Mechanomorphological Guidance of Colloidal Gel Regulates Cell Morphogenesis

Mapping Biomaterial Complexity by Machine Learning

Biomaterials often have subtle properties that ultimately drive their bespoke performance. Given this nuanced structure-function behavior, the standard scientific approach of one experiment at a time or design of experiment methods is largely inefficient for the discovery of complex biomaterials. More recently, high-throughput experimentation coupled with machine learning methods has matured beyond expert users allowing scientists and engineers from diverse backgrounds to access these powerful data science tools. As a result, we now have the opportunity to strategically utilize all available data from high-throughput experiments to train efficacious models and map the structure-function behavior of biomaterials for their discovery. Herein, we discuss this necessary shift to data-driven determination of structure-function properties of biomaterials as we highlight how machine learning is leveraged in identifying physicochemical cues for biomaterials in tissue engineering, gene delivery, drug delivery, protein stabilization, and antifouling materials. We also discuss data-mining approaches that are coupled with machine learning to map biomaterial functions that reduce the load on experimental approaches for faster biomaterial discovery. Ultimately, harnessing the prowess of machine learning will lead to accelerated discovery and development of optimal biomaterial designs.

Encapsulation of pristine and silica-coated human adipose-derived mesenchymal stem cells in gelatin colloidal hydrogels for tissue engineering and bioprinting applications

Colloidal gels assembled from gelatin nanoparticles (GNPs) as particulate building blocks show strong promise to solve challenges in cell delivery and biofabrication, such as low cell survival and limited spatial retention. These gels offer evident advantages to facilitate cell encapsulation, but research on this topic is still limited, which hampers our understanding of the relationship between the physicochemical and biological properties of cell-laden colloidal gels. Human adipose-derived mesenchymal stem cells were successfully encapsulated in gelatin colloidal gels and evaluated their mechanical and biological performance over 7 days. The cells dispersed well within the gels without compromising gel cohesiveness, remained viable, and spread throughout the gels. Cells partially coated with silica were introduced into these gels, which increased their storage moduli and decreased their self-healing capacity after 7 days. This finding demonstrates the ability to modulate gel stiffness by incorporating cells partially coated with silica, without altering the solid content or introducing additional particles. Our work presents an efficient method for cell encapsulation while preserving gel integrity, expanding the applicability of colloidal hydrogels for tissue engineering and bioprinting. Overall, our study contributes to the design of improved cell delivery systems and biofabrication techniques.