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SuperCol: Rational design of super-selective and responsive colloidal particles for biomedical applications

Project description

Tiny colloidal superheros could have oversized power for diagnosis and treatment

Colloidal particles are microscopic or even nanoscopic-sized particles whose surfaces can be functionalised. Their very large surface areas relative to their small volumes means you can load each one many molecules to deliver and release drugs or bind pathogens and biomarkers at the target site, leading to powerful diagnostic and therapeutic systems. However, their potential has yet to be exploited due to technical limitations with control of receptor activity and placement. SuperCol plans to get a closer look at colloidal surfaces both visually and with receptor chemistry. Observations will inform models with the goal of rationale design of super-selective and responsive particles that enable capture (of disease biomarkers) or release (of therapeutics) on demand.

Objective

SuperCol will train a new generation of scientists in modern super-resolution microscopy and particle functionalisation for improved colloidal design and nanoscale interfacial control for particle-based biosensing. Colloidal-particle-based materials have shown unique abilities in (bio)sensing and other applications because of their large surface-to-volume ratio and biochemical functionalisation. However, we have had only limited success in controlling the sensing properties and cannot yet realise the sensitivity, selectivity and kinetic control that is achieved by e.g. cellular sensing networks. In order to realise this we need to initiate a paradigm shift and create surfaces with a quantified and rationally designed number, distribution, and activity of receptors. To achieve this, we need to understand the structure-function relation between the loading patterns and the sensing characteristics. At this moment, we do not know: (1) how to accurately visualise individual receptors on colloidal surfaces, (2) how to influence the number, distribution and activity of receptors and (3) how this surface organisation translates to highly desired functionalities such as responsiveness and super-selectivity. Super-resolution imaging, advanced particle surface chemistry and advanced modelling will allow to bridge these gaps. However, the field lacks the human capital that can oversee and bridge these disciplines to advance these approaches and effectively (a) control, (b) visualise and quantify, and (c) rationally design surface-functionality to advance particle-based biomedical applications. SuperCol will train the next generation of researchers to overcome this barrier, and will develop e.g. super-selective biosensors for dengue and cholera, and responsive particles that allow biomolecules to be captured (e.g. inflammation markers) and released (e.g. doxorubicin) on demand.

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

MSCA-ITN - Marie Skłodowska-Curie Innovative Training Networks (ITN)

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Call for proposal

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(opens in new window) H2020-MSCA-ITN-2019

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Coordinator

TECHNISCHE UNIVERSITEIT EINDHOVEN
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 062 479,52
Address
GROENE LOPER 3
5612 AE Eindhoven
Netherlands

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Region
Zuid-Nederland Noord-Brabant Zuidoost-Noord-Brabant
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 062 479,52

Participants (7)

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