Project acronym 3DWATERWAVES
Project Mathematical aspects of three-dimensional water waves with vorticity
Researcher (PI) Erik Torsten Wahlén
Host Institution (HI) LUNDS UNIVERSITET
Call Details Starting Grant (StG), PE1, ERC-2015-STG
Summary The goal of this project is to develop a mathematical theory for steady three-dimensional water waves with vorticity. The mathematical model consists of the incompressible Euler equations with a free surface, and vorticity is important for modelling the interaction of surface waves with non-uniform currents. In the two-dimensional case, there has been a lot of progress on water waves with vorticity in the last decade. This progress has mainly been based on the stream function formulation, in which the problem is reformulated as a nonlinear elliptic free boundary problem. An analogue of this formulation is not available in three dimensions, and the theory has therefore so far been restricted to irrotational flow. In this project we seek to go beyond this restriction using two different approaches. In the first approach we will adapt methods which have been used to construct three-dimensional ideal flows with vorticity in domains with a fixed boundary to the free boundary context (for example Beltrami flows). In the second approach we will develop methods which are new even in the case of a fixed boundary, by performing a detailed study of the structure of the equations close to a given shear flow using ideas from infinite-dimensional bifurcation theory. This involves handling infinitely many resonances.
Summary
The goal of this project is to develop a mathematical theory for steady three-dimensional water waves with vorticity. The mathematical model consists of the incompressible Euler equations with a free surface, and vorticity is important for modelling the interaction of surface waves with non-uniform currents. In the two-dimensional case, there has been a lot of progress on water waves with vorticity in the last decade. This progress has mainly been based on the stream function formulation, in which the problem is reformulated as a nonlinear elliptic free boundary problem. An analogue of this formulation is not available in three dimensions, and the theory has therefore so far been restricted to irrotational flow. In this project we seek to go beyond this restriction using two different approaches. In the first approach we will adapt methods which have been used to construct three-dimensional ideal flows with vorticity in domains with a fixed boundary to the free boundary context (for example Beltrami flows). In the second approach we will develop methods which are new even in the case of a fixed boundary, by performing a detailed study of the structure of the equations close to a given shear flow using ideas from infinite-dimensional bifurcation theory. This involves handling infinitely many resonances.
Max ERC Funding
1 203 627 €
Duration
Start date: 2016-03-01, End date: 2021-02-28
Project acronym A-DATADRIVE-B
Project Advanced Data-Driven Black-box modelling
Researcher (PI) Johan Adelia K Suykens
Host Institution (HI) KATHOLIEKE UNIVERSITEIT LEUVEN
Call Details Advanced Grant (AdG), PE7, ERC-2011-ADG_20110209
Summary Making accurate predictions is a crucial factor in many systems (such as in modelling energy consumption, power load forecasting, traffic networks, process industry, environmental modelling, biomedicine, brain-machine interfaces) for cost savings, efficiency, health, safety and organizational purposes. In this proposal we aim at realizing a new generation of more advanced black-box modelling techniques for estimating predictive models from measured data. We will study different optimization modelling frameworks in order to obtain improved black-box modelling approaches. This will be done by specifying models through constrained optimization problems by studying different candidate core models (parametric models, support vector machines and kernel methods) together with additional sets of constraints and regularization mechanisms. Different candidate mathematical frameworks will be considered with models that possess primal and (Lagrange) dual model representations, functional analysis in reproducing kernel Hilbert spaces, operator splitting and optimization in Banach spaces. Several aspects that are relevant to black-box models will be studied including incorporation of prior knowledge, structured dynamical systems, tensorial data representations, interpretability and sparsity, and general purpose optimization algorithms. The methods should be suitable for handling larger data sets and high dimensional input spaces. The final goal is also to realize a next generation software tool (including symbolic generation of models and handling different supervised and unsupervised learning tasks, static and dynamic systems) that can be generically applied to data from different application areas. The proposal A-DATADRIVE-B aims at getting end-users connected to the more advanced methods through a user-friendly data-driven black-box modelling tool. The methods and tool will be tested in connection to several real-life applications.
