Project acronym DIRECT-fMRI
Project Sensing activity-induced cell swellings and ensuing neurotransmitter releases for in-vivo functional imaging sans hemodynamics
Researcher (PI) Noam Shemesh
Host Institution (HI) FUNDACAO D. ANNA SOMMER CHAMPALIMAUD E DR. CARLOS MONTEZ CHAMPALIMAUD
Call Details Starting Grant (StG), PE4, ERC-2015-STG
Summary Functional-Magnetic Resonance Imaging (fMRI) has transformed our understanding of brain function due to its ability to noninvasively tag ‘active’ brain regions. Nevertheless, fMRI only detects neural activity indirectly, by relying on slow hemodynamic couplings whose relationships with underlying neural activity are not fully known.
We have recently pioneered two unique MR approaches: Non-Uniform Oscillating-Gradient Spin-Echo (NOGSE) MRI and Relaxation Enhanced MR Spectroscopy (RE MRS). NOGSE-MRI is an exquisite microstructural probe, sensing cell sizes (l) with an unprecedented l^6 sensitivity (compared to l^2 in conventional approaches); RE MRS is a new spectral technique capable of recording metabolic signals with extraordinary fidelity at ultrahigh fields.
This proposal aims to harness these novel concepts for mapping neural activity directly, without relying on hemodynamics. The specific objectives of this proposal are:
(1) Mapping neural activity via sensing cell swellings upon activity (μfMRI): we hypothesize that NOGSE can robustly sense subtle changes in cellular microstructure upon neural firings and hence convey neural activity directly.
(2) Probing the nature of elicited activity via detection of neurotransmitter release: we posit that RE MRS is sufficiently sensitive to robustly detect changes in Glutamate and GABA signals upon activation.
(3) Network mapping in optogenetically-stimulated, behaving mice: we propose to couple our novel approaches with optogenetics to resolve neural correlates of behavior in awake, behaving mice.
Simulations for μfMRI predict >4% signal changes upon subtle cell swellings; further, our in vivo RE MRS experiments have detected metabolites with SNR>50 in only 6 seconds. Hence, these two complementary –and importantly, hemodynamics-independent– approaches will represent a true paradigm shift: from indirect detection of neurovasculature couplings towards direct and noninvasive mapping of neural activity in vivo.
Summary
Functional-Magnetic Resonance Imaging (fMRI) has transformed our understanding of brain function due to its ability to noninvasively tag ‘active’ brain regions. Nevertheless, fMRI only detects neural activity indirectly, by relying on slow hemodynamic couplings whose relationships with underlying neural activity are not fully known.
We have recently pioneered two unique MR approaches: Non-Uniform Oscillating-Gradient Spin-Echo (NOGSE) MRI and Relaxation Enhanced MR Spectroscopy (RE MRS). NOGSE-MRI is an exquisite microstructural probe, sensing cell sizes (l) with an unprecedented l^6 sensitivity (compared to l^2 in conventional approaches); RE MRS is a new spectral technique capable of recording metabolic signals with extraordinary fidelity at ultrahigh fields.
This proposal aims to harness these novel concepts for mapping neural activity directly, without relying on hemodynamics. The specific objectives of this proposal are:
(1) Mapping neural activity via sensing cell swellings upon activity (μfMRI): we hypothesize that NOGSE can robustly sense subtle changes in cellular microstructure upon neural firings and hence convey neural activity directly.
(2) Probing the nature of elicited activity via detection of neurotransmitter release: we posit that RE MRS is sufficiently sensitive to robustly detect changes in Glutamate and GABA signals upon activation.
(3) Network mapping in optogenetically-stimulated, behaving mice: we propose to couple our novel approaches with optogenetics to resolve neural correlates of behavior in awake, behaving mice.
Simulations for μfMRI predict >4% signal changes upon subtle cell swellings; further, our in vivo RE MRS experiments have detected metabolites with SNR>50 in only 6 seconds. Hence, these two complementary –and importantly, hemodynamics-independent– approaches will represent a true paradigm shift: from indirect detection of neurovasculature couplings towards direct and noninvasive mapping of neural activity in vivo.
Max ERC Funding
1 787 500 €
Duration
Start date: 2016-03-01, End date: 2021-02-28
Project acronym LIQUIDMASS
Project High throughput mass spectrometry of single proteins in liquid environment
Researcher (PI) Montserrat Calleja Gomez
Host Institution (HI) AGENCIA ESTATAL CONSEJO SUPERIOR DEINVESTIGACIONES CIENTIFICAS
Call Details Consolidator Grant (CoG), PE4, ERC-2015-CoG
Summary Although mass spectrometry has brought about major advancements in proteomics in the last decade, protein mass spectrometers still have important limitations. One fundamental limitation is that they require sample ionization, desorption into the gas phase and fragmentation, clearly leading to protein denaturation. Since relevant protein complexes are unstable or transient, their characterization in its native state and physiological environment remains an unexplored route towards the full understanding of protein function and protein interactions. This problem has only been targeted to date through theoretical approaches or low throughput experimental techniques, such as atomic force spectroscopy, optical tweezers or FRET. A high throughput characterization technology capable of addressing single proteins in its native state would have a large impact in proteomics. The goal of LIQUIDMASS is to develop a high throughput spectrometric technique addressing single proteins from complex samples while in physiological conditions. LIQUIDMASS also proposes a new concept for protein spectrometry, by characterizing not only the mass, but also the hydrodynamic radius, geometry and stiffness of single proteins. This multiparameter approach will serve to open up new routes to understand protein structure-function relations by providing insight into the fast conformational changes that occur in liquids. In order to attain these goals, I propose to integrate nanomechanical resonators, nano-optics and nanofluidics. The disruptive approach proposed will bring about new knowledge about protein interactions and protein conformation that is elusive today. The enabling technologies aimed at the LIQUIDMASS will increase our understanding of protein misfolding related diseases, such as Alzheimer’s or diabetes, as well as bring closer a full understanding of the human interactome, contributing to the advancement of the proteomics field.
