Experimental models are used in medical research, including cancer research, to investigate normal biological processes and how these are altered in human disease. Commonly used models include in vitro tissue culture, where cell lines or primary cells are cultured as a flat layer of cells in a dish. Whilst this approach has yielded many important insights, for processes where cell-to-cell interactions or the structure of the tissue are thought to be important, this two-dimensional model system does not represent natural physiology.
Researchers are now developing new experimental models that aim to be more similar to in vivo tissue architecture. Organoids are three-dimensional multicellular cultures obtained by growing cells in a carefully designed cocktail of biochemical factors, sometimes with the aid of an artificial scaffold, to approximate the structure of naturally occurring tissues. An alternative approach is to 3D-print tissues, so that cells are placed precisely in position relative to neighbouring cells. Professor Hagan Bayley and colleagues from the Department of Chemistry are pioneering innovative methods for high-resolution 3D-printed tissues.
The Bayley lab's technique involves printing very small aqueous droplets coated in a monolayer of lipids. When these droplets come together, they form lipid bilayers that are similar to cell membranes. By building up layers of printed droplets, a synthetic 3D tissue structure can be formed. In their latest work, published in Nature Communications, lab members Drs Alessandro Alcinesio and Ravinash Krishna Kumar have discovered a key parameter that influences how well printed droplets pack together. With this new understanding about the optimum contact angle for packing, the researchers are able to improve the precision of printing to enable the creation of more complex functional synthetic tissues. The improved technology will be used to build tissues for future cancer research, for example to investigate how cancers initiate, potentially revealing new early detection biomarkers, and to screen drugs for therapeutic efficacy.