High-throughput organ-on-chip platform with integrated programmable fluid flow and real-time sensing for complex tissue models in drug development workflows.

Drug development suffers from a lack of predictive and human-relevant in vitro models. Organ-on-chip (OOC) technology provides advanced culture capabilities to generate physiologically appropriate, human-based tissue in vitro, therefore providing a route to a predictive in vitro model. However, OOC technologies are often created at the expense of throughput, industry-standard form factors, and compatibility with state-of-the-art data collection tools. Here we present an OOC platform with advanced culture capabilities supporting a variety of human tissue models including liver, vascular, gastrointestinal, and kidney. The platform has 96 devices per industry standard plate and compatibility with contemporary high-throughput data collection tools. Specifically, we demonstrate programmable flow control over two physiologically relevant flow regimes: perfusion flow that enhances hepatic tissue function and high-shear stress flow that aligns endothelial monolayers. In addition, we integrate electrical sensors, demonstrating quantification of barrier function of primary gut colon tissue in real-time. We utilize optical access to the tissues to directly quantify renal active transport and oxygen consumption via integrated oxygen sensors. Finally, we leverage the compatibility and throughput of the platform to screen all 96 devices using high content screening (HCS) and evaluate gene expression using RNA sequencing (RNA-seq). By combining these capabilities in one platform, physiologically-relevant tissues can be generated and measured, accelerating optimization of an in vitro model, and ultimately increasing predictive accuracy of in vitro drug screening.

An ERP analysis of implicit structured sequence learning.

When task exposure facilitates performance without producing corresponding changes in verbalizable knowledge, learning is said to be implicit. In Experiment 1, event-related potentials (ERPs) were recorded as individuals practiced an implicit structured sequence learning (ISSL) task wherein only some target events required a response. With practice, the ERPs to targets that obeyed the underlying grammar diverged from those that did not at around 200 ms; grammatical targets appeared to be more positive between 200 and 500 ms because a similar positivity for the ungrammatical targets was delayed. In Experiment 2, the grammar was simplified allowing a direct comparison to be made between an implicit learning group and an explicit group, who were taught the grammar prior to recording. The results of the comparison revealed a remarkable similarity but did implicate at least partially nonidentical neural mechanisms in implicit and explicit structured sequence learning.