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Related Experiment Video

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Author Spotlight: An Alternative Approach to Protein Quantification by Bradford Assay Using a Smartphone
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A smartphone readable colorimetric sensing platform for rapid multiple protein detection.

Feiyang Wang1, Yuexiang Lu, Jiacheng Yang

  • 1Department of Chemistry, Capital Normal University, Xisanhuan North Rd. 105, Beijing, 100048, P.R. China. yueyingliu@cnu.edu.cn.

The Analyst
|July 27, 2017
PubMed
Summary
This summary is machine-generated.

A novel colorimetric sensor array using gold nanoparticles (AuNP) and varying salt concentrations enables sensitive protein detection. This smartphone-readable system accurately distinguishes twelve proteins, even in human urine, for potential point-of-care diagnostics.

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Area of Science:

  • Nanotechnology
  • Analytical Chemistry
  • Biomedical Engineering

Background:

  • Sensitive and specific detection of multiple proteins is crucial for early disease diagnosis.
  • Existing methods often require complex instrumentation and trained personnel.
  • Development of simple, cost-effective, and portable diagnostic tools is highly desirable.

Purpose of the Study:

  • To develop a simple, visible, and smartphone-readable sensor for sensitive detection and discrimination of multiple proteins.
  • To evaluate the sensor's performance in aqueous solutions and complex biological matrices like human urine.
  • To demonstrate the potential for point-of-care diagnosis using a colorimetric approach.

Main Methods:

  • Utilized gold nanoparticles (AuNP) as a sensing material.
  • Employed varying concentrations of NaCl to induce differential aggregation of AuNP based on protein interactions and ionic strength.
  • Developed a colorimetric sensor array for visual detection and smartphone-based color value readout.
  • Applied hierarchical clustering analysis (HCA) for data interpretation and discrimination of proteins.

Main Results:

  • The AuNP-based sensor demonstrated differential color changes in response to different proteins due to varying ionic strengths.
  • Successfully discriminated twelve proteins at 50 nM in aqueous solution.
  • Achieved 100% accuracy in discriminating these proteins at 200 nM in human urine.
  • Smartphone readout combined with HCA enabled accurate identification of twelve proteins and mixtures (lysozyme and human serum albumin) in urine.

Conclusions:

  • The proposed colorimetric sensor array offers a sensitive, selective, and cost-effective method for multiplexed protein detection.
  • The smartphone-based readout system eliminates the need for specialized equipment, facilitating point-of-care applications.
  • This technology holds significant promise for developing rapid diagnostic tools for various clinical settings.