Our major goal is to accelerate biomedical research by developing and combining enabling technologies. We aim to use this approach to solve some of the major bottlenecks in research. In particular, we combine biomedical research with artificial intelligence (AI), and nanotechnology. We also use engineering to develop tools to implement our technologies directly in the medical system. 

We foresee that an unbiased approach, from imaging to molecular analysis (transcriptomics and proteomics), will be very powerful for tackling major diseases. We are also keen to use engineered tissues and organoid models to scale up the translation of human diseases.

Currently, we have following major research fields in our institute: 


Using our unique tissue clearing approach paired with AI deep learning algorithms we have examined the nervous system in several studies:

  • Studying the role of skull-meninges connections (Cai…Ertürk, 2019, Nature Neuroscience) and exploring their potential as a route to deliver drugs into the brain
  • Mapping the human brain using SHANEL transparency (Zhao…Ertürk, 2020, Cell)
  • Exploiting unbiased vascular analysis (Todorov…Ertürk, 2020, Nature Methods) to investigate brain pathologies including stroke and dementia


Implementing DeepMACT method to develop better cancer treatments in the pre-clinical arena (Pan…Ertürk, Cell, 2019). We use organoid models of human cancers in combination with our tissue clearing and AI-based approaches.

Tissue Engineering

We use tissue engineering including organoids and 3D-bioprinting technologies to develop new models of human diseases. Besides reducing the use of animals, this approach can generate highly personalized tissues (using patient’s own cells) that are quicker to produce and use in high-throughout screens.

One of our major goals is to generate large-scale human tissues and organs using 3D-bioprinting technologies. In particular, we make use of the unique cellular maps of human organs that we can generate using our SHANEL technology (Zhao…Ertürk, Cell 2020).