Summary
Single-molecule protein sensing and sequencing technologies are emerging on the horizon as new powerful tools, which ultimate goal is the characterization of the proteome, namely the total set of proteins made by a cell or organism. Despite recent advances, challenges in terms of full-length reads, high throughput and intrinsic complexity related to the larger set of building blocks (i.e. 20 amino acids) still remain. Furthermore, the unambiguous identification of proteins at low concentration is an open issue for conventional techniques, such as Mass Spectroscopy and ELISA. I aim to develop a plasmon-enhanced single-molecule sensing device to track and identify proteins, combining a dual-color amino-acid-specific technique to label the proteins and the enhancement to the fluorescence provided by properly-designed plasmonic nanostructures integrated within a nanochannel-based device. The plasmonic nanochannel is filled with a custom-made gel to slow down the motion of proteins and separate them by their molecular mass during the electrophoretic migration, meanwhile plasmonic nanostructures enhance the fluorescence signals acquired from each individual dual-color labelled protein. I will focus on the optimization of the device and testing its capabilities for the discrimination of clinically-relevant isoforms of Vascular endothelial growth factor (VEGFa) family. I will tackle the challenge of isoforms identification even further, by using the device to discriminate between VEGFa and VEGFb, which differ for only six amino acids at the C termini. Results from this work will provide a technique for the identification and quantification of protein isoforms with single-molecule resolution. Potentially, the use of this diagnostic tool will provide new insight into the role of VEGF isoforms and their up/down regulation into several diseases and in turns, also new avenues for targeted therapeutics.
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| Web resources: | https://cordis.europa.eu/project/id/101152070 |
| Start date: | 01-05-2024 |
| End date: | 30-04-2026 |
| Total budget - Public funding: | - 184 698,00 Euro |
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Original description
Single-molecule protein sensing and sequencing technologies are emerging on the horizon as new powerful tools, which ultimate goal is the characterization of the proteome, namely the total set of proteins made by a cell or organism. Despite recent advances, challenges in terms of full-length reads, high throughput and intrinsic complexity related to the larger set of building blocks (i.e. 20 amino acids) still remain. Furthermore, the unambiguous identification of proteins at low concentration is an open issue for conventional techniques, such as Mass Spectroscopy and ELISA. I aim to develop a plasmon-enhanced single-molecule sensing device to track and identify proteins, combining a dual-color amino-acid-specific technique to label the proteins and the enhancement to the fluorescence provided by properly-designed plasmonic nanostructures integrated within a nanochannel-based device. The plasmonic nanochannel is filled with a custom-made gel to slow down the motion of proteins and separate them by their molecular mass during the electrophoretic migration, meanwhile plasmonic nanostructures enhance the fluorescence signals acquired from each individual dual-color labelled protein. I will focus on the optimization of the device and testing its capabilities for the discrimination of clinically-relevant isoforms of Vascular endothelial growth factor (VEGFa) family. I will tackle the challenge of isoforms identification even further, by using the device to discriminate between VEGFa and VEGFb, which differ for only six amino acids at the C termini. Results from this work will provide a technique for the identification and quantification of protein isoforms with single-molecule resolution. Potentially, the use of this diagnostic tool will provide new insight into the role of VEGF isoforms and their up/down regulation into several diseases and in turns, also new avenues for targeted therapeutics.Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
22-11-2024
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