Summary
To date, antibody-free protein identification methods have not reached single-molecule precision. Instead, they rely on averaging from many cells, obscuring the details of important biological processes. The ability to identify each individual protein from within a single cell would transform proteomics research and biomedicine. However, single protein identification (ID) presents a major challenge, necessitating a breakthrough in single-molecule sensing technologies.
We propose to develop a method for proteome-level analysis, with single protein resolution. Bioinformatics studies show that >99% of human proteins can be uniquely identified by the order in which only three amino-acids, Lysine, Cysteine, and Methionine (K, C and M, respectively), appear along the proteins’ chain. By specifically labelling K, C and M residues with three distinct fluorophores, and threading them, one by one, through solid-state nanopores equipped with custom plasmonic amplifiers, we hypothesize that we can obtain multi-color fluorescence time-trace fingerprints uniquely representing most proteins in the human proteome. The feasibility of our method will be established by attaining 4 main aims: i) in vitro K,C,M protein labelling, ii) development of a machine learning classifier to uniquely ID proteins based on their optical fingerprints, iii) fabrication of state-of-the-art plasmonic nanopores for high-resolution optical sensing of proteins, and iv) devising methods for regulating the translocation speed to enhance the signal to noise ratio. Next, we will scale up our platform to enable the analysis of thousands of different proteins in minutes, and apply it to sense blood-secreted proteins, as well as whole proteomes in pre- and post-metastatic cancer cells. NanoProt-ID constitutes the first and most challenging step towards the proteomic analysis of individual cells, opening vast research directions and applications in biomedicine and systems biology.
We propose to develop a method for proteome-level analysis, with single protein resolution. Bioinformatics studies show that >99% of human proteins can be uniquely identified by the order in which only three amino-acids, Lysine, Cysteine, and Methionine (K, C and M, respectively), appear along the proteins’ chain. By specifically labelling K, C and M residues with three distinct fluorophores, and threading them, one by one, through solid-state nanopores equipped with custom plasmonic amplifiers, we hypothesize that we can obtain multi-color fluorescence time-trace fingerprints uniquely representing most proteins in the human proteome. The feasibility of our method will be established by attaining 4 main aims: i) in vitro K,C,M protein labelling, ii) development of a machine learning classifier to uniquely ID proteins based on their optical fingerprints, iii) fabrication of state-of-the-art plasmonic nanopores for high-resolution optical sensing of proteins, and iv) devising methods for regulating the translocation speed to enhance the signal to noise ratio. Next, we will scale up our platform to enable the analysis of thousands of different proteins in minutes, and apply it to sense blood-secreted proteins, as well as whole proteomes in pre- and post-metastatic cancer cells. NanoProt-ID constitutes the first and most challenging step towards the proteomic analysis of individual cells, opening vast research directions and applications in biomedicine and systems biology.
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| Web resources: | https://cordis.europa.eu/project/id/833399 |
| Start date: | 01-08-2019 |
| End date: | 31-07-2024 |
| Total budget - Public funding: | 2 498 869,00 Euro - 2 498 869,00 Euro |
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Original description
To date, antibody-free protein identification methods have not reached single-molecule precision. Instead, they rely on averaging from many cells, obscuring the details of important biological processes. The ability to identify each individual protein from within a single cell would transform proteomics research and biomedicine. However, single protein identification (ID) presents a major challenge, necessitating a breakthrough in single-molecule sensing technologies.We propose to develop a method for proteome-level analysis, with single protein resolution. Bioinformatics studies show that >99% of human proteins can be uniquely identified by the order in which only three amino-acids, Lysine, Cysteine, and Methionine (K, C and M, respectively), appear along the proteins’ chain. By specifically labelling K, C and M residues with three distinct fluorophores, and threading them, one by one, through solid-state nanopores equipped with custom plasmonic amplifiers, we hypothesize that we can obtain multi-color fluorescence time-trace fingerprints uniquely representing most proteins in the human proteome. The feasibility of our method will be established by attaining 4 main aims: i) in vitro K,C,M protein labelling, ii) development of a machine learning classifier to uniquely ID proteins based on their optical fingerprints, iii) fabrication of state-of-the-art plasmonic nanopores for high-resolution optical sensing of proteins, and iv) devising methods for regulating the translocation speed to enhance the signal to noise ratio. Next, we will scale up our platform to enable the analysis of thousands of different proteins in minutes, and apply it to sense blood-secreted proteins, as well as whole proteomes in pre- and post-metastatic cancer cells. NanoProt-ID constitutes the first and most challenging step towards the proteomic analysis of individual cells, opening vast research directions and applications in biomedicine and systems biology.
Status
SIGNEDCall topic
ERC-2018-ADGUpdate Date
27-04-2024
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