An integrated platform to assay melanoblast and melanoma invasion of epidermis in vitro

Mathematical and computational modelling are powerful techniques that allow the testing of hypotheses in silico before actual experiments are conducted. We are currently using these techniques to replace the use of animals in our own research into neural crest stem cells (NCSCs). Melanoblasts are a NCSC subtype that populate the embryonic epidermis and become the pigment producing melanocytes found in skin and hair follicles. Defects in their development lead to pigmentary disorders whilst reactivation of their developmental program causes metastatic melanoma. Recent advances in imaging technologies have allowed melanoblasts to be studied using time-lapse confocal microscopy and for melanoma metastasis to be investigated using intra-vital imaging. These methodologies are powerful and informative but the burden on animal resources is high, especially where complicated genetic crosses are required.

This project is designed to develop an in vitro system to assay melanoblast behaviour and melanoma cell metastasis in an epidermal organ substitute using live imaging, image analysis and mathematical modelling. We will genetically engineer keratinocyte cell lines which can then be differentiated into epidermis and used as an environment in which to assay the behaviour of primary melanoblasts and melanocyte/melanoma cell lines. In parallel we will use mathematical modelling of NCSC behaviour and the key signalling pathways that modulate this behaviour to refine and explore our in vitro system.

Our approach will reduce the number of animals required to study melanoblast development as well as establishing a tractable easily manipulated model system. By altering the host-tissue's genetic background in vitro and by choosing NCSC lines derived from different genetic backgrounds, complex genetic experiments can be conducted with great flexibility and without the need for a large mouse breeding program. Furthermore the use of live-imaging and careful mathematical modelling and prediction to refine our experimental approach will allow us to develop an efficient pipeline for pathway analysis further reducing animal usage.