(lactic acid) and poly (lactic-co-glycolic acid) at two different monomer ratios (85:15 and 50:50) were incrementally blended with collagen. Physical properties (wettability and stiffness) of the scaffolds were characterized and related to biological performance (proliferation, ECM production, and gene expression) and structure-function relationships were developed. We found that soft scaffolds with an intermediate wettability composed of the highly biodegradable PLGA50:50 and collagen, in two ratios (40:60 and 60:40), were optimal for chondrogenic differentiation of ATDC5 cells as determined by increased ECM production and enhanced cartilage specific gene expression. Long-term cultures indicated a stable phenotype with minimal de-differentiation or hypertrophy. The combinatorial methodology applied herein is a promising approach for the design and development of scaffolds for regenerative medicine.

Towards the design of 3D multiscale instructive tissue engineering constructs: Current approaches and trends

The design of 3D constructs with adequate properties to instruct and guide cells both in vitro and in vivo is one of the major focuses of tissue engineering. Successful tissue regeneration depends on the favorable crosstalk between the supporting structure, the cells and the host tissue so that a balanced matrix production and degradation are achieved. Herein, the major occurring events and players in normal and regenerative tissue are overviewed. These have been inspiring the selection or synthesis of instructive cues to include into the 3D constructs. We further highlight the importance of a multiscale perception of the range of features that can be included on the biomimetic structures. Lastly, we focus on the current and developing tissue-engineering approaches for the preparation of such 3D constructs: top-down, bottom-up and integrative. Bottom-up and integrative approaches present a higher potential for the design of tissue engineering devices with multiscale features and higher biochemical control than top-down strategies, and are the main focus of this review.

3-D Scaffold Platform for Optimized Non-viral Transfection of Multipotent Stem Cells

Optimization of non-viral gene delivery from biomaterials is of critical importance, as several material parameters are known to influence non-viral transfection efficiency. A series of previous studies have achieved screening of gene delivery vectors on two dimensional (2D) substrates, which have direct relevance to cell culture applications. There is an additional need to create screening systems that are 3-dimensional (3D), and can thus be applied to emerging tissue engineering applications. Here, we report an enhanced throughput, 3D scaffold platform to screen for the influence of mineral coating properties on stem cell transfection. Mineral coatings with a range of physicochemical properties were formed on the scaffolds within a 96-well plate format, while maintaining an interconnected macroporous scaffold structure. A series of general gene delivery parameters, including plasmid amount, N/P ratio, and cell density, were efficiently screened in scaffolds using a luciferase-encoding plasmid as a reporter. In addition, human mesenchymal stem cell (hMSC) transfection with a plasmid encoding bone morphogenetic protein-2 (BMP-2) was successfully optimized by screening a library of mineral coatings, resulting in over 5-fold increases in BMP-2 production when compared to standard techniques. Notably, the majority of BMP-2 was incorporated into the mineral coating following secretion from the cells. The 3D mineral coated scaffold platform described here may accelerate gene delivery optimization and improve the predictability of the screening systems, which could facilitate translation of gene delivery to clinical applications.

Finding the winning combination. Combinatorial screening of three dimensional niches to guide stem cell osteogenesis

The ability to predict and guide stem cell differentiation remains a major challenge in regenerative medicine. Numerous dynamic microenvironmental cues often provide synergistic or combinatorial signals that influence the fate of stem cells, and ultimately drive functional tissue formation. This interplay between microenvironmental cues within tissues is under intense investigation. Our goal was to better understand this interplay within the framework of a systematic 3D platform that would enable high-throughput screening (HTS) of factors that contribute to stem cell fate decisions. It is important that such platforms provide valid biomimetic microenvironments, which can be translated to macroscale constructs. Specifically, we reported on a technique for screening of combinatorial 3D niches to guide the osteogenic differentiation of human mesenchymal stem cells (hMSCs). This platform offers a rapid, cost-effective and multiplexed approach for a variety of tissue engineering applications.

Combinatorial approach to develop tailored biodegradable poly(xylitol dicarboxylate) polyesters

The objective of this work was to develop a versatile strategy for preparing biodegradable polymers with tunable properties for biomedical applications. A family of xylitol-based cross-linked polyesters was synthesized by melt condensation. The effect of systematic variation of chain length of the diacid, stoichiometric ratio, and postpolymerization curing time on the physicochemical properties was characterized. The degradation rate decreased as the chain length of the diacid increased. The polyesters synthesized by this approach possess a diverse spectrum of degradation (ranging from ∼4 to 100% degradation in 7 days), mechanical strength (from 0.5 to ∼15 MPa) and controlled release properties. The degradation was a first-order process and the rate constant of degradation decreased linearly as the hydrophobicity of the polyester increased. In controlled release studies, the order of diffusion increased with chain length and curing time. The polymers were found to be cytocompatible and are thus suitable for possible use as biodegradable polymers. This work demonstrates that this particular combinatorial approach to polymer synthesis can be used to prepare biomaterials with independently tunable properties.

Locked-in biomimetic surface gradients that are tunable in size, density and functionalization

Tuneable and stable surface-chemical gradients in supported lipid bilayers (SLBs) hold great promise for a range of applications in biological sensing and screening. Yet, until now, no method has been reported that provides temporal control of SLB gradients. Herein we report on the development of locked-in SLB gradients that can be tuned in space, time and density by applying a process to control lipid phase behaviour, electric field and temperature. Stable gradients of charged Texas-Red-, serine- or biotin-terminated lipids have been prepared. For example, the Texas-Red surface density was varied from 0 to 2 mol %, while the length was varied between several tens to several hundreds of microns. At room temperature the gradients are shown to be stable up to 24 h, while at 60 °C the gradients could be erased in 30 min. Covalent and non-covalent chemical modification of the gradients is demonstrated, for example, by FITC, hexahistidine-tagged proteins, and SAv/biotin. The amenability to various (bio)chemistries paves the way for novel SLB-based gradients, useful in sensing, high-throughput screening and for understanding dynamic biological processes.