(620b) 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.