Automated and Multiplexed Organs-on-Chips

Automated, multiplexed cell culturing chips with high spatiotemporal resolution are essential for multiparameter screening and studying response kinetics. Here at AST we are developing highly multiplexed microfluidic chips with integrated valves for programmable cell culture medium exchange and stimulation factor exposure.

Open-TOP (Translational Organ-on-Chip Platform)

The Translational Organ-on-chip Platform (TOP) aims to bridge the gap between organ-on-chip developers and end-users by connecting microfluidic building blocks, such as organs-on-chips and sensors, on fluidic circuit boards in an openly standardized manner. The use of a common, standardized platform facilitates collaboration and chip transfer between partners from different labs to achieve higher level (multi-) organ-on-chip systems. The TOP can be used for a wide range of applications, such as highly parallelized cell culturing, automated medium recirculation and online sensing.

Researchers

Postdoc

Meike Große-Elshoff

MSc student

Associate professor

Photoresponsive Hybrid PDMS-Hydrogel Microfluidic Valving

Develop hybrid PDMS-hydrogel valves (PHVs) using PDMS membranes and photoresponsive hydrogel materials for organ-on-chip applications. A new, orthogonal microfluidic actuation system would compliment the already multiplexed TOP platform by allowing users to select any combination of culture chambers for perturbation or variable flow conditions. Through the combination of digital micromirror devices (DMDs) and strategically positioned PHVs, flow conditions could be altered mid-experiment without the need for mechanical valving or physical handling of the system. 

Grant
Standardized open Modular Approach to Recapitulate Tissues (SMART) Organ-on-Chip

Researchers

PhD candidate

Associate professor

Multiplex micro-engineered patient-specific tissues array for artificial intelligence disease prediction

This project will focus on developing a microfluidic array of micro-engineered heart tissues (µEHT) that combine with artificial intelligence (AI) software, aims to predict disease developments in patients. The microfluidic networks will allow users to control the perfusions of each µEHT. Besides, we will assess the effect of cytokines and different drug compounds on patient-specific µEHT.  This platform will revolutionize not only research methods in academia, but also for clinical test in hospitals and drug development in industries.

Grant
Digipredict

Researchers

PhD candidate

Postdoc

Associate professor

Guest researcher