The goal of this proposal is to create an ultrasensitive, chemical-specific, label-free Raman detection to enable characterization of microparticles (MPs) found in biological fluids. The particles derive from cells and are diagnostic of disease pathology, but challenge current methods of investigation. Our approach builds from initial results in our laboratory showing that we can detect individual liposomes in solution. Surface enhanced Raman scattering (SERS) from our nanostructured surface provides signal enhancements that enable particle identification. By combining microscale fluidics with nanotechnology, we will create an ultrasensitive detector that can be coupled to existing separation equipment.
The specific aims of this proposal are: 1) Optimize high-efficiency SERS detection using hydrodynamic focusing in a prototype detector. 2) Demonstrate the ability to distinguish lipid vesicle samples as a model system for MPs. 3) Couple the flow detector to a capillary electrophoresis separation to analyze a complex mixture. The proposal will develop the technology, validate performance, and demonstrate utility to analyze a biological sample. We will study MPs obtained from a cell culture system to prove the efficacy of this approach. This research will produce instrumentation capable of chemical characterization at ultralow concentrations, potentially the single particle level.

Public Health Relevance

This research will develop instrumentation for investigating cell-derived microparticles found in human fluids, such as blood and saliva. These particles are released in immune signaling pathways and reflect disease pathology. Ultrasensitive detection of these particles will enable improved diagnoses and treatment.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21GM107893-02
Application #
8729504
Study Section
Special Emphasis Panel (ZGM1-BBCB-A (BT))
Program Officer
Friedman, Fred K
Project Start
2013-09-02
Project End
2016-06-30
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
2
Fiscal Year
2014
Total Cost
$190,000
Indirect Cost
$65,000
Name
University of Notre Dame
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
824910376
City
Notre Dame
State
IN
Country
United States
Zip Code
46556
Bailey, Matthew R; Schultz, Zachary D (2016) SERS speciation of the electrochemical oxidation-reduction of riboflavin. Analyst 141:5078-87
Bailey, Matthew R; Martin, R Scott; Schultz, Zachary D (2016) Role of Surface Adsorption in the Surface-Enhanced Raman Scattering and Electrochemical Detection of Neurotransmitters. J Phys Chem C Nanomater Interfaces 120:20624-20633
Riordan, Colleen M; Jacobs, Kevin T; Negri, Pierre et al. (2016) Sheath flow SERS for chemical profiling in urine. Faraday Discuss 187:473-84
Jacobs, Kevin T; Schultz, Zachary D (2015) Increased SERS detection efficiency for characterizing rare events in flow. Anal Chem 87:8090-5
Bailey, Matthew R; Pentecost, Amber M; Selimovic, Asmira et al. (2015) Sheath-flow microfluidic approach for combined surface enhanced Raman scattering and electrochemical detection. Anal Chem 87:4347-55
Asiala, Steven M; Marr, James M; Gervinskas, Gediminas et al. (2015) Plasmonic color analysis of Ag-coated black-Si SERS substrate. Phys Chem Chem Phys 17:30461-7
Negri, Pierre; Sarver, Scott A; Schiavone, Nicole M et al. (2015) Online SERS detection and characterization of eight biologically-active peptides separated by capillary zone electrophoresis. Analyst 140:1516-22
Wang, Hao; Carrier, Stacey L; Park, Sheldon et al. (2015) Selective TERS detection and imaging through controlled plasmonics. Faraday Discuss 178:221-35
Negri, Pierre; Flaherty, Ryan J; Dada, Oluwatosin O et al. (2014) Ultrasensitive online SERS detection of structural isomers separated by capillary zone electrophoresis. Chem Commun (Camb) 50:2707-10
Negri, Pierre; Schultz, Zachary D (2014) Online SERS detection of the 20 proteinogenic L-amino acids separated by capillary zone electrophoresis. Analyst 139:5989-98

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