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An Integrated Peptide-Antigen Microarray on Plasmonic Gold Films for Sensitive Human Antibody Profiling

Overview of attention for article published in PLOS ONE, July 2013
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Title
An Integrated Peptide-Antigen Microarray on Plasmonic Gold Films for Sensitive Human Antibody Profiling
Published in
PLOS ONE, July 2013
DOI 10.1371/journal.pone.0071043
Pubmed ID
Authors

Bo Zhang, Justin A. Jarrell, Jordan V. Price, Scott M. Tabakman, Yanguang Li, Ming Gong, Guosong Hong, Ju Feng, Paul J. Utz, Hongjie Dai

Abstract

High-throughput screening for interactions of peptides with a variety of antibody targets could greatly facilitate proteomic analysis for epitope mapping, enzyme profiling, drug discovery and biomarker identification. Peptide microarrays are suited for such undertaking because of their high-throughput capability. However, existing peptide microarrays lack the sensitivity needed for detecting low abundance proteins or low affinity peptide-protein interactions. This work presents a new peptide microarray platform constructed on nanostructured plasmonic gold substrates capable of metal enhanced NIR fluorescence enhancement (NIR-FE) by hundreds of folds for screening peptide-antibody interactions with ultrahigh sensitivity. Further, an integrated histone peptide and whole antigen array is developed on the same plasmonic gold chip for profiling human antibodies in the sera of systemic lupus erythematosus (SLE) patients, revealing that collectively a panel of biomarkers against unmodified and post-translationally modified histone peptides and several whole antigens allow more accurate differentiation of SLE patients from healthy individuals than profiling biomarkers against peptides or whole antigens alone.

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Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 8%
Unknown 48 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 31%
Student > Ph. D. Student 8 15%
Student > Master 6 12%
Other 4 8%
Student > Bachelor 3 6%
Other 5 10%
Unknown 10 19%
Readers by discipline Count As %
Chemistry 13 25%
Agricultural and Biological Sciences 7 13%
Biochemistry, Genetics and Molecular Biology 6 12%
Engineering 6 12%
Immunology and Microbiology 2 4%
Other 5 10%
Unknown 13 25%