Summary
Although mass spectrometry has brought about major advancements in proteomics in the last decade, protein mass spectrometers still have important limitations. One fundamental limitation is that they require sample ionization, desorption into the gas phase and fragmentation, clearly leading to protein denaturation. Since relevant protein complexes are unstable or transient, their characterization in its native state and physiological environment remains an unexplored route towards the full understanding of protein function and protein interactions. This problem has only been targeted to date through theoretical approaches or low throughput experimental techniques, such as atomic force spectroscopy, optical tweezers or FRET. A high throughput characterization technology capable of addressing single proteins in its native state would have a large impact in proteomics. The goal of LIQUIDMASS is to develop a high throughput spectrometric technique addressing single proteins from complex samples while in physiological conditions. LIQUIDMASS also proposes a new concept for protein spectrometry, by characterizing not only the mass, but also the hydrodynamic radius, geometry and stiffness of single proteins. This multiparameter approach will serve to open up new routes to understand protein structure-function relations by providing insight into the fast conformational changes that occur in liquids. In order to attain these goals, I propose to integrate nanomechanical resonators, nano-optics and nanofluidics. The disruptive approach proposed will bring about new knowledge about protein interactions and protein conformation that is elusive today. The enabling technologies aimed at the LIQUIDMASS will increase our understanding of protein misfolding related diseases, such as Alzheimer’s or diabetes, as well as bring closer a full understanding of the human interactome, contributing to the advancement of the proteomics field.
Max ERC Funding
2 470 283 €
Duration
Start date: 2016-11-01, End date: 2021-10-31
Project acronym NetMoDEzyme
Project Network models for the computational design of proficient enzymes
Researcher (PI) Silvia Osuna Oliveras
Host Institution (HI) UNIVERSITAT DE GIRONA
Call Details Starting Grant (StG), PE4, ERC-2015-STG
Summary Billions of years of evolution have made enzymes superb catalysts capable of accelerating reactions by several orders of magnitude. The underlying physical principles of their extraordinary catalytic power still remains highly debated, which makes the alteration of natural enzyme activities towards synthetically useful targets a tremendous challenge for modern chemical biology. The routine design of enzymes will, however, have large socio-economic benefits, as because of the enzymatic advantages the production costs of many drugs will be reduced and will allow industries to use environmentally friendly alternatives. The goal of this project is to make the routine design of proficient enzymes possible. Current computational and experimental approaches are able to confer natural enzymes new functionalities but are economically unviable and the catalytic efficiencies lag far behind their natural counterparts. The groundbreaking nature of NetMoDEzyme relies on the application of network models to reduce the complexity of the enzyme design paradigm and completely reformulate previous computational design approaches. The new protocol proposed accurately characterizes the enzyme conformational dynamics and customizes the included mutations by exploiting the correlated movement of the enzyme active site residues with distal regions. The guidelines for mutation are withdrawn from the costly directed evolution experimental technique, and the most proficient enzymes are easily identified via chemoinformatic models. The new strategy will be applied to develop proficient enzymes for the synthesis of enantiomerically pure β-blocker drugs for treating cardiovascular problems at a reduced cost. The experimental assays of our computational predictions will finally elucidate the potential of this genuinely new approach for mimicking Nature’s rules of evolution.
Summary
Billions of years of evolution have made enzymes superb catalysts capable of accelerating reactions by several orders of magnitude. The underlying physical principles of their extraordinary catalytic power still remains highly debated, which makes the alteration of natural enzyme activities towards synthetically useful targets a tremendous challenge for modern chemical biology. The routine design of enzymes will, however, have large socio-economic benefits, as because of the enzymatic advantages the production costs of many drugs will be reduced and will allow industries to use environmentally friendly alternatives. The goal of this project is to make the routine design of proficient enzymes possible. Current computational and experimental approaches are able to confer natural enzymes new functionalities but are economically unviable and the catalytic efficiencies lag far behind their natural counterparts. The groundbreaking nature of NetMoDEzyme relies on the application of network models to reduce the complexity of the enzyme design paradigm and completely reformulate previous computational design approaches. The new protocol proposed accurately characterizes the enzyme conformational dynamics and customizes the included mutations by exploiting the correlated movement of the enzyme active site residues with distal regions. The guidelines for mutation are withdrawn from the costly directed evolution experimental technique, and the most proficient enzymes are easily identified via chemoinformatic models. The new strategy will be applied to develop proficient enzymes for the synthesis of enantiomerically pure β-blocker drugs for treating cardiovascular problems at a reduced cost. The experimental assays of our computational predictions will finally elucidate the potential of this genuinely new approach for mimicking Nature’s rules of evolution.
Max ERC Funding
1 445 588 €
Duration
Start date: 2016-05-01, End date: 2021-04-30