Colorimetric aptasensor coupled with a deep-learning-powered smartphone app for programmed death ligand-1 expressing extracellular vesicles
Lung cancer is a devastating public health threat and a leading cause of cancer-related deaths. Therefore, it is imperative to develop sophisticated techniques for the non-invasive detection of lung cancer. Extracellular vesicles expressing programmed death ligand-1 (PD-L1) markers (PD-L1@EVs) in th...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1479403/full |
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| author | Adeel Khan Adeel Khan Haroon Khan Nongyue He Zhiyang Li Heba Khalil Alyahya Yousef A. Bin Jardan |
| author_facet | Adeel Khan Adeel Khan Haroon Khan Nongyue He Zhiyang Li Heba Khalil Alyahya Yousef A. Bin Jardan |
| author_sort | Adeel Khan |
| collection | DOAJ |
| description | Lung cancer is a devastating public health threat and a leading cause of cancer-related deaths. Therefore, it is imperative to develop sophisticated techniques for the non-invasive detection of lung cancer. Extracellular vesicles expressing programmed death ligand-1 (PD-L1) markers (PD-L1@EVs) in the blood are reported to be indicative of lung cancer and response to immunotherapy. Our approach is the development of a colorimetric aptasensor by combining the rapid capturing efficiency of (Fe3O4)-SiO2-TiO2 for EV isolation with PD-L1 aptamer-triggered enzyme-linked hybridization chain reaction (HCR) for signal amplification. The numerous HRPs catalyze their substrate dopamine (colorless) into polydopamine (blackish brown). Change in chromaticity directly correlates with the concentration of PD-L1@EVs in the sample. The colorimetric aptasensor was able to detect PD-L1@EVs at concentrations as low as 3.6×102 EVs/mL with a wide linear range from 103 to 1010 EVs/mL with high specificity and successfully detected lung cancer patients’ serum from healthy volunteers’ serum. To transform the qualitative colorimetric approach into a quantitative operation, we developed an intelligent convolutional neural network (CNN)-powered quantitative analyzer for chromaticity in the form of a smartphone app named ExoP, thereby achieving the intelligent analysis of chromaticity with minimal user intervention or additional hardware attachments for the sensitive and specific quantification of PD-L1@EVs. This combined approach offers a simple, sensitive, and specific tool for lung cancer detection using PD-L1@EVs. The addition of a CNN-powered smartphone app further eliminates the need for specialized equipment, making the colorimetric aptasensor more accessible for low-resource settings. |
| format | Article |
| id | doaj-art-a4fb94e6e5fe497fbfd91beb33f9b609 |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-a4fb94e6e5fe497fbfd91beb33f9b6092025-01-23T06:56:13ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011510.3389/fimmu.2024.14794031479403Colorimetric aptasensor coupled with a deep-learning-powered smartphone app for programmed death ligand-1 expressing extracellular vesiclesAdeel Khan0Adeel Khan1Haroon Khan2Nongyue He3Zhiyang Li4Heba Khalil Alyahya5Yousef A. Bin Jardan6State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaDepartment of Biotechnology, University of Science and Technology, Bannu, PakistanNeuroscience and Neuroengineering Research Center, Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaState Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaDepartment of Clinical Laboratory, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, ChinaDepartment of Exercise Physiology, College of Sport Science and Physical Activity, King Saud University, Riyadh, Saudi ArabiaDepartment of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi ArabiaLung cancer is a devastating public health threat and a leading cause of cancer-related deaths. Therefore, it is imperative to develop sophisticated techniques for the non-invasive detection of lung cancer. Extracellular vesicles expressing programmed death ligand-1 (PD-L1) markers (PD-L1@EVs) in the blood are reported to be indicative of lung cancer and response to immunotherapy. Our approach is the development of a colorimetric aptasensor by combining the rapid capturing efficiency of (Fe3O4)-SiO2-TiO2 for EV isolation with PD-L1 aptamer-triggered enzyme-linked hybridization chain reaction (HCR) for signal amplification. The numerous HRPs catalyze their substrate dopamine (colorless) into polydopamine (blackish brown). Change in chromaticity directly correlates with the concentration of PD-L1@EVs in the sample. The colorimetric aptasensor was able to detect PD-L1@EVs at concentrations as low as 3.6×102 EVs/mL with a wide linear range from 103 to 1010 EVs/mL with high specificity and successfully detected lung cancer patients’ serum from healthy volunteers’ serum. To transform the qualitative colorimetric approach into a quantitative operation, we developed an intelligent convolutional neural network (CNN)-powered quantitative analyzer for chromaticity in the form of a smartphone app named ExoP, thereby achieving the intelligent analysis of chromaticity with minimal user intervention or additional hardware attachments for the sensitive and specific quantification of PD-L1@EVs. This combined approach offers a simple, sensitive, and specific tool for lung cancer detection using PD-L1@EVs. The addition of a CNN-powered smartphone app further eliminates the need for specialized equipment, making the colorimetric aptasensor more accessible for low-resource settings.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1479403/fullextracellular vesiclesexosomesprogrammed death ligand-1PD-L1lung cancerAI -diagnostic |
| spellingShingle | Adeel Khan Adeel Khan Haroon Khan Nongyue He Zhiyang Li Heba Khalil Alyahya Yousef A. Bin Jardan Colorimetric aptasensor coupled with a deep-learning-powered smartphone app for programmed death ligand-1 expressing extracellular vesicles Frontiers in Immunology extracellular vesicles exosomes programmed death ligand-1 PD-L1 lung cancer AI -diagnostic |
| title | Colorimetric aptasensor coupled with a deep-learning-powered smartphone app for programmed death ligand-1 expressing extracellular vesicles |
| title_full | Colorimetric aptasensor coupled with a deep-learning-powered smartphone app for programmed death ligand-1 expressing extracellular vesicles |
| title_fullStr | Colorimetric aptasensor coupled with a deep-learning-powered smartphone app for programmed death ligand-1 expressing extracellular vesicles |
| title_full_unstemmed | Colorimetric aptasensor coupled with a deep-learning-powered smartphone app for programmed death ligand-1 expressing extracellular vesicles |
| title_short | Colorimetric aptasensor coupled with a deep-learning-powered smartphone app for programmed death ligand-1 expressing extracellular vesicles |
| title_sort | colorimetric aptasensor coupled with a deep learning powered smartphone app for programmed death ligand 1 expressing extracellular vesicles |
| topic | extracellular vesicles exosomes programmed death ligand-1 PD-L1 lung cancer AI -diagnostic |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1479403/full |
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