[Research Vision]
PRISM Laboratory aims to bridge biofabrication, biomaterials, and translational medicine by developing next-generation human tissue models that accurately replicate complex human physiology and pathology.
Our long-term goal is to establish clinically predictive in vitro systems that accelerate:
· Disease mechanism discovery
· Precision therapeutics
· Drug screening
· Regenerative medicine
· Personalized healthcare
[Core Technologies]
1. Tissue-Specific dECM Bioinks
We engineer biomimetic bioinks derived from native tissues to recreate physiological extracellular microenvironments.
2. Integrated Multi-Modal Bioprinting
Our platforms combine extrusion printing, coaxial printing, and multi-nozzle systems for fabrication of heterogeneous tissues.
3. Organ-on-a-Chip Systems
We develop perfusable microphysiological systems for disease modeling and drug testing.
4. Vascularized Tissue Engineering
We reconstruct functional vascular structures to enhance physiological relevance and tissue maturation.
[Multi-Organ Disease-on-a-Chip for Type 2 Diabetes]
We developed a tissue-specific multiple-organ-on-a-chip platform to emulate the complex pathophysiology of Type 2 Diabetes (T2D) using 3D cell printing and decellularized extracellular matrix (dECM) bioinks. The platform integrates pancreas, liver, adipose tissue, and retinal compartments within a single perfusable microphysiological system.
The system reconstructs:
· Hyperglycemic microenvironments
· Visceral adipose tissue-mediated inflammation
· Inter-organ metabolic crosstalk
· Retinal diabetic complications
Using tissue-derived dECM bioinks, the platform reproduces organ-specific extracellular environments and enables clinically relevant drug response evaluation. This model provides a powerful tool for studying metabolic disease progression and diabetic complications.
Key Technologies
· Tissue-specific dECM bioinks
· Multi-head integrated 3D bioprinting
· Dynamic perfusion culture
· Multi-organ microphysiological systems
· Disease-specific inflammatory modeling
· Representative Publication
[Retina-on-a-Chip for Retinal Vein Occlusion (RVO)]
We developed an advanced retina-on-a-chip platform capable of recapitulating retinal vein occlusion (RVO), one of the major retinal vascular diseases leading to vision loss. The system integrates retinal-derived dECM bioinks with triple-coaxial vascular bioprinting to reconstruct the blood-retinal barrier (BRB) and interconnected vascular structures. The platform successfully reproduced clinically relevant RVO progression and therapeutic drug responses.
To induce pathological RVO conditions, vascular stenosis was generated through controlled vascular printing parameters and LDL perfusion, enabling:
· Progressive vascular narrowing
· Endothelial dysfunction
· Macrophage infiltration
· BRB breakdown
· Ischemic retinal damage
Key Technologies
· Hybrid retinal dECM bioink
· Triple-coaxial vascular bioprinting
· Blood-retinal barrier reconstruction
· Perfusable vascular microenvironment
· Retinal disease modeling
[Bioengineered Neural Networks (BENN)]
Our laboratory developed a 3D bioprinted engineered neural network (BENN) platform to replicate the compartmentalized structure and aligned axonal architecture of the brain. The system combines brain-derived dECM bioinks with electrical stimulation-guided axonal organization to generate unidirectional neural networks.
The BENN platform enables:
· Reconstruction of gray and white matter-like regions
· Directed axonal growth
· Functional neuronal signal propagation
· Visualization of neurodegenerative pathology
Using alcohol-induced neurodegeneration models, the platform successfully visualized:
· Axonal deformation
· Amyloid-beta formation
· Region-specific degeneration
· Neural connectivity disruption
This system provides a robust in vitro platform for studying neurodegenerative disease mechanisms and neural network dynamics.
Key Technologies
· Brain-specific dECM bioink
· 3D neural bioprinting
· Electrical stimulation-guided axonal alignment
· Neural compartmentalization
· Neurodegenerative disease modeling