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Focal Adhesion Kinetics In nanosurface Recognition

Periodic Reporting for period 3 - FAKIR (Focal Adhesion Kinetics In nanosurface Recognition)

Reporting period: 2018-08-01 to 2020-01-31

The provision of advanced functional materials to provide the appropriate targeted function in the area of regenerative medicine and discovery depends on many different factors. As adherent cells “read” their environment through interactions with the substrate, there is great interest in developing such materials in a predictable manner. A cell’s first point of contact is through so-called focal adhesions and it is also through them that forces are applied allowing the cell to migrate and establish cytoskeletal tension which in turn regulates cell function. The objective of this project is to investigate the cell-substrate interaction at the nanoscale and link that response to the nanoscale patterns so that predictable biomaterials can be manufactured. Through the application of state-of-the-art nanofabrication we fabricate precise surface nanopatterns with length scales comparable to the structural units found in a cell’s focal adhesions. The aim is to map and understand the topographical influence in the architectural arrangement of the proteins in such adhesions. Aided by super resolution microscopy we will classify cell types on different nanotopographies. Combining that information with machine learning, we will be able to gain information about cell characteristics from the rule set. That information can also be used in reverse to identify cell types with the previously defined characteristic. This approach is similar to face recognition seen on cameras and mobile phones.

The proposed research project will not only provide insight to an area of biomaterials not previously explored, but also aim to provide a blueprint for future design of biomaterials.
This is an ambitious project entering an area of biomaterials design not previously explored. This has required the establishment of new methods in 3 areas: 1) For us to be able to explore and quantify biological activities at the nanoscale in real time, it has been necessary to develop new super resolution microscopy techniques. This involves both microscope design and development mathematical algorithms to reconstruct the data. These methods are now in place and we are now starting to acquire the first datasets. 2) The materials required for our experiments have what appear to be two opposing requirements. In one situation we need to be able to make 100s-1000s of samples. This radical upscaling of sample manufacturing is unique in an academic setting and is solved by injection moulding and assembly of the parts in standard formats, e.g. multiwell plates. This enables us to collect very large datasets of microscopy images from a large range of materials and at the same time collect information on molecular biology activities for the different materials. However, on the other hand for super resolution microscopy, few samples are required but the format is very different. Each sample requires high precision manufacturing to deliver very high optical quality substrates. 3) Then to combine all our information, we apply machine learning and regression models which can link material properties/design with biological response.
This is still in the early stage of the project and thus ave not yet generated significant impact. However, the expected impact will be better biomaterials for future applications for implants, drug screening and diagnostics.
Super resolution image of focal adhesion dynamics on a nanopatterned substrate
Super resolution image of focal adhesion dynamics on a nanopatterned substrate