Summary
Making accurate predictions is a crucial factor in many systems (such as in modelling energy consumption, power load forecasting, traffic networks, process industry, environmental modelling, biomedicine, brain-machine interfaces) for cost savings, efficiency, health, safety and organizational purposes. In this proposal we aim at realizing a new generation of more advanced black-box modelling techniques for estimating predictive models from measured data. We will study different optimization modelling frameworks in order to obtain improved black-box modelling approaches. This will be done by specifying models through constrained optimization problems by studying different candidate core models (parametric models, support vector machines and kernel methods) together with additional sets of constraints and regularization mechanisms. Different candidate mathematical frameworks will be considered with models that possess primal and (Lagrange) dual model representations, functional analysis in reproducing kernel Hilbert spaces, operator splitting and optimization in Banach spaces. Several aspects that are relevant to black-box models will be studied including incorporation of prior knowledge, structured dynamical systems, tensorial data representations, interpretability and sparsity, and general purpose optimization algorithms. The methods should be suitable for handling larger data sets and high dimensional input spaces. The final goal is also to realize a next generation software tool (including symbolic generation of models and handling different supervised and unsupervised learning tasks, static and dynamic systems) that can be generically applied to data from different application areas. The proposal A-DATADRIVE-B aims at getting end-users connected to the more advanced methods through a user-friendly data-driven black-box modelling tool. The methods and tool will be tested in connection to several real-life applications.
Max ERC Funding
2 485 800 €
Duration
Start date: 2012-04-01, End date: 2017-03-31
Project acronym ABACUS
Project Advancing Behavioral and Cognitive Understanding of Speech
Researcher (PI) Bart De Boer
Host Institution (HI) VRIJE UNIVERSITEIT BRUSSEL
Call Details Starting Grant (StG), SH4, ERC-2011-StG_20101124
Summary I intend to investigate what cognitive mechanisms give us combinatorial speech. Combinatorial speech is the ability to make new words using pre-existing speech sounds. Humans are the only apes that can do this, yet we do not know how our brains do it, nor how exactly we differ from other apes. Using new experimental techniques to study human behavior and new computational techniques to model human cognition, I will find out how we deal with combinatorial speech.
The experimental part will study individual and cultural learning. Experimental cultural learning is a new technique that simulates cultural evolution in the laboratory. Two types of cultural learning will be used: iterated learning, which simulates language transfer across generations, and social coordination, which simulates emergence of norms in a language community. Using the two types of cultural learning together with individual learning experiments will help to zero in, from three angles, on how humans deal with combinatorial speech. In addition it will make a methodological contribution by comparing the strengths and weaknesses of the three methods.
The computer modeling part will formalize hypotheses about how our brains deal with combinatorial speech. Two models will be built: a high-level model that will establish the basic algorithms with which combinatorial speech is learned and reproduced, and a neural model that will establish in more detail how the algorithms are implemented in the brain. In addition, the models, through increasing understanding of how humans deal with speech, will help bridge the performance gap between human and computer speech recognition.
The project will advance science in four ways: it will provide insight into how our unique ability for using combinatorial speech works, it will tell us how this is implemented in the brain, it will extend the novel methodology of experimental cultural learning and it will create new computer models for dealing with human speech.
Summary
I intend to investigate what cognitive mechanisms give us combinatorial speech. Combinatorial speech is the ability to make new words using pre-existing speech sounds. Humans are the only apes that can do this, yet we do not know how our brains do it, nor how exactly we differ from other apes. Using new experimental techniques to study human behavior and new computational techniques to model human cognition, I will find out how we deal with combinatorial speech.
