Microphysiological System for High-Throughput Computer Vision Measurement of Microtissue Contraction

Advances in the design, generation, and application of tissue-engineered myocardial equivalents

Due to the limited regenerative ability of cardiomyocytes, the disabling irreversible condition of myocardial failure can only be treated with conservative and temporary therapeutic approaches, not able to repair the damage directly, or with organ transplantation. Among the regenerative strategies, intramyocardial cell injection or intravascular cell infusion should attenuate damage to the myocardium and reduce the risk of heart failure. However, these cell delivery-based therapies suffer from significant drawbacks and have a low success rate. Indeed, cardiac tissue engineering efforts are directed to repair, replace, and regenerate native myocardial tissue function. In a regenerative strategy, biomaterials and biomimetic stimuli play a key role in promoting cell adhesion, proliferation, differentiation, and neo-tissue formation. Thus, appropriate biochemical and biophysical cues should be combined with scaffolds emulating extracellular matrix in order to support cell growth and prompt favorable cardiac microenvironment and tissue regeneration. In this review, we provide an overview of recent developments that occurred in the biomimetic design and fabrication of cardiac scaffolds and patches. Furthermore, we sift in vitro and in situ strategies in several preclinical and clinical applications. Finally, we evaluate the possible use of bioengineered cardiac tissue equivalents as in vitro models for disease studies and drug tests.

High throughput screening system for engineered cardiac tissues

Introduction: Three dimensional engineered cardiac tissues (3D ECTs) have become indispensable as in vitro models to assess drug cardiotoxicity, a leading cause of failure in pharmaceutical development. A current bottleneck is the relatively low throughput of assays that measure spontaneous contractile forces exerted by millimeter-scale ECTs typically recorded through precise optical measurement of deflection of the polymer scaffolds that support them. The required resolution and speed limit the field of view to at most a few ECTs at a time using conventional imaging. Methods: To balance the inherent tradeoff among imaging resolution, field of view and speed, an innovative mosaic imaging system was designed, built, and validated to sense contractile force of 3D ECTs seeded on a 96-well plate. Results: The system performance was validated through real-time, parallel contractile force monitoring for up to 3 weeks. Pilot drug testing was conducted using isoproterenol. Discussion: The described tool increases contractile force sensing throughput to 96 samples per measurement; significantly reduces cost, time and labor needed for preclinical cardiotoxicity assay using 3D ECT. More broadly, our mosaicking approach is a general way to scale up image-based screening in multi-well formats.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.