Ian Gilmore (National Physical Laboratory): Metabolic imaging at the single-cell scale using mass spectrometry imaging
There is an increasing appreciation that every cell, even of the same type, is different. This complexity, when additionally combined with the variety of different cell types in tissue, is driving the need for spatially resolved omics at the single-cell scale. Rapid advances are being made in genomics and transcriptomics but progress in metabolomics lags. This is partly because amplification and tagging strategies are not suited to dynamically created metabolite molecules. Mass spectrometry imaging has excellent potential for metabolic imaging. This talk will highlight recent advances in secondary ion mass spectrometry (SIMS), notably the 3D OrbiSIMS, and place this in context with matrix assisted laser desorption ionisation (MALDI) mass spectrometry and their convergence in sub-cellular spatial resolution and molecular information.
Roisin Owens (University of Cambridge): A bioelectronic in vitro model of the gut-brain axis
In vitro models of biological systems are essential for our understanding of biological systems. In many cases where animal models have failed to translate to useful data for human diseases, physiologically relevant in vitro models can bridge the gap. Many difficulties exist in interfacing complex, 3D models with sensing technology adapted for monitoring function of cells within these models. Polymeric electroactive materials and devices can bridge the gap between hard inflexible materials used for physical transducers and soft, compliant biological tissues. An additional advantage of these electronic materials is their flexibility for processing and fabrication in a wide range of formats. In this presentation, I will discuss our recent progress in developing bioelectronics devices to integrate with 3D cell models of the gut-brain axis, a key player in understanding diverse pathologies, including dementia.
Martyn Boutelle (Imperial College London): Measuring dynamic neurochemical changes in the injured brain using microfluidic biosensors
Patients who have suffered severe acute brain injury can often evolve secondary brain injury during the time they are under intensive care. We have develop a real-time multimodal monitoring approach to detect the secondary insults to the injured brain (such as transient ischeamia and cortical spreading depolarisations) and measure the dynamic effect of these insults on brain neurochemistry. The brain intracellular fluid is sampled using a microdialysis probe. The dialysate stream produced is then analysed in real-time using 3D printed microfluidic manifolds containing replicable electrochemical sensors and biosensors. Wireless electronics allow the devices to placed close to the patient for high temporal responsiveness. We monitor energy metabolites (glucose, lactate and pyruvate), neurotransmitters (glutamate) and ionic changes to detect the pattern of changes associated with changes in tissue pathophysiology .
Patrick Dupont (Katholieke Universiteit Leuven): Is graph theoretical analysis of brain imaging data a useful tool to study neurodegeneration?
A graph theoretical analysis of a network is a well-established mathematical technique for quantification of this network. It is used in different fields and the last decade it became a popular technique to study imaging based large-scale brain networks. I will briefly review the literature on neurodegenerative diseases in which a graph theoretical approach was used to study human brain networks defined using resting-state fMRI, structural MRI, diffusion MRI, EEG, MEG, or PET based networks using different tracers. While reviewing these studies I will highlight some issues when using a graph theoretical approach and I will discuss what needs to be further investigated to fully understand the results obtained using this technique as well as the requirements before we can use it as a biomarker in a clinical setting.