The experimental part will study individual and cultural learning. Experimental cultural learning is a new technique that simulates cultural evolution in the laboratory. Two types of cultural learning will be used: iterated learning, which simulates language transfer across generations, and social coordination, which simulates emergence of norms in a language community. Using the two types of cultural learning together with individual learning experiments will help to zero in, from three angles, on how humans deal with combinatorial speech. In addition it will make a methodological contribution by comparing the strengths and weaknesses of the three methods.
The computer modeling part will formalize hypotheses about how our brains deal with combinatorial speech. Two models will be built: a high-level model that will establish the basic algorithms with which combinatorial speech is learned and reproduced, and a neural model that will establish in more detail how the algorithms are implemented in the brain. In addition, the models, through increasing understanding of how humans deal with speech, will help bridge the performance gap between human and computer speech recognition.
The project will advance science in four ways: it will provide insight into how our unique ability for using combinatorial speech works, it will tell us how this is implemented in the brain, it will extend the novel methodology of experimental cultural learning and it will create new computer models for dealing with human speech.
Max ERC Funding
1 276 620 €
Duration
Start date: 2012-02-01, End date: 2017-01-31
Project acronym ABATSYNAPSE
Project Evolution of Alzheimer’s Disease: From dynamics of single synapses to memory loss
Researcher (PI) Inna Slutsky
Host Institution (HI) TEL AVIV UNIVERSITY
Call Details Starting Grant (StG), LS5, ERC-2011-StG_20101109
Summary A persistent challenge in unravelling mechanisms that regulate memory function is how to bridge the gap between inter-molecular dynamics of single proteins, activity of individual synapses and emerging properties of neuronal circuits. The prototype condition of disintegrating neuronal circuits is Alzheimer’s Disease (AD). Since the early time of Alois Alzheimer at the turn of the 20th century, scientists have been searching for a molecular entity that is in the roots of the cognitive deficits. Although diverse lines of evidence suggest that the amyloid-beta peptide (Abeta) plays a central role in synaptic dysfunctions of AD, several key questions remain unresolved. First, endogenous Abeta peptides are secreted by neurons throughout life, but their physiological functions are largely unknown. Second, experience-dependent physiological mechanisms that initiate the changes in Abeta composition in sporadic, the most frequent form of AD, are unidentified. And finally, molecular mechanisms that trigger Abeta-induced synaptic failure and memory decline remain elusive.
To target these questions, I propose to develop an integrative approach to correlate structure and function at the level of single synapses in hippocampal circuits. State-of-the-art techniques will enable the simultaneous real-time visualization of inter-molecular dynamics within signalling complexes and functional synaptic modifications. Utilizing FRET spectroscopy, high-resolution optical imaging, electrophysiology, molecular biology and biochemistry we will determine the casual relationship between ongoing neuronal activity, temporo-spatial dynamics and molecular composition of Abeta, structural rearrangements within the Abeta signalling complexes and plasticity of single synapses and whole networks. The proposed research will elucidate fundamental principles of neuronal circuits function and identify critical steps that initiate primary synaptic dysfunctions at the very early stages of sporadic AD.
Summary
A persistent challenge in unravelling mechanisms that regulate memory function is how to bridge the gap between inter-molecular dynamics of single proteins, activity of individual synapses and emerging properties of neuronal circuits. The prototype condition of disintegrating neuronal circuits is Alzheimer’s Disease (AD). Since the early time of Alois Alzheimer at the turn of the 20th century, scientists have been searching for a molecular entity that is in the roots of the cognitive deficits. Although diverse lines of evidence suggest that the amyloid-beta peptide (Abeta) plays a central role in synaptic dysfunctions of AD, several key questions remain unresolved. First, endogenous Abeta peptides are secreted by neurons throughout life, but their physiological functions are largely unknown. Second, experience-dependent physiological mechanisms that initiate the changes in Abeta composition in sporadic, the most frequent form of AD, are unidentified. And finally, molecular mechanisms that trigger Abeta-induced synaptic failure and memory decline remain elusive.
