Miniaturized high-throughput synthesis and screening of responsive hydrogels using nanoliter compartments

A Rapid Screening Platform for Simultaneous Evaluation of Biodegradation and Therapeutic Release of an Ocular Hydrogel

This study attempts to address the challenge of accurately measuring the degradation of biodegradable hydrogels, which are frequently employed in drug delivery for controlled and sustained release. The traditional method utilizes a mass-loss approach, which is cumbersome and time consuming. The aim of this study was to develop an innovative screening platform using a millifluidic device coupled with automated image analysis to measure the degradation of Gelatin methacrylate (GelMA) and the subsequent release of an entrapped wetting agent, polyvinyl alcohol (PVA). Gel samples were placed within circular wells on a custom millifluidic chip and stained with a red dye for enhanced visualization. A camera module captured time-lapse images of the gels throughout their degradation. An image-analysis algorithm was used to translate the image data into degradation rates. Simultaneously, the eluate from the chip was collected to quantify the amount of GelMA degraded and PVA released at various time points. The visual method was validated by comparing it with the mass-loss approach (R = 0.91), as well as the amount of GelMA eluted (R = 0.97). The degradation of the GelMA gels was also facilitated with matrix metalloproteinases 9. Notably, as the gels degraded, there was an increase in the amount of PVA released. Overall, these results support the use of the screening platform to assess hydrogel degradation and the subsequent release of entrapped therapeutic compounds.

Accelerated Engineering of Optimized Functional Composite Hydrogels via High-Throughput Experimentation

The Materials Genome Initiative (MGI) seeks to accelerate the discovery and engineering of advanced materials via high-throughput experimentation (HTE), which is a challenging task, given the common trade-off between design for optimal processability vs performance. Here, we report a HTE method based on automated formulation, synthesis, and multiproperty characterization of bulk soft materials in well plate formats that enables accelerated engineering of functional composite hydrogels with optimized properties for processability and performance. The method facilitates rapid high-throughput screening of hydrogel composition-property relations for multiple properties in well plate formats. The feasibility and utility of the method were demonstrated by application to several functional composite hydrogel systems, including alginate/poly(N-isopropylacrylamide) (PNIPAM) and poly(ethylene glycol) dimethacrylate (PEGDMA)/poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) hydrogels. The HTE method was leveraged to identify formulations of conductive PEGDMA/PEDOT:PSS composite hydrogels for optimized performance and processability in three-dimensional (3D) printing. This work provides an advance in experimental methods based on automated dispensing, mixing, and sensing for the accelerated engineering of soft functional materials.

High-Throughput Synthesis and Machine Learning Assisted Design of Photodegradable Hydrogels

Due to the large chemical space, the design of functional and responsive soft materials poses many challenges but also offers a wide range of opportunities in terms of the scope of possible properties. Herein, an experimental workflow for miniaturized combinatorial high-throughput screening of functional hydrogel libraries is reported. The data created from the analysis of the photodegradation process of more than 900 different types of hydrogel pads are used to train a machine learning model for automated decision making. Through iterative model optimization based on Bayesian optimization, a substantial improvement in response properties is achieved and thus expanded the scope of material properties obtainable within the chemical space of hydrogels in the study. It is therefore demonstrated that the potential of combining miniaturized high-throughput experiments with smart optimization algorithms for cost and time efficient optimization of materials properties.

