Lung-Liver Interactions: A Multi-Scale In Vitro Systems Analysis

Individual cells integrate many external cues — including those that arise from various extracellular matrix components, mechanical stimulation and soluble signals from adjacent and even distant cells — to generate a basal phenotype and respond to perturbations in their environment [1]. Yet, to capture the complexity of human physiology in vitro demands the evaluation of interacting cell types coexisting in a hierarchical 3-dimensional structure influenced by gradients in nutrients, mechanics, and cell composition. Ultimately, to computationally model integrated responses of multiple cells types (including immune cells) requires the need to assess systems under both homeostatic and pathologic conditions. In this study, we propose to integrate communication between lung-liver tissue modules. Specifically, we investigated how changes in the immune cell populations regulate the response to inflammatory signals, such as cytokines and chemokines, and corroborated the effects of cell function and viability across a broad spectrum of metrics.

To investigate the effects of inflammatory stimuli, multi-scale approaches provide a basic framework to assess liver and lung tissues in isolation, as well as in an interacting system. Multivariate analyses of resultant phenotypic responses to external cues were assessed in a dynamic fashion. In this study, we systematically evaluated organ systems crosstalk in the presence of multiple factors, including stimuli that mimic bacterial (e.g. LPS), viral responses (e.g. polyI:C) or cytokines in the presence or absence of drugs to minimize inflammation. Experimentally, standard metrics for cell function were implemented, i.e. retention of Cyp450 enzymes, secretion of serum proteins, gene/protein expression, and immunofluorescence.

Due to the intrinsic complexity of biological systems, integration of experimental and computational approaches is crucial to transform the way we study human tissue physiology and pathophysiology in vitro. Here, a systems biology perspective – including the use of statistical techniques such as PCA and PLSDA – led to novel insights attributed to cytokine profiles and signaling networks across a range of physiological perturbations, with emphasis on development and validation of predictive models for cell and tissue behavior relevant in drug discovery.

[1] Griffith, L. and Swartz, M. Capturing complex 3D tissue physiology in vitro. Molecular Cell Biology 2006:211-224.