To target these questions, I propose to develop an integrative approach to correlate structure and function at the level of single synapses in hippocampal circuits. State-of-the-art techniques will enable the simultaneous real-time visualization of inter-molecular dynamics within signalling complexes and functional synaptic modifications. Utilizing FRET spectroscopy, high-resolution optical imaging, electrophysiology, molecular biology and biochemistry we will determine the casual relationship between ongoing neuronal activity, temporo-spatial dynamics and molecular composition of Abeta, structural rearrangements within the Abeta signalling complexes and plasticity of single synapses and whole networks. The proposed research will elucidate fundamental principles of neuronal circuits function and identify critical steps that initiate primary synaptic dysfunctions at the very early stages of sporadic AD.
Max ERC Funding
2 000 000 €
Duration
Start date: 2011-12-01, End date: 2017-09-30
Project acronym AcetyLys
Project Unravelling the role of lysine acetylation in the regulation of glycolysis in cancer cells through the development of synthetic biology-based tools
Researcher (PI) Eyal Arbely
Host Institution (HI) BEN-GURION UNIVERSITY OF THE NEGEV
Call Details Starting Grant (StG), LS9, ERC-2015-STG
Summary Synthetic biology is an emerging discipline that offers powerful tools to control and manipulate fundamental processes in living matter. We propose to develop and apply such tools to modify the genetic code of cultured mammalian cells and bacteria with the aim to study the role of lysine acetylation in the regulation of metabolism and in cancer development. Thousands of lysine acetylation sites were recently discovered on non-histone proteins, suggesting that acetylation is a widespread and evolutionarily conserved post translational modification, similar in scope to phosphorylation and ubiquitination. Specifically, it has been found that most of the enzymes of metabolic processes—including glycolysis—are acetylated, implying that acetylation is key regulator of cellular metabolism in general and in glycolysis in particular. The regulation of metabolic pathways is of particular importance to cancer research, as misregulation of metabolic pathways, especially upregulation of glycolysis, is common to most transformed cells and is now considered a new hallmark of cancer. These data raise an immediate question: what is the role of acetylation in the regulation of glycolysis and in the metabolic reprogramming of cancer cells? While current methods rely on mutational analyses, we will genetically encode the incorporation of acetylated lysine and directly measure the functional role of each acetylation site in cancerous and non-cancerous cell lines. Using this methodology, we will study the structural and functional implications of all the acetylation sites in glycolytic enzymes. We will also decipher the mechanism by which acetylation is regulated by deacetylases and answer a long standing question – how 18 deacetylases recognise their substrates among thousands of acetylated proteins? The developed methodologies can be applied to a wide range of protein families known to be acetylated, thereby making this study relevant to diverse research fields.
Summary
Synthetic biology is an emerging discipline that offers powerful tools to control and manipulate fundamental processes in living matter. We propose to develop and apply such tools to modify the genetic code of cultured mammalian cells and bacteria with the aim to study the role of lysine acetylation in the regulation of metabolism and in cancer development. Thousands of lysine acetylation sites were recently discovered on non-histone proteins, suggesting that acetylation is a widespread and evolutionarily conserved post translational modification, similar in scope to phosphorylation and ubiquitination. Specifically, it has been found that most of the enzymes of metabolic processes—including glycolysis—are acetylated, implying that acetylation is key regulator of cellular metabolism in general and in glycolysis in particular. The regulation of metabolic pathways is of particular importance to cancer research, as misregulation of metabolic pathways, especially upregulation of glycolysis, is common to most transformed cells and is now considered a new hallmark of cancer. These data raise an immediate question: what is the role of acetylation in the regulation of glycolysis and in the metabolic reprogramming of cancer cells? While current methods rely on mutational analyses, we will genetically encode the incorporation of acetylated lysine and directly measure the functional role of each acetylation site in cancerous and non-cancerous cell lines. Using this methodology, we will study the structural and functional implications of all the acetylation sites in glycolytic enzymes. We will also decipher the mechanism by which acetylation is regulated by deacetylases and answer a long standing question – how 18 deacetylases recognise their substrates among thousands of acetylated proteins? The developed methodologies can be applied to a wide range of protein families known to be acetylated, thereby making this study relevant to diverse research fields.
