The brain is a complex network of inter-connected neurons that communicate through synapses. SYNAPS aims to for the first time mimic such synapses using liposomes as artificial cells, and visible light to trigger a signal from a ‘sender’- to a ‘receiver’-liposome. Mimicking such communication processes will help with understanding how complex natural emergent properties arise, and could ultimately allow for the construction of a chemical computer. SYNAPS will excel beyond the state-of-the-art by maintaining chemical isolation between liposome interiors, ensuring local, time-bound communication between connected liposomes, and using light as an external stimulus and fuel. These concepts are essential to construct artificial tissues that can communicate on an individual liposome-to-liposome basis, in contrast to the state-of-the-art where communication generally occurs with the bulk solution. To achieve this, a messenger compound will be locally photosynthesised through transmembrane electron transfer by porphyrin dimers that portray a charge-transfer excited state. The liposomes will be organised into a synaptic cleft through the use of synthetic complementary clustering compounds that provide stable adhesion between sender and receiver liposomes. The messenger compound will be recognised by reversible and selective membrane-spanning receptors in the receiver liposome, that will output the signal through fluorescence. In addition, a reaction cascade network will be constructed involving the messenger to produce an artificial action potential, that is, a transient peak in the concentration of the messenger, ensuring a time-bound dissipative signal. Altogether, SYNAPS will provide advances in systems chemistry by providing a nanoscale platform for communication between chemically isolated systems, but also results that are useful for applications such as light-to-chemical energy conversion, chemical sensing and smart drug-delivery.

Aging involves a series of changes affecting both our cognitive abilities and our motor performance. As any other action, the control we exert over our eye movements changes due to aging. For example, the older we are, the less accurately we pursue moving targets. Even though vision -assisted by eye movements- is the main sensory source of information we use to make most of our decisions (and especially those related to actions), the effect that changes in the oculomotor behavior have on our perceptual and motor decisions remains unknown. The aim of DECEYEDE is to define how oculomotor control evolves with age, and how these changes accompany changes in perceptual and motor decisions. To do so, we will first assess how adults of different ages look at targets in dynamic scenarios about which they have to make a decision. Several parameters such as saccade latencies, number of saccades or pursuit gain will be evaluated, and a relation between such parameters and the performance in perceptual and motor decisions tasks will be established. Second, we will apply parameter based-feedback to explore if we can train eye movements in order to improve performance. We will do so by providing feedback to participants about specific parameters of their oculomotor performance. Such feedback will try to steer eye movements towards parameters similar to those that lead to better perceptual and motor performance. DECEYEDE will advance knowledge on the effects of aging on how we sample visual information from the world around us, and will allow further understanding of the mechanisms behind the decline in perceptual and motor tasks with aging. Furthermore, a successful method to train eye movements based on parameter feedback would represent a breakthrough that would set foundational principles for new ways of sensorimotor learning that will likely impact on the industry developing serious games or training and rehabilitation programs.

Neurodegenerative diseases affect more than 6 million people in Europe, and its prevalence is growing as population ages, hence it is a timeliness health challenge we are facing as a society. Parkinsonism is the 2nd most prevalent neurodegenerative form, being Parkinson’s disease (PD) the most frequent, whereas 20% of the patients are diagnosed with atypical parkinsonisms (AP). Despite presenting some clinical overlap, AP tends to be more therapy-resistant and have faster degeneration rates than PD. SYNPARK is an interdisciplinary project that will investigate the discriminative power of different imaging markers’ modalities in parkinsonisms. Improving diagnostic accuracy is crucial as disease-modifying therapies are becoming available for PD. For this challenge, I propose a multidisciplinary approach: from the in-vivo synaptic molecular brain assessment (using positron emission tomography, PET), the whole-brain connectomics organisation (using magnetic resonance imaging, MRI) to the clinics. I will conduct the outgoing phase in one of the world’s PET leading centres in Toronto to test the clinical validity of a new generation PET tracer in AP/PD. My host return institution (Barcelona) has pioneered the research on machine learning (ML) techniques that are revolutionising the medical sciences field to improve parkinsonisms’ differential diagnosis at the single-patient level by means of whole-brain MRI connectomics information. My current expertise and the proposed ambitious training objectives will position me at the forefront of this exciting new avenue in medical sciences and will enhance my professional independence for research leadership. Overall, the synergies established between these two leading centres are expected to have a tremendous impact on the understanding of AP/PD brain pathophysiology and its diagnostic accuracy, and ultimately enhancing a revolution in personalised medicine, a futuristic therapy that is increasingly becoming a reality.

Many pathogenic mechanisms are involved in Huntington’s disease (HD), the most prevalent monogenic neurological disease in Europe, with no current cure. The host team reported a dysregulation of tRNA-fragments (tRFs) in HD brains; other studies showed higher stress granules' (SG) density in HD, and that tRFs promote SG assembly. Thus, my hypothesis is that HD-related tRFs contribute to the accumulation of SG, initiating mutant huntingtin (mtHTT) aggregation and leading to disease pathogenesis. I propose to explore the interaction between mtHTT aggregates, and tRFs and SG, as new potential therapeutic targets. Specifically, I aim to study the 1) cell-type specificity of tRFs formation and their correlation with HD progression; 2) molecular mechanisms by which tRFs may induce HD pathology; and 3) potential of tRFs modulators to treat mtHTT-related neurotoxicity. This project’s success relies on the perfect synergy of the host team’s expertise in tRFs biology and HD and my own experience in RNA and proteotoxicity in neurodegenerative diseases, thoroughly addressing the hypothesis via complementary human, cellular and mice HD models and a plural array of bioinformatic, molecular biology and genomic approaches. The outcomes will help define the role of tRFs and SG in mtHTT aggregation, and may be used as preclinical proof-of-concept for novel HD therapies, reflecting the project’s translational potential. I will benefit from scientific exchanges with expert neuroscientists in the host institution, particularly my supervisor, whose skills in functional genomics fully match my expertise in RNA and protein pathological mechanisms. My translational technical knowledge and network of collaborators will be useful to the host group to build new cooperative projects. Thus, the MSCA fellowships will allow me to develop unique technical and transferable skills, helping me progress and establish myself as a leader in neurodegenerative research within a European research institute.