ERC FUNDED PROJECTS

Recent technological advances in optical microscopy have vastly broadened the possibilities for applications in the biomedical sciences. Fluorescence microscopy is the central tool for investigation of molecular structures and dynamics that take place in the cellular and tissue environment. Coupled with progress in labeling methods, these microscopes permit observation of biological structures and processes with unprecedented sensitivity and resolution. This work has been enabled by the engineering development of diverse optical systems that provide different capabilities for the imaging toolkit. All such methods rely upon high fidelity optics to provide optimal resolution and efficiency, but they all suffer from aberrations caused by refractive index variations within the specimen. It is widely accepted that in many applications this fundamental problem prevents optimum operation and limits capability. Adaptive optics (AO) has been introduced to overcome these limitations by correcting aberrations and a range of demonstrations has shown clearly its potential. Indeed, it shows great promise to improve virtually all types of research or commercial microscopes, but significant challenges must still be met before AO can be widely implemented in routine imaging. Current advances are being made through development of bespoke AO solutions to individual imaging tasks. However, the diversity of microscopy methods means that individual solutions are often not translatable to other systems. This proposal is directed towards the creation of theoretical and practical frameworks that tie together AO concepts and provide a suite of scientific tools with broad application. This will be achieved through a systems approach that encompasses theoretical modelling, optical engineering and the requirements of biological applications. Additional outputs will include practical designs, operating protocols and software algorithms that will support next generation AO microscope systems.

3 234 789 €

Start date: 2016-09-01, End date: 2021-08-31

Neurons make long-distance connections with synaptic targets via axons. These axons survive throughout the lifetime of an organism, often many years in mammals, yet how axons are maintained is not fully understood. Recently, we provided in vivo evidence that local mRNA translation in mature axons is required for their maintenance. This new finding, along with in vitro work from other groups, indicates that promoting axonal protein synthesis is a key mechanism by which trophic factors act to prevent axon degeneration. Here we propose a program of research to investigate the importance of ribosomal proteins (RPs) in axon maintenance and degeneration. The rationale for this is fourfold. First, recent genome-wide studies of axonal transcriptomes have revealed that protein synthesis (including RP mRNAs) is the highest functional category in several neuronal types. Second, some RPs have evolved extra-ribosomal functions that include signalling, such as 67LR which acts both as a cell surface receptor for laminin and as a RP. Third, mutations in different RPs in vertebrates cause unexpectedly specific defects, such as the loss of optic axons. Fourth, preliminary results show that RP mRNAs are translated in optic axons in response to trophic factors. Collectively these findings lead us to propose that locally synthesized RPs play a role in axon maintenance through either ribosomal or extra-ribosomal function. To pursue this proposal, we will perform unbiased screens and functional assays using an array of experimental approaches and animal models. By gaining an understanding of how local RP synthesis contributes to axon survival, our studies have the potential to provide novel insights into how components conventionally associated with a housekeeping role (translation) are linked to axon degeneration. Our findings could provide new directions for developing therapeutic tools for neurodegenerative disorders and may have an impact on more diverse areas of biology and disease.

2 426 573 €

Start date: 2013-03-01, End date: 2018-09-30

Pericytes, located at intervals along capillaries, have recently been revealed as major controllers of brain blood flow. Normally, they dilate capillaries in response to neuronal activity, increasing local blood flow and energy supply. But in pathology they have a more sinister role. After artery block causes a stroke, the brain suffers from the so-called “no-reflow” phenomenon - a failure to fully reperfuse capillaries, even after the upstream occluded artery has been reperfused successfully. The resulting long-lasting decrease of energy supply damages neurons. I have shown that a major cause of no-reflow lies in pericytes: during ischaemia they constrict and then die in rigor. This reduces capillary diameter and blood flow, and probably degrades blood-brain barrier function. However, despite their crucial role in regulating blood flow physiologically and in pathology, little is known about the mechanisms by which pericytes function. By using blood vessel imaging, patch-clamping, two-photon imaging, optogenetics, immunohistochemistry, mathematical modelling, and live human tissue obtained from neurosurgery, this programme of research will: (i) define the signalling mechanisms controlling capillary constriction and dilation in health and disease; (ii) identify the relative contributions of neurons, astrocytes and microglia to regulating pericyte tone; (iii) develop approaches to preventing brain pericyte constriction and death during ischaemia; (iv) define how pericyte constriction of capillaries and pericyte death contribute to Alzheimer’s disease; (v) extend these results from rodent brain to human brain pericytes as a prelude to developing therapies. The diseases to which pericytes contribute include stroke, spinal cord injury, diabetes and Alzheimer’s disease. These all have an enormous economic impact, as well as causing great suffering for patients and their carers. This work will provide novel therapeutic approaches for treating these diseases.

2 499 954 €

Start date: 2017-09-01, End date: 2022-08-31

The aim of this project is to develop the next generation of compressive and computational sensing and processing techniques. The ability to identify and exploit good signal representations is pivotal in many signal and data processing tasks. During the last decade sparse representations have provided stunning performance gains for applications such as: imaging coding, computer vision, super-resolution microscopy and most recently in MRI, achieving many-fold acceleration through compressed sensing (CS). However in most real world sensing it is generally not possible to fully adopt the random sampling strategies advocated by CS. Systems are often nonlinear, measurements have limited dynamic range, noise is rarely Gaussian and reconstruction is not always the final goal. Furthermore, iterative reconstruction techniques are often not adopted in commercial imaging systems as they typically incur at least an order of magnitude more computation than traditional techniques. Thus there is a real need for a new framework for generalized computationally accelerated sensing and processing techniques. The research proposed here will build on the PIs recent work in this area and will develop and analyse a much richer class of hierarchical low dimensional signal models, accommodating everything from physical laws to data-driven models such as deep neural networks. It will provide quantitative guidance for system design and address sensing tasks beyond reconstruction including detection, classification and statistical estimation. It will also exploit low dimensional structure to reduce computational cost as well as estimation accuracy, challenging the notion that exploiting prior information must come at a computational cost. This research will result in a new generation of data-driven, physics-aware and task-orientated sensing systems in application domains such as advanced radar, CT and MR imaging and emerging sensing modalities such as multispectral time-of-flight cameras.

2 212 048 €

Start date: 2016-09-01, End date: 2021-08-31