Automated high-throughput image processing as part of the screening platform for personalized oncology

Cancer is a devastating disease and the second leading cause of death worldwide. However, the development of resistance to current therapies is making cancer treatment more difficult. Combining the multi-omics data of individual tumors with information on their in-vitro Drug Sensitivity and Resistance Test (DSRT) can help to determine the appropriate therapy for each patient. Miniaturized high-throughput technologies, such as the droplet microarray, enable personalized oncology. We are developing a platform that incorporates DSRT profiling workflows from minute amounts of cellular material and reagents. Experimental results often rely on image-based readout techniques, where images are often constructed in grid-like structures with heterogeneous image processing targets. However, manual image analysis is time-consuming, not reproducible, and impossible for high-throughput experiments due to the amount of data generated. Therefore, automated image processing solutions are an essential component of a screening platform for personalized oncology. We present our comprehensive concept that considers assisted image annotation, algorithms for image processing of grid-like high-throughput experiments, and enhanced learning processes. In addition, the concept includes the deployment of processing pipelines. Details of the computation and implementation are presented. In particular, we outline solutions for linking automated image processing for personalized oncology with high-performance computing. Finally, we demonstrate the advantages of our proposal, using image data from heterogeneous practical experiments and challenges.

Advances in microfabrication technologies in tissue engineering and regenerative medicine

BACKGROUND

Tissue engineering provides various strategies to fabricate an appropriate microenvironment to support the repair and regeneration of lost or damaged tissues. In this matter, several technologies have been implemented to construct close-to-native three-dimensional structures at numerous physiological scales, which are essential to confer the functional characteristics of living tissues.

METHODS

In this article, we review a variety of microfabrication technologies that are currently utilized for several tissue engineering applications, such as soft lithography, microneedles, templated and self-assembly of microstructures, microfluidics, fiber spinning, and bioprinting.

RESULTS

These technologies have considerably helped us to precisely manipulate cells or cellular constructs for the fabrication of biomimetic tissues and organs. Although currently available tissues still lack some crucial functionalities, including vascular networks, innervation, and lymphatic system, microfabrication strategies are being proposed to overcome these issues. Moreover, the microfabrication techniques that have progressed to the preclinical stage are also discussed.

CONCLUSIONS

This article aims to highlight the advantages and drawbacks of each technique and areas of further research for a more comprehensive and evolving understanding of microfabrication techniques in terms of tissue engineering and regenerative medicine applications.

Miniaturized droplet microarray platform enables maintenance of human induced pluripotent stem cell pluripotency

The capacity of human induced pluripotent stem cells (hiPSCs) for indefinite self-renewal warrants their application in disease modeling, drug discovery, toxicity assays and efficacy screening. However, their poor proliferation ability, inability to adhere to surfaces without Matrigel coating and tendency to spontaneously differentiate in vitro hinder the application of hiPSCs in these fields. Here we study the ability to culture hiPSCs inside 200 ​nL droplets on the droplet microarray (DMA) platform. We demonstrate that (1) hiPSCs can attach to the Matrigel (MG)-free surface of DMA and show good viability after 24 h culture; (2) hiPSC do not spontaneously differentiate when cultured on the MG-free surface of DMAs; (3) culturing of hiPSCs in 200 ​nL as compared to 2 ​mL culture leads to higher expression of the Nanog pluripotency marker. Overall, the results demonstrate the possibility to culture undifferentiated hiPSCs in 200 ​nL droplets on DMA, thereby opening the possibility for high-throughput screenings of hiPSCs with various factors without compromising the results through the involvement of animal-derived materials, such as Matrigel.

High-throughput hyaluronic acid hydrogel arrays for cell selective adhesion screening

As a component of extracellular matrix (ECM), hyaluronic acid (HA) has plenty of applications in the biomedical field such as tissue engineering. Due to its non-adhesive nature, HA requires further grafting of functional molecules for cell related study. RGD and YIGSR are two kinds of cell adhesion peptides. YIGSR enhances endothelial cell (EC) adhesion, which is important for endothelialization after implantation of stents to prevent in-stent restenosis. However, the effect of combined densities of these peptides for EC and smooth muscle cell (SMC) adhesion has not been explored in a quantitative and high-throughput manner. In this work, single or orthogonal gradient densities of RGD and YIGSR were grafted onto the HA hydrogel array surfaces using thiol-norbornene click chemistry. Optimized peptide combinations for EC preponderant adhesion were found in hydrogel arrays and confirmed by scaling samples.