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Multiplexed, Single Molecule Protein Identification for Single Cell Proteomics

Multiplexed, Single Molecule Protein Identification for Single Cell Proteomics
单细胞蛋白质组学的多重、单分子蛋白质鉴定
批准号:
8413642
负责人:
Peter Alan Sims
金额:
$21.9万
依托单位国家:
美国
项目类别:
财政年份:
2012
资助国家:
美国
项目状态:
已结题
起止时间:
2012-09-30 至 2014-08-31

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英文摘要
DESCRIPTION (provided by applicant): The main objective of this project is to enable single cell proteomics by developing a novel, ultrasensitive technology for protein identification. This will be accomplished by combining the "bottom-up" approach to proteomics typically employed in mass spectrometry with single molecule fluorescence microscopy and a microfabricated array platform. Single protein molecules will be trypsinized in micron-scale chambers that are chemically modified to capture the resultant peptides (Aim 1). By applying amino acid-specific labels and monitoring the activity of amino acid-specific endopeptidases, single molecule fluorescence imaging can be used to obtain sufficient sequence information from individual tryptic peptides to identify a single protein molecule (Aim 2). The single molecule sensitivity and digital quantification afforded by this technique will be used to measure protein abundance in single cells in a variety of experimental contexts. In particular, a microfluidic device with an integrated protein identification array will be interfaced with laser capture microdissection to extract individual cells from glioblastoma samples. Large-scale proteomic characterization of individual cancer cells will provide an unprecedented view of tumor heterogeneity, a key obstacle to effectively treating many forms of cancer. PUBLIC HEALTH RELEVANCE: Heterogeneity in cancer and host-pathogen systems poses a serious challenge to conventional bulk studies of diseased tissue. By enabling the characterization and quantification of a large fraction of proteins from individual cells, this proect will significantly enhance our ability to understand these complex systems, analyze clinical samples, and ultimately design more effective treatments.
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Advanced Single Cell Technology Innovation Core
The Bioinformatics and Single Cell Analysis Core
The Bioinformatics and Single Cell Analysis Core
Single Cell Core
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