[Illinois] ECE 416 Protein Microarrays I

By Brian Cunningham1; NanoBio Node1

1. University of Illinois at Urbana-Champaign

Published on

Abstract

           In this lecture, we discussed the concepts of a Protein Microarray. The goal would be to be able to detect individual proteins. In the microarray, there are simultaneous measurements of many proteins for a small volume. A panel of biomarker assay is used to reduce the rate of false of positive and false negative. The more times the test is run through, the better. The protein microarrays are also used as cancer biomarkers. The cancer-derived proteins can be found in circulating blood and the concentrations are very low. Most biomarkers are proteins that show up regularly, but increase with cancer. In a microarray, a single biomarker is inconclusive for the heterogeneous disease. The microarray simultaneously profiles the pattern of proteins and provides a high throughput along with high sensitivity and specificity. A printed array usually comes out with 30 spots, and an antibody marker in each to tell us about their different levels. Multiple assays are used to put different controls and the sample is diluted to see if the ratios remain the same between the concentrations. The assay used is built as a sandwich assay with a substrate, capture antibody, chemical blocker, serum, then let them incubate, wash extra things like membranes off, add the detection antibody, incubate, wash, and Streptavidin-Cy5, Incubate, and wash. Example data is then shared. Different things that can go wrong with the protein microarray that are not seen in the DNA Microarray are also discussed. One of these that are also discussed is the selectivity. The lecture concludes with the idea of the cross reactivity test and the idea of removing one secondary Antibody at a time to solve this problem.

Bio

My research group is focused on the application of sub-wavelength optical phenomena and fabrication methods to the development of novel devices and instrumentation for the life sciences. The group is highly interdisciplinary, with expertise in the areas of microfabrication, nanotechnology, computer simulation, instrumentation, molecular biology, and cell biology. In particular, we are working on biosensors based upon photonic crystal concepts that can either be built from low-cost flexible plastic materials, or integrated with semiconductor-based active devices, such as light sources and photodetectors, for high performance integrated detection systems.

Using a combination of micrometer-scale and nanometer-scale fabrication tools, we are devising novel methods and materials for producing electro-optic devices with nanometer-scale features that can be scaled for low-cost manufacturing. Many of our techniques are geared for compatibility with flexible plastic materials, leading to applications such as low cost disposable sensors, wearable sensors, flexible electronics, and flexible displays. Because our structures manipulate light at a scale that is smaller than an optical wavelength, we rely on computer simulation tools such as Rigorous Coupled Wave Analysis (RCWA) and Finite Difference Time Doman (FDTD) to model, design, and understand optical phenomena within photonic crystals and related devices.

In addition to fabricating devices, our group is also focused on the design, prototyping, and testing of biosensor instrumentation for high sensitivity, portability, and resolution. Advanced instruments enable high resolution imaging of biochemical and cellular interactions with the ability to monitor images of biochemical interactions as a function of time. Using the sensors and instrumentation, we are exploring new applications for optical biosensor technology including protein microarrays, biosensor/mass spectrometry systems, and microfluidics-based assays using nanoliter quantities of reagents. The methods and systems developed in the laboratory are applied in the fields of life science research, drug discovery, diagnostic testing, and environmental monitoring. -From Professor Cunningham's Faculty Profile

Cite this work

Researchers should cite this work as follows:

  • Brian Cunningham, NanoBio Node (2013), "[Illinois] ECE 416 Protein Microarrays I," https://nanohub.org/resources/17702.

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Time

Location

University of Illinois, Urbana-Champaign, IL

[Illinois] ECE 416 Lecture 33: Protein Microarrays I
  • Protein Microarrays 1. Protein Microarrays 0
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  • Problem Addressed by Protein Microarrays 2. Problem Addressed by Protein M… 153.34468890466053
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  • Other Applications for Protein Microarrays 3. Other Applications for Protein… 308.30092264017031
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  • Origins 4. Origins 368.79583629051342
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  • Breast Cancer 5. Breast Cancer 494.1244381357937
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  • Cancer biomarkers 6. Cancer biomarkers 707.84007570380891
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  • Serum 7. Serum 790.4007570380885
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  • ECE/BioE 416 Lecture 23 8. ECE/BioE 416 Lecture 23 999.40572510054415
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  • Microarray 9. Microarray 1082.9581263307309
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  • Cancer biomarker panel 10. Cancer biomarker panel 1158.8246983676365
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  • Multiple assay layout and generation of standard curves 11. Multiple assay layout and gene… 1309.5661225455406
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  • 12. "Sandwich" Assay 1523.0338301395789
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  • Sandwich Assay 13. Sandwich Assay 1886.1272770286255
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  • Example Standard Curve 14. Example Standard Curve 1909.680624556423
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  • Example Standard Curve 15. Example Standard Curve 1971.6158426343322
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  • Example Standard Curve 16. Example Standard Curve 1978.0579996625122
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  • Example Standard Curve 17. Example Standard Curve 1980.0159101318613
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  • Example Standard Curve 18. Example Standard Curve 2016.5846250271195
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  • Example Standard Curve 19. Example Standard Curve 2019.8688619434467
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  • Example Standard Curve 20. Example Standard Curve 2023.2162572620109
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  • Example Standard Curve 21. Example Standard Curve 2023.8478412843817
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  • Question 22. Question 2115.5579734140688
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  • Effect of Antibody 23. Effect of Antibody 2117.1980127750176
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  • Options for Capture Molecules 24. Options for Capture Molecules 2278.3524958599478
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  • Selectivity 25. Selectivity 2280.2119706647741
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  • Characterizing Selectivity 26. Characterizing Selectivity 2457.9777620061509
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  • Characterizing Selectivity 27. Characterizing Selectivity 2599.4218121599242
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