New three-dimensional models for the study of normal and diseased human neural cells

Supervisor: Dr Patrizia Ferretti

There is much need for developing 3D (3-dimensional) systems that can better mimic the behaviour of human tissue and provide reliable models for studying normal and abnormal human neural cells, and for establishing better platforms for the assessment of human cell response to insult and putative therapeutic compounds.

This PhD proposal aims to establish such systems focusing on modelling the behaviour of i) human neural stem/progenitor cells (hNSCs) and their differentiated progeny, and ii) of paediatric neural tumours, in order to use them to compare responses of these different cell types to various insults and anticancer drugs.

Neural differentiation and development as well as growth of neural tumours has been much studied in animal models, but it is apparent that not all the information acquired from these models can be directly applied to humans. Two-dimensional cultures do not reproduce the spatial architecture and interactions of cells found in the developing central nervous system (CNS) or in a tumour. Preliminary experiments we have carried out using various 3-dimensional (3D) extracellular matrices show a very different behaviour between hNSCs and neuroblastoma cells in 3D cultures, with the former establishing networks and the latter a tumour-like mass, as in vivo. Furthermore, collaborative work with colleagues at the UCL Centre for Nanotechnology and UCL Mechanical Engineering has shown distinct cellular responses of hNSCs to different nanomaterials and that electric field driven patterning techniques have no negative effect on hNSC morphology and survival. Well defined human CNS 3D models, however, are still lacking. Therefore, in order to better understand human NSC development, and assess potential damage of existing and novel anticancer compounds to the young CNS, as well as their efficacy in eliminating/ reducing tumour mass, it is imperative to develop 3D in vitro models reproducing appropriate extracellular environment and structure.


  • To further characterize human neural development, with a focus on neural progenitor maturation, in sections of human embryos, to provide a baseline for assessing human neural cell maturation in 3D systems.
  • To characterize the behaviour of hNSCs and their ability to differentiate and become organized in a tissue-like fashion in different 3D matrices and patterns in order to identify the conditions that better mimic in vivo development and mature neural tissue.
  • To assess the potential of selected 3D systems for testing the response of NSCs and their progeny and of paediatric neural tumours (e.g. neuroblastoma, gliomas) to putative toxic agents and anticancer agents with the view to developing high throughput assays.
  • This project will provide an excellent multidisciplinary training. The student will not only acquire in depth knowledge of stem cell biology and of a broad range of cellular and molecular techniques, including cell culture, protein and RNA analysis immunocytochemistry, Western blot, q-PCR), imaging techniques (fluorescence, confocal and time-lapse microscopy), but also expertise in material science.

1) Guasti, L., Vagaska, B., Bulstrode, N.W., Seifalian, A., Ferretti, P., 2013. Chondrogenic differentiation of adipose tissue-derived stem cells within nanocaged POSS-PCU bioscaffolds: a new tool for nanomedicine. Nanomedicine. Accepted pending revision.
2) Prasongchean W et al. Amniotic fluid stem cells increase embryo survival following injury. Stem Cells Dev. (2012) 21:675-688.
3) Thalhammer, A., Edgington, R.J., Cingolani, L.A., Schoepfer, R., Jackman, R.B., 2010. The use of nanodiamond monolayer coatings to promote the formation of functional neuronal networks. Biomaterials 31, 2097-2104.
4) Jayasinghe SN et al. Bio-electrosprayed living microenvironments implanted into mouse models, Macromol Biosci, (2011) 11:1364-1369.
5) Illes S, et al. Niche-dependent development of functional neuronal networks from embryonic stem cell-derived neural populations. BMC Neuroscience 2009, doi:10.1186/1471-2202-10-93.