(69a) Engineering a Physiologically Relevant Model of the Cardiac Autonomic Nervous System Conference: AIChE Annual MeetingYear: 2018Proceeding: 2018 AIChE Annual MeetingGroup: Food, Pharmaceutical & Bioengineering DivisionSession: Cells, Organs, and Labs on a Chip I: Modeling Cell Interactions Time: Monday, October 29, 2018 - 8:00am-8:18am Authors: Soucy, J., Northeastern University Torregrosa, T., Northeastern University Hosic, S., Northeastern University Annabi, N., Northeastern University Koppes, A., Northeastern University Koppes, R., Northeastern University Engineering a Physiologically Relevant Model of the Cardiac Autonomic Nervous System Jonathan R Soucy1, Tess Torregrosa1, Sanjin Hosic1, Nasim Annabi1,2, Abigail N Koppes1,3, Ryan A Koppes1 1Chemical Engineering, Northeastern University, Boston, MA 2Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 3Biology, Northeastern University, Boston, MA Introduction: The autonomic nervous system (ANS) functions to maintain homeostasis in the heart via the complementary functions of the sympathetic nervous system (SNS) and the parasympathetic nervous system (PSNS) [1]. However, ANS dysfunction can lead to increased cardiac arrhythmias and even sudden cardiac failure due to an inability to effectively modulate heart hate following overexertion or excessive stress [2-4]. Typically, dysautonomia arises to meet the increased cardiac demands of a damaged myocardium by promoting SNS hyperinnervation/activation and PSNS withdrawal/deactivation [1, 5, 6]. This understanding of the cardiac ANS pathophysiology has led to development of beta-blocker therapies to inhibit SNS activity systemically [7-9], and an investigation into vagal nerve stimulation to increase PSNS function [10-13]. Yet, there is no clear understanding of how to mediate an ANS imbalance, nor are the underlying cellular mechanicals for cardiac innervation well understood. Traditionally, animal models have been used to investigate cardiac ANS dysfunction, but due to their inherent complexities and variability, in vitro alternatives must be developed [14]. Microfluidic devices are an attractive platform to develop innervated muscle organ systems in vitro, but only recently have been applied to cardiac system [14-16]. In these previous works, the authors chose to develop models focused primarily on compartmentalization and the hierarchical structure of the neuro-cardiac axis, rather than a more physiologically relevant 3D cell culture, which may be necessary to mimic cardiac innervation in vivo [17]. Therefore, to better recapitulate the in vivo environment, we aim to engineer a microphysiological system to culture cardiac cells and ANS neuron populations in a biomimetic scaffold. Methods: Custom microfluidic chips were fabricated using a novel laser cut and assembly method via a commercial laser engraver system to cut and shape acrylic sheets, double sided adhesive, and a polycarbonate track etched membrane to support the 3D culture of cardiac cells and both SNS and PSPS neurons (Figure 1). The devices utilize a phase guide to compartmentalize cell-laden 3D hydrogels in the basal channel, and polycarbonate membrane to enable medium diffusion from the apical channel and mimic circulation. Polycarbonate provides benefits in minimizing analyte adsorption compared to traditional PDMS platforms [18]. Primary neonatal rodent cardiac cells from the heart, cholinergic neurons from the intracardiac ganglia, and adrenergic neurons from the superior cervical ganglia will be isolated to develop our model of the cardiac ANS [14]. Cells will be encapsulated within a photocrosslinkable gelatin based hydrogel, gelatin methacrylate (GelMA) in situ using our custom microfluidic chips. Specifically, GelMA will be crosslinked in the presence of lithium phenyl-2,4,6-trimethylbenzoylphosphinate (LAP, BioBots) using an in-house LED photocrosslinking system (405nm). Cardiac output within the innervated systems will be assessed with a custom MATLAB code to calculate beating on a cell-by-cell basis using video microscopy, and results compared to non-innervated cardiac cultures. Results and Discussion: Towards our goal of recapitulating the ANS innervation into cardiac tissue, using a commercially available chip (DAX1, AimBiotech), we have successfully encapsulated cardiac cells in a biomimetic gelatin scaffold in situ and observed SNS innervation (Figure 2). However, this commercially available microfluidic system does not support the 3D encapsulation and compartmentation of the neural components of the cardiac ANS. Therefore, we have fabricated a custom laser cut microfluidic chip to support 3D compartmentalized cell culture of cardiac cells and both ANS neuron populations. Notably, this device design permits neurons to be encapsulated the day prior to the addition of cardiac cells, which allows for the necessary handling time of each cell population. Additionally, our custom chip contained a tight and well-defined hydrogel boundary between compartments so that innervation will be unobstructed and easier to quantify (not shown). We have demonstrated an ability to measure cardiac output (beat rate and beating synchrony) of encapsulated cardiac cells cultured in microfluidics chips using video microscopy. Further, we have shown that cardiac cells encapsulated using our visible light crosslinking platform have significantly greater cell viability compared to systems using UV light for cardiac cell encapsulation. Future experiments will be to incorporate the primary SNS and PSNS neurons in situ to investigate their rate of innervation, in addition to how their spontaneous firing will affect cardiac output. This development of a physiologically relevant model of the cardiac ANS will enable the systematic investigation of novel therapies to promote/prevent the intervention of different neural populations, in addition to improving our understanding of cardiac dysautonomia. References: [1] Ripplinger et al., Prog Biophys Mol Biol, 2016. 120(1-3): p. 199-209. [2] Cao et al., Circ. Res., 2000. 86(7): p. 816-21. [3] Chrousos and Gold, JAMA, 1992. 267(9): p. 1244-1252. [4] Ben-Zvi et al., PLoS Comput. Biol., 2009. 5(1). [5] Binkley et al., JACC, 1991. 18(2): p. 464-472. [6] Hou et al., Heart Rhythm, 2016. 13(2): p. 584-92. [7] Florea and Cohn, Circ. Res., 2014. 114(11): p. 1815-1826. [8] Kobayashi et al., Ann Thorac Surg, 2013. 96(1): p. 339-45. [9] Triposkiadis et al., J Am Coll Cardiol, 2009. 54(19): p. 1747-62. [10] Vanoli et al., Circ. Res., 1991. 68(5): p. 1471-81. [11] Calvillo et al., Journal of , 2011. [12] Klein and Ferrari, Cardiology journal, 2010. 17(6): p. 638-644. [13] Brack et al., Cardiovascular Research, 2011. 91(3): p. 437-446. [14] Oiwa et al., Integr Biol (Camb), 2016. 8(3): p. 341-8. [15] Takeuchi et al., Lab Chip, 2011. 11(13): p. 2268-75. [16] Takeuchi et al., Integr Biol (Camb), 2012. 4(12): p. 1532-9. [17] Miwa et al., PLoS One, 2013. 8(7): p. e65202. [18] Toepke and Beebe, Lab Chip, 2006. 6(12): p. 1484-1486. Topics: Biological Engineering