Scaling human liver microphysiological systems: implementing a higher-throughput liver acinus microphysiological system platform

The advancement in the use of all-human high content microphysiological systems (MPS) has enabled better in vitro modeling of liver function and disease progression as well as drug efficacy, metabolism and toxicity (ADME-Tox) testing. However, a continuing need in liver MPS development is balancing throughput without loss of the high-content biological complexity required for physiologically relevant modeling. Here, we present a scalable version of our well-established liver acinus microphysiological system (LAMPS). This higher-throughput format (ht-LAMPS) is designed to recapitulate the physiological complexity of the standard single-chamber LAMPS system while increasing experimental capacity through a seven-chamber microfluidic design. The ht-LAMPS is constructed using the same four key liver cell types as the LAMPS: primary hepatocytes and liver sinusoidal endothelial cells (LSECs) as well as Kupffer-like cells (THP-1) and hepatic stellate cells (LX-2). It recapitulates key physiological characteristics previously established in the LAMPS platform, including oxygen zonation–dependent liver phenotypes including model viability, secretion of functional and cytotoxicity markers, mitochondrial activity, and lipid accumulation, demonstrating reproducibility in the ht-LAMPS format. Finally, we also demonstrate that the ht-LAMPS model recapitulates key phenotypes associated with the progression of metabolic dysfunction–associated steatotic liver disease (MASLD), including increased steatosis and elevated production of inflammatory cytokines and profibrotic markers using our established MASLD media formulations. Overall, by increasing throughput while maintaining key high-content biological features of the LAMPS, ht-LAMPS provides a scalable platform for investigating liver function, modeling disease progression, and enabling downstream drug testing in MASLD and other liver-related conditions.