Max ERC Funding
1 499 375 €
Duration
Start date: 2016-07-01, End date: 2021-06-30
Project acronym AcTafactors
Project AcTafactors: Tumor Necrosis Factor-based immuno-cytokines with superior therapeutic indexes
Researcher (PI) Jan Honoré L Tavernier
Host Institution (HI) VIB
Call Details Proof of Concept (PoC), ERC-2015-PoC, ERC-2015-PoC
Summary Tumor Necrosis Factor (TNF) is a homotrimeric pro-inflammatory cytokine that was originally discovered based on its extraordinary antitumor activity. However, its shock-inducing properties, causing hypotension, leukopenia and multiple organ failure, prevented its systemic use in cancer treatment. With this proof-of-concept study we want to evaluate a novel class of cell-targeted TNFs with strongly reduced systemic toxicities (AcTafactors). In these engineered immuno-cytokines, single-chain TNFs that harbor mutations to reduce the affinity for its receptor(s) are fused to a cell- specific targeting domain. Whilst almost no biological activity is observed on non-targeted cells, thus preventing systemic toxicity, avidity effects at the targeted cell membrane lead to recovery of over 90% of the TNF signaling activity. In this project we propose a lead optimization program to further improve the lead AcTafactors identified in the context of the ERC Advanced Grant project and to evaluate the resulting molecules for their ability to target the tumor (neo)vasculature in clinically relevant murine tumor models. The pre-clinical proof-of-concept we aim for represents a first step towards clinical development and ultimately potential market approval of an effective AcTafactor anti-cancer therapy.
Summary
Tumor Necrosis Factor (TNF) is a homotrimeric pro-inflammatory cytokine that was originally discovered based on its extraordinary antitumor activity. However, its shock-inducing properties, causing hypotension, leukopenia and multiple organ failure, prevented its systemic use in cancer treatment. With this proof-of-concept study we want to evaluate a novel class of cell-targeted TNFs with strongly reduced systemic toxicities (AcTafactors). In these engineered immuno-cytokines, single-chain TNFs that harbor mutations to reduce the affinity for its receptor(s) are fused to a cell- specific targeting domain. Whilst almost no biological activity is observed on non-targeted cells, thus preventing systemic toxicity, avidity effects at the targeted cell membrane lead to recovery of over 90% of the TNF signaling activity. In this project we propose a lead optimization program to further improve the lead AcTafactors identified in the context of the ERC Advanced Grant project and to evaluate the resulting molecules for their ability to target the tumor (neo)vasculature in clinically relevant murine tumor models. The pre-clinical proof-of-concept we aim for represents a first step towards clinical development and ultimately potential market approval of an effective AcTafactor anti-cancer therapy.
Max ERC Funding
149 320 €
Duration
Start date: 2015-11-01, End date: 2017-04-30
Project acronym AD-VIP
Project Alzheimer’s disease and AAV9: Use of a virus-based delivery system for vectored immunoprophylaxis in dementia.
Researcher (PI) MATTHEW GUY HOLT
Host Institution (HI) VIB
Call Details Proof of Concept (PoC), PC1, ERC-2015-PoC
Summary Alzheimer’s disease (AD) is the most common form of dementia in the Western World, representing an economic and social cost of billions of euros a year. Given the changing demographics of society, these costs will only increase over the coming decades.
Amyloid plaques, composed of amyloid beta peptide (Abeta), are a defining characteristic of AD. Evidence now suggests that Abeta is central to disease pathogenesis due to its toxicity, which leads to cell loss and eventual cognitive decline. Abeta is generated by proteolytic cleavage of amyloid precursor protein, a process that involves the protein BACE1.
