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Updated: Feb 28, 2026

Combining Human Organoids and Organ-on-a-Chip Technology to Model Intestinal Region-Specific Functionality
Published on: May 5, 2022
A 3D-Printed Pump-Free Multi-Organ-on-a-Chip Platform for Modeling the Intestine-Liver-Muscle Axis
Rodi Kado Abdalkader1, Takuya Fujita1,2
1Ritsumeikan Global Innovation Research Organization (R-GIRO), Ritsumeikan University, Kusatsu 525-8577, Shiga, Japan.
Abstract:
The intestine-liver-muscle axis plays an essential role in drug and nutrient absorption, metabolism, and energy balance. Yet in vitro models capable of recapitulating this inter-organ communication remain limited. Here, we present a pump-free, 3D-printed multi-organ-on-a-chip device that enables dynamic co-culture of Caco-2 intestinal epithelial cells, HepG2 hepatocytes, and primary human skeletal myoblasts (HSkMs) under gravity-driven oscillatory flow. The device consists of five interconnected chambers designed to accommodate Transwell cell culture inserts for intestine and muscle compartments and hydrogel-embedded hepatocyte spheroids in the central hepatic compartment. The device was fabricated by low-cost fused deposition modeling (FDM) using acrylonitrile butadiene styrene (ABS) polymers. Under dynamic rocking, oscillatory perfusion promoted inter-organ communication without the need for external pumps or complex tubing. Biological assessments revealed that dynamic co-culture significantly enhanced the characteristics of skeletal muscle, as indicated by increased myosin heavy chain expression and elevated lactate production, while HepG2 spheroids exhibited improved hepatic function with higher albumin expression compared with monoculture. Additionally, Caco-2 cells maintained stable tight junctions and transepithelial electrical resistance, demonstrating preserved intestinal barrier integrity under dynamic flow. These results establish the device as a versatile, accessible 3D-printed platform for modeling the intestine-liver-muscle axis and investigating metabolic cross-talk in drug discovery and disease modeling.

