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KNAW

Royal Netherlands Academy of Arts and Sciences
Country: Netherlands
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274 Projects, page 1 of 55
  • Funder: EC Project Code: 742225
    Overall Budget: 2,500,000 EURFunder Contribution: 2,500,000 EUR

    From populations of unicellular organisms to complex tissues, cell-to-cell variability in phenotypic traits seems to be universal. To study this heterogeneity and its biological consequences, researchers have used advanced microscopy-based approaches that provide exquisite spatial and temporal resolution, but these methods are typically limited to measuring a few properties in parallel. On the other hand, next generation sequencing technologies allow for massively parallel genome-wide approaches but have, until recently, relied on studying population averages obtained from pooling thousands to millions of cells, precluding genome-wide analysis of cell-to-cell variability. Very excitingly, in the last few years there has been a revolution in single-cell sequencing technologies allowing genome-wide quantification of mRNA and genomic DNA in thousands of individual cells leading to the convergence of genomics and single-cell biology. However, during this convergence the spatial and temporal information, easily accessed by microscopy-based approaches, is often lost in a single-cell sequencing experiment. The overarching goal of this proposal is to develop single-cell sequencing technology that retains important aspects of the spatial-temporal information. In particular I will focus on integrating single-cell transcriptome and epigenome measurements with the physical cell-to-cell interaction network (spatial information) and lineage information (temporal information). These tools will be utilized to (i) explore the division symmetry of intestinal stem cells in vivo; (ii) to reconstruct the cell lineage history during zebrafish regeneration; and (iii) to determine lineage relations and the physical cell-to-cell interaction network of progenitor cells in the murine bone marrow.

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  • Funder: EC Project Code: 957641
    Funder Contribution: 150,000 EUR

    A recent surge in consumer products to record sleep and provide feedback on its duration show that a large market exists for sleep improvement. Ensuring high quality sleep is at least as important to promote health as exercise, restricting caloric intake and quitting smoking and alcohol. Consumers are keen to utilize tools that help them attain these aims. Commercially available devices recording movement and physiology provide consumers with an estimate of SLEEP DURATION. However, novel findings from our ERC-AdG project, point out that SLEEP QUALITY matters more for health and well-being than duration. About one-third of the world population report insomnia complaints. In 10%, symptom severity and chronicity justifies a diagnosis of insomnia disorder. Early detection and quantification of poor sleep quality is essential to detect a need for sleep behavioral changes and to evaluate personalized intervention effectiveness. However, NONE OF THE CURRENTLY AVAILABLE CONSUMER SLEEP SYSTEMS INCLUDES A VALIDATED METHOD TO ASSESS SLEEP QUALITY. Our ERC-AdG project revealed readily detectable sleep features with a tremendous impact on health and well-being. These features are highly promising sleep quality biomarkers with unprecedented commercial potential to disrupt and innovate the current market of consumer sleep devices and applications. Combined with sleep training programs, THIS WILL ENABLE END USERS TO IMPROVE BOTH SLEEP QUANTITY AND QUALITY. This PoC project provides automated versions of sleep quality assessment algorithms validated in the host ERC-AdG project. Feature extraction and algorithms are co-developed with companies active in sleep measurement, ensuring implementation of the end product in novel commercial systems. We envision this project to be a double-edged sword: a valuable addition to the toolbox of the clinician treating insomnia, and providing the general public with the ability to measure and improve sleep quality through consumer electronics.

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  • Funder: EC Project Code: 677936
    Overall Budget: 1,500,000 EURFunder Contribution: 1,500,000 EUR

    Gene expression is a highly dynamic and inherently variable process. Yet, it needs to be tightly regulated, especially during the cell cycle, when continuous large-scale changes occur to the proteome. Even small deviations in the expression levels of a single protein in individual cells can de-regulate cell cycle entry and promote tumorigenesis. Here, I will develop new technology to study gene expression dynamics in single cells to uncover how active regulation and stochastic variability shape the expression of key cell cycle genes and ensure reliable cell cycle-entry decisions. I recently developed a protein multimerization system, called SunTag, which allows very bright fluorescence imaging, as well as manipulation of transcription. To understand how accurate expression levels of a core set of cell cycle proteins are achieved, I will combine single-cell RNA sequencing with SunTag fluorescence imaging technology to visualize, with single molecule sensitivity, the rates of transcription, translation and mRNA degradation. These analyses will identify the contribution of each type of regulation to accurate gene expression, and will reveal how active regulation ensures correct cell cycle decisions in the presence of stochastic expression variability. Furthermore, I will develop new methodology to specifically perturb the different types of gene expression control during defined cell cycle stages. This will enable an unprecedented ability to interrogate the function of gene expression control for cell cycle entry, and will identify the genes for which tight control of expression is critical for correct cell cycle decisions. Together, this approach will: 1) Uncover how individual regulatory mechanisms (e.g. regulation of transcription, translation or mRNA degradation) contribute to accurate cell cycle entry through gene expression control of key cell cycle proteins 2) Examine how stochastic variability in gene expression influences the decision to enter the cell cycle

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  • Funder: EC Project Code: 101088548
    Overall Budget: 1,998,350 EURFunder Contribution: 1,998,350 EUR

    At first blush entities and concepts such as “Dutch East India Company” or “coffee” may seem straightforward, but in fact they are complex and multifaceted. The wealth of digital sources presents the massive potential to study these notions at an unprecedented scale. However, current technologies for distant reading are not capable of dealing with this. TRIFECTA aims to create a database that describes complex entities and concepts and their contexts by combining language and semantic web technology to extract and relate information from different texts over time. In addition, a key aim of TRIFECTA is to advance the state of the art in these technologies to deal with change over time and connections to many different narratives. Sophisticated knowledge representation methods from the semantic web can mitigate the failing that many language technology methods do not incorporate enough background knowledge to recognise and interpret complex entities and concepts in their historical contexts. By treating them as rich networks (or graphs) of knowledge that can express change and relationships to different concepts in space and time, semantic databases can handle the complexity needed to make the outputs of language technology tools suited to humanities research. Via two use cases, I identify a set of core contentious entities and concepts in maritime and food history. Next, through a data-driven, iterative approach, I advance beyond the state-of-the-art in natural language technology for the humanities by targeting three key aspects of the recognition and modelling of complex concepts (i.e. identity, change, and the long tail). I propose a novel peer-evaluation approach in which a team of humanities scholars, computational linguists, and semantic web researchers collaborate closely to create truly hybrid artificial intelligence systems that will enable humanities research to scale to big data without losing sight of the contextual complexity.

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  • Funder: EC Project Code: 261114
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