Knock-down of BACE1 is sufficient to prevent amyloid pathology and cognitive deficits in transgenic mouse models of AD, so BACE1 is an attractive target for therapeutic intervention. Although many small molecule inhibitors of BACE1 have been developed, many have problems with imperfect selectivity, posing a substantial risk for off-target toxicity in vivo. In contrast, antibody-based therapeutics provide an attractive alternative given their excellent molecular selectivity. However, the success of antibody therapies in AD is limited by the blood brain barrier, which limits antibody entry into the brain from the systemic circulation.
Recent studies have shown that adeno-associated virus serotype 9 (AAV9) effectively crosses the blood brain barrier. Here, we propose evaluating the use of AAV9 as a delivery system for a highly specific and potent inhibitory nanobody targeted against BACE1 as a treatment for AD.
Summary
Alzheimer’s disease (AD) is the most common form of dementia in the Western World, representing an economic and social cost of billions of euros a year. Given the changing demographics of society, these costs will only increase over the coming decades.
Amyloid plaques, composed of amyloid beta peptide (Abeta), are a defining characteristic of AD. Evidence now suggests that Abeta is central to disease pathogenesis due to its toxicity, which leads to cell loss and eventual cognitive decline. Abeta is generated by proteolytic cleavage of amyloid precursor protein, a process that involves the protein BACE1.
Knock-down of BACE1 is sufficient to prevent amyloid pathology and cognitive deficits in transgenic mouse models of AD, so BACE1 is an attractive target for therapeutic intervention. Although many small molecule inhibitors of BACE1 have been developed, many have problems with imperfect selectivity, posing a substantial risk for off-target toxicity in vivo. In contrast, antibody-based therapeutics provide an attractive alternative given their excellent molecular selectivity. However, the success of antibody therapies in AD is limited by the blood brain barrier, which limits antibody entry into the brain from the systemic circulation.
Recent studies have shown that adeno-associated virus serotype 9 (AAV9) effectively crosses the blood brain barrier. Here, we propose evaluating the use of AAV9 as a delivery system for a highly specific and potent inhibitory nanobody targeted against BACE1 as a treatment for AD.
Max ERC Funding
150 000 €
Duration
Start date: 2016-12-01, End date: 2018-05-31
Project acronym ARISE
Project The Ecology of Antibiotic Resistance
Researcher (PI) Roy Kishony
Host Institution (HI) TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY
Call Details Starting Grant (StG), LS8, ERC-2011-StG_20101109
Summary Main goal. We aim to understand the puzzling coexistence of antibiotic-resistant and antibiotic-sensitive species in natural soil environments, using novel quantitative experimental techniques and mathematical analysis. The ecological insights gained will be translated into novel treatment strategies for combating antibiotic resistance.
Background. Microbial soil ecosystems comprise communities of species interacting through copious secretion of antibiotics and other chemicals. Defence mechanisms, i.e. resistance to antibiotics, are ubiquitous in these wild communities. However, in sharp contrast to clinical settings, resistance does not take over the population. Our hypothesis is that the ecological setting provides natural mechanisms that keep antibiotic resistance in check. We are motivated by our recent finding that specific antibiotic combinations can generate selection against resistance and that soil microbial strains produce compounds that directly target antibiotic resistant mechanisms.
Approaches. We will: (1) Isolate natural bacterial species from individual grains of soil, characterize their ability to produce and resist antibiotics and identify the spatial scale for correlations between resistance and production. (2) Systematically measure interactions between species and identify interaction patterns enriched in co-existing communities derived from the same grain of soil. (3) Introducing fluorescently-labelled resistant and sensitive strains into natural soil, we will measure the fitness cost and benefit of antibiotic resistance in situ and identify natural compounds that select against resistance. (4) Test whether such “selection-inverting” compounds can slow evolution of resistance to antibiotics in continuous culture experiments.
Conclusions. These findings will provide insights into the ecological processes that keep antibiotic resistance in check, and will suggest novel antimicrobial treatment strategies.
