Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms
Abstract Glycans are the most abundant fundamental biomolecules, but profiling glycans is challenging due to their structural complexity. To address this, a novel glycan detection platform is developed by integrating surface‐enhanced Raman spectroscopy (SERS), boronic acid receptors, and machine lea...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley-VCH
2023-12-01
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| Series: | Advanced Sensor Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/adsr.202300052 |
| _version_ | 1797392172871319552 |
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| author | Qiang Hu Dacheng Kuai Hyundo Park Haley Clark Perla B. Balbuena Joseph Sang‐Il Kwon Hung‐Jen Wu |
| author_facet | Qiang Hu Dacheng Kuai Hyundo Park Haley Clark Perla B. Balbuena Joseph Sang‐Il Kwon Hung‐Jen Wu |
| author_sort | Qiang Hu |
| collection | DOAJ |
| description | Abstract Glycans are the most abundant fundamental biomolecules, but profiling glycans is challenging due to their structural complexity. To address this, a novel glycan detection platform is developed by integrating surface‐enhanced Raman spectroscopy (SERS), boronic acid receptors, and machine learning tools. Boronic acid receptors bind with glycans, and the reaction influences molecular vibrations, leading to unique Raman spectral patterns. Unlike prior studies that focus on designing a boronic acid with high binding selectivity toward a target glycan, this sensor is designed to analyze overall changes in spectral patterns using machine learning algorithms. For proof‐of‐concept, 4‐mercaptophenylboronic acid (4MBA) and 1‐thianthrenylboronic acid (1TBA) are used for glycan detection. The sensing platform successfully recognizes the stereoisomers and the structural isomers with different glycosidic linkages. The collective spectra that combine the spectra from both boronic acid receptors improve the performance of the support vector machine model due to the enrichment of the structural information of glycans. In addition, this new sensor could quantify the mole fraction of sialic acid in lactose background using the machine learning regression technique. This low‐cost, rapid, and highly accessible sensor will provide the scientific community with another option for frequent comparative glycan screening in standard biological laboratories. |
| first_indexed | 2024-03-08T23:43:27Z |
| format | Article |
| id | doaj.art-236d53ee90af4e349d182789d676598f |
| institution | Directory Open Access Journal |
| issn | 2751-1219 |
| language | English |
| last_indexed | 2024-03-08T23:43:27Z |
| publishDate | 2023-12-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Advanced Sensor Research |
| spelling | doaj.art-236d53ee90af4e349d182789d676598f2023-12-14T03:47:48ZengWiley-VCHAdvanced Sensor Research2751-12192023-12-01212n/an/a10.1002/adsr.202300052Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning AlgorithmsQiang Hu0Dacheng Kuai1Hyundo Park2Haley Clark3Perla B. Balbuena4Joseph Sang‐Il Kwon5Hung‐Jen Wu6The Artie McFerrin Department of Chemical Engineering Texas A&M University College Station TX 77843 USAThe Artie McFerrin Department of Chemical Engineering Texas A&M University College Station TX 77843 USