3DbioNet is a Technology Touching Life Network. The network is officially funded and the project is starting in January 2019 with a Launch Workshop. If you would like to keep informed, you can sign up to become a member (free) and follow us on Twitter. Below is a quick summary of the scope and ambition of the network.
Conventional two-dimensional cell models have been pivotal in advancing knowledge of cell biology. They helped understand mechanisms of disease and contributed to the discovery of drugs that have improved human health. Nevertheless, these models are a poor representation of human tissue physiology and its pharmacological response to drugs. Moreover, animal models are often not accurate models of human disease either; for example, 38 to 51% of compounds eliciting liver injury in man do not show similar effects in animal studies .
The increasing awareness of these problems has led to the development of 3D cell culture models of human tissues. This includes unicellular models, multicellular models based on known cellular compositions of particular human tissues, as well as tissue stem cell-derived organoids, collectively termed here micro-tissues. These have enormous potential for helping to elucidate human physiology, mechanisms of diseases and how these may be safely treated .
However, exploitation of these opportunities are limited by major challenges when compared with 2D culture. Drug discovery, cell therapy and personalised medicine applications require suites of new technologies that replicate biophysical cell growth conditions, enhance the reproduciblity of micro-tissue handling and, importantly, provide analytical options that capture the complexity of the cellular structures that can now be generated. This requires sustained interdisciplinary collaborations and innovations in the physical sciences.
Indeed, many of the routine research methods perfected over decades do not easily translate from 2D to 3D. To provide a biomimetic cell environment and facilitate handling, innovative scaffold materials and bioengineering processes are often necessary. To investigate complexity, technologies that perturb and record (live and in 3D), the (tens of) thousands of single cells and their biochemical responses are required. We also need tools to handle and analyse large data sets, and compare them with advanced mathematical and computational models. Such theoretical models serve three key purposes: to identify the optimal operating conditions for engineering a particular microtissue, to assist experimental design (to identify the times and physical quantities that should be measured to validate the models), and, predictively, to translate results from the 3D microtissues to in vivo .
3DBioNet will assemble researchers who together possess the expertise needed to address these challenges including engineers (3D printing, microfluidics), physicists (advanced imaging, biomechanics), chemists (scaffold materials, dyes with high penetration into tissues), mathematicians (modeling the physiological and pharmacological behaviour of 3D complex systems) and biologists, biomedical scientists and relevant industrial stakeholders. Through our interactions with clinical groups, we will also assess the human relevance of the improvements in the 3D culture models overseen by 3DBioNet, for example in the areas of gastrointestinal and hepatic organoids.
In summary, we will strengthen the position of the UK as a leader in the field of 3D micro-tissues by establishing a new, multidisciplinary network of researchers from academia and industry united by a common vision for developing a technological platform to support 3D cell culture models throughout their life cycle.
- Goldring C, Antoine DJ, Bonner F, et al. Stem cell-derived models to improve mechanistic understanding and prediction of human drug-induced liver injury. Hepatology 2017 ; 65 : 710–721.
- Huch M, Knoblich JA, Lutolf MP, et al. The hope and the hype of organoid research. Development 2017 ; 144 .
- Collis J, Connor AJ, Paczkowski M, et al. Bayesian Calibration, Validation and Uncertainty Quantification for Predictive Modelling of Tumour Growth: A Tutorial. Bull. Math. Biol. 2017 ; 79 : 939–974.