Summary
Main goal. We aim to understand the puzzling coexistence of antibiotic-resistant and antibiotic-sensitive species in natural soil environments, using novel quantitative experimental techniques and mathematical analysis. The ecological insights gained will be translated into novel treatment strategies for combating antibiotic resistance.
Background. Microbial soil ecosystems comprise communities of species interacting through copious secretion of antibiotics and other chemicals. Defence mechanisms, i.e. resistance to antibiotics, are ubiquitous in these wild communities. However, in sharp contrast to clinical settings, resistance does not take over the population. Our hypothesis is that the ecological setting provides natural mechanisms that keep antibiotic resistance in check. We are motivated by our recent finding that specific antibiotic combinations can generate selection against resistance and that soil microbial strains produce compounds that directly target antibiotic resistant mechanisms.
Approaches. We will: (1) Isolate natural bacterial species from individual grains of soil, characterize their ability to produce and resist antibiotics and identify the spatial scale for correlations between resistance and production. (2) Systematically measure interactions between species and identify interaction patterns enriched in co-existing communities derived from the same grain of soil. (3) Introducing fluorescently-labelled resistant and sensitive strains into natural soil, we will measure the fitness cost and benefit of antibiotic resistance in situ and identify natural compounds that select against resistance. (4) Test whether such “selection-inverting” compounds can slow evolution of resistance to antibiotics in continuous culture experiments.
Conclusions. These findings will provide insights into the ecological processes that keep antibiotic resistance in check, and will suggest novel antimicrobial treatment strategies.
Max ERC Funding
1 900 000 €
Duration
Start date: 2012-09-01, End date: 2018-08-31
Project acronym ARRAY SEQ
Project Array-tagged single cell gene expression by parallel linear RNA amplification and sequencing
Researcher (PI) Itai Yanai
Host Institution (HI) TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY
Call Details Proof of Concept (PoC), ERC-2015-PoC, ERC-2015-PoC
Summary In many biomedical research and clinical applications it would be tremendously useful to know the gene expression profile of each and every cell in a sample, be it a blood sample or tumor. At present, the most advanced single-cell technologies are limited to a few thousand cells by a laborious and expensive approach. We have invented a method allowing the determination of the transcriptomes of millions of cells in parallel, using array-based technique for tagging single cells. The protocol combines our previously published protocol for single cell transcriptomics – CEL-Seq – with a new membrane based system for capturing single cells and a DNA microarray for differentially tagging each cell in the membrane. If further developed into a commercial platform, our method could have tremendous impact on clinical and research transcriptomics. Our method requires no expensive equipment, low amounts of reagents and little hands-on, making it unlike any available protocol for single cell analysis. Our method also has great versatility as it can be used for analyzing up to a million cells, but can also be easily scaled down to several hundreds, promising to make it the state of the art protocol for any lab interested in single cell biology. Our method thus represents a game-changer because it completely reinvents the scale under which cells can be examined – affordably and without a need for expensive instruments – by at least three orders of magnitude. The aim of this project is to establish a user-friendly platform for our method that could be commercially available in the coming years. The developed platform will facilitate a large-scale ability to query cells; the breadth of possible research and personal medicine applications is unimaginable at present.
Summary
In many biomedical research and clinical applications it would be tremendously useful to know the gene expression profile of each and every cell in a sample, be it a blood sample or tumor. At present, the most advanced single-cell technologies are limited to a few thousand cells by a laborious and expensive approach. We have invented a method allowing the determination of the transcriptomes of millions of cells in parallel, using array-based technique for tagging single cells. The protocol combines our previously published protocol for single cell transcriptomics – CEL-Seq – with a new membrane based system for capturing single cells and a DNA microarray for differentially tagging each cell in the membrane. If further developed into a commercial platform, our method could have tremendous impact on clinical and research transcriptomics. Our method requires no expensive equipment, low amounts of reagents and little hands-on, making it unlike any available protocol for single cell analysis. Our method also has great versatility as it can be used for analyzing up to a million cells, but can also be easily scaled down to several hundreds, promising to make it the state of the art protocol for any lab interested in single cell biology. Our method thus represents a game-changer because it completely reinvents the scale under which cells can be examined – affordably and without a need for expensive instruments – by at least three orders of magnitude. The aim of this project is to establish a user-friendly platform for our method that could be commercially available in the coming years. The developed platform will facilitate a large-scale ability to query cells; the breadth of possible research and personal medicine applications is unimaginable at present.
