Optimization and validation of a fat-on-a-chip model for non-invasive therapeutic drug discovery

Abstract

Obesity is a significant public health concern that is closely associated with various comorbidities such as heart disease, stroke, type II diabetes (T2D), and certain cancers. Due to the central role of adipose tissue in many disease etiologies and the pervasive nature in the body, engineered adipose tissue models are essential for drug discovery and studying disease progression. This study validates a fat-on-a-chip (FOAC) model derived from primary mature adipocytes. Our FOAC model uses a Micronit perfusion device and introduces a novel approach for collecting continuous data by using two non-invasive readout techniques, resazurin and glucose uptake. The Micronit platform proved to be a reproducible model that can effectively maintain adipocyte viability, metabolic activity, and basic functionality, and is capable of mimicking physiologically relevant responses such as adipocyte hypertrophy and insulin-mediated glucose uptake. Importantly, we demonstrate that adipocyte size is highly dependent on extracellular matrix properties, as adipocytes derived from different patients with variable starting lipid areas equilibrate to the same size in the hyaluronic acid hydrogel. This model can be used to study T2D and monitor adipocyte responses to insulin for longitudinally tracking therapeutic efficacy of novel drugs or drug combinations.