Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus
Fluorescence-based microarray offers great potential in clinical diagnostics due to its high-throughput capability, multiplex capabilities, and requirement for a minimal volume of precious clinical samples. However, the technique relies on expensive and complex imaging systems for the analysis of si...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Chemosensors |
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| Online Access: | https://www.mdpi.com/2227-9040/10/8/330 |
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| author | Guang Yang Yaxi Li Chenling Tang Feng Lin Tianfu Wu Jiming Bao |
| author_facet | Guang Yang Yaxi Li Chenling Tang Feng Lin Tianfu Wu Jiming Bao |
| author_sort | Guang Yang |
| collection | DOAJ |
| description | Fluorescence-based microarray offers great potential in clinical diagnostics due to its high-throughput capability, multiplex capabilities, and requirement for a minimal volume of precious clinical samples. However, the technique relies on expensive and complex imaging systems for the analysis of signals. In the present study, we developed a smartphone-based application to analyze signals from protein microarrays to quantify disease biomarkers. The application adopted Android Studio open platform for its wide access to smartphones, and Python was used to design a graphical user interface with fast data processing. The application provides multiple user functions such as “Read”, “Analyze”, “Calculate” and “Report”. For rapid and accurate results, we used ImageJ, Otsu thresholding, and local thresholding to quantify the fluorescent intensity of spots on the microarray. To verify the efficacy of the application, three antigens each with over 110 fluorescent spots were tested. Particularly, a positive correlation of over 0.97 was achieved when using this analytical tool compared to a standard test for detecting a potential biomarker in lupus nephritis. Collectively, this smartphone application tool shows promise for cheap, efficient, and portable on-site detection in point-of-care diagnostics. |
| first_indexed | 2024-03-09T04:35:38Z |
| format | Article |
| id | doaj.art-2c6da66abfd9486e8877779b4e10574f |
| institution | Directory Open Access Journal |
| issn | 2227-9040 |
| language | English |
| last_indexed | 2024-03-09T04:35:38Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Chemosensors |
| spelling | doaj.art-2c6da66abfd9486e8877779b4e10574f2023-12-03T13:27:51ZengMDPI AGChemosensors2227-90402022-08-0110833010.3390/chemosensors10080330Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in LupusGuang Yang0Yaxi Li1Chenling Tang2Feng Lin3Tianfu Wu4Jiming Bao5Materials Science & Engineering, University of Houston, Houston, TX 77204, USADepartment of Biomedical Engineering, University of Houston, Houston, TX 77204, USADepartment of Biomedical Engineering, University of Houston, Houston, TX 77204, USADepartment of Electrical and Computer Engineering, Texas Center for Superconductivity (TCSUH), University of Houston, Houston, TX 77204, USADepartment of Biomedical Engineering, University of Houston, Houston, TX 77204, USAMaterials Science & Engineering, University of Houston, Houston, TX 77204, USAFluorescence-based microarray offers great potential in clinical diagnostics due to its high-throughput capability, multiplex capabilities, and requirement for a minimal volume of precious clinical samples. However, the technique relies on expensive and complex imaging systems for the analysis of signals. In the present study, we developed a smartphone-based application to analyze signals from protein microarrays to quantify disease biomarkers. The application adopted Android Studio open platform for its wide access to smartphones, and Python was used to design a graphical user interface with fast data processing. The application provides multiple user functions such as “Read”, “Analyze”, “Calculate” and “Report”. For rapid and accurate results, we used ImageJ, Otsu thresholding, and local thresholding to quantify the fluorescent intensity of spots on the microarray. To verify the efficacy of the application, three antigens each with over 110 fluorescent spots were tested. Particularly, a positive correlation of over 0.97 was achieved when using this analytical tool compared to a standard test for detecting a potential biomarker in lupus nephritis. Collectively, this smartphone application tool shows promise for cheap, efficient, and portable on-site detection in point-of-care diagnostics.https://www.mdpi.com/2227-9040/10/8/330fluorescent microarraysmartphone applicationclinical diagnosticsbiomarkerimage processing |
| spellingShingle | Guang Yang Yaxi Li Chenling Tang Feng Lin Tianfu Wu Jiming Bao Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus Chemosensors fluorescent microarray smartphone application clinical diagnostics biomarker image processing |
| title | Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus |
| title_full | Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus |
| title_fullStr | Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus |
| title_full_unstemmed | Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus |
| title_short | Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus |
| title_sort | smartphone based quantitative analysis of protein array signals for biomarker detection in lupus |
| topic | fluorescent microarray smartphone application clinical diagnostics biomarker image processing |
| url | https://www.mdpi.com/2227-9040/10/8/330 |
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