Max ERC Funding
150 000 €
Duration
Start date: 2015-09-01, End date: 2017-02-28
Project acronym AstroFunc
Project Molecular Studies of Astrocyte Function in Health and Disease
Researcher (PI) Matthew Guy Holt
Host Institution (HI) VIB
Call Details Starting Grant (StG), LS5, ERC-2011-StG_20101109
Summary Brain consists of two basic cell types – neurons and glia. However, the study of glia in brain function has traditionally been neglected in favor of their more “illustrious” counter-parts – neurons that are classed as the computational units of the brain. Glia have usually been classed as “brain glue” - a supportive matrix on which neurons grow and function. However, recent evidence suggests that glia are more than passive “glue” and actually modulate neuronal function. This has lead to the proposal of a “tripartite synapse”, which recognizes pre- and postsynaptic neuronal elements and glia as a unit.
However, what is still lacking is rudimentary information on how these cells actually function in situ. Here we propose taking a “bottom-up” approach, by identifying the molecules (and interactions) that control glial function in situ. This is complicated by the fact that glia show profound changes when placed into culture. To circumvent this, we will use recently developed cell sorting techniques, to rapidly isolate genetically marked glial cells from brain – which can then be analyzed using advanced biochemical and physiological techniques. The long-term aim is to identify proteins that can be “tagged” using transgenic technologies to allow protein function to be studied in real-time in vivo, using sophisticated imaging techniques. Given the number of proteins that may be identified we envisage developing new methods of generating transgenic animals that provide an attractive alternative to current “state-of-the art” technology.
The importance of studying glial function is given by the fact that every major brain pathology shows reactive gliosis. In the time it takes to read this abstract, 5 people in the EU will have suffered a stroke – not to mention those who suffer other forms of neurotrauma. Thus, understanding glial function is not only critical to understanding normal brain function, but also for relieving the burden of severe neurological injury and disease
Summary
Brain consists of two basic cell types – neurons and glia. However, the study of glia in brain function has traditionally been neglected in favor of their more “illustrious” counter-parts – neurons that are classed as the computational units of the brain. Glia have usually been classed as “brain glue” - a supportive matrix on which neurons grow and function. However, recent evidence suggests that glia are more than passive “glue” and actually modulate neuronal function. This has lead to the proposal of a “tripartite synapse”, which recognizes pre- and postsynaptic neuronal elements and glia as a unit.
However, what is still lacking is rudimentary information on how these cells actually function in situ. Here we propose taking a “bottom-up” approach, by identifying the molecules (and interactions) that control glial function in situ. This is complicated by the fact that glia show profound changes when placed into culture. To circumvent this, we will use recently developed cell sorting techniques, to rapidly isolate genetically marked glial cells from brain – which can then be analyzed using advanced biochemical and physiological techniques. The long-term aim is to identify proteins that can be “tagged” using transgenic technologies to allow protein function to be studied in real-time in vivo, using sophisticated imaging techniques. Given the number of proteins that may be identified we envisage developing new methods of generating transgenic animals that provide an attractive alternative to current “state-of-the art” technology.
The importance of studying glial function is given by the fact that every major brain pathology shows reactive gliosis. In the time it takes to read this abstract, 5 people in the EU will have suffered a stroke – not to mention those who suffer other forms of neurotrauma. Thus, understanding glial function is not only critical to understanding normal brain function, but also for relieving the burden of severe neurological injury and disease
Max ERC Funding
1 490 168 €
Duration
Start date: 2012-01-01, End date: 2016-12-31
