This week will feature Dr Alistair Curd from the University of Leeds
Scientific Organisers: Stefanie Reichelt, Alex Sossick, Nick Barry, Alessandro Esposito and Kirti Prakash
The meeting will begin at 1pm BST.
As part of the 'Imaging ONEWORLD' series, the focus of these lectures is on microscopy and image analysis methods and how to apply these to your research. Almost all aspects of imaging such as sample preparation, labelling strategies, experimental workflows, ‘how-to’ image and analyse, as well as facilitating collaborations and inspiring new scientific ideas will be covered. Speakers will be available for questions and answers. The organisers, CRUK CI core facility staff, Gurdon Institute, MRC-LMB, MRC Cancer Unit and NPL will be able to continue the discussion and provide advice on your imaging projects.
University of Leeds
Alistair is a Research Fellow in Michelle Peckham’s group at the University of Leeds. Following his physics degree at the University of Oxford, he has continued to pursue his interest in light and our perception of it, in industry and academia. Over several years’ R&D in Sharp Corporation, he invented and co-invented a variety of optical systems and components for display technology and lighting, and was recognised for the commercial impact of his anti-reflection system internal to a liquid crystal display. He followed this in 2011 with a PhD in vision science at the University of Bradford, where he studied the dynamic optical aberrations of human eyes, using adaptive optics and aberrometry to control and measure the visual stimulus and response. In 2014 Alistair began working at the University of Leeds and has constructed an instant structured illumination microscope and single-molecule localisation microscopy systems, as well as an OpenSPIM he has exhibited for the public, including at MMC2017. He enjoys collaborating with biologists on experiments using these and other microscopy systems, which also leads to research in data analysis methods so that new information and conclusions can be found.
As a single-molecule localisation microscopy (SMLM) community, we would like to be able to take data on many instances of a supramolecular complex and use it to find the organisation of the complex at a resolution of ~10 nm or better. However, in many experiments on new biology with SMLM, many instances of a molecule of interest (e.g. a particular protein) can remain unlocalised, because of limited labelling efficiency, limited switching efficiency of the fluorescent label and high background signal, which is a more severe problem when obtaining information in 3D. PERPL (Pattern Extraction from Relative Positions of Localisations) allows us to find average structure from many instances of a supramolecular complex, even when only detecting a small number (e.g. < 1 %) of the target molecules of interest. It does not require image segmentation or particle averaging of these sparse datasets, which would be very challenging. We can do this by building different models for the relative position distribution (RPD) expected between pairs of molecules in 2D or 3D, and comparing the RPD from experimental SMLM data with these models. I will illustrate the details of this method with both a well-known nuclear pore SMLM dataset and challenging 3D datasets on cardiomyocyte Z-disk protein arrangements with a ~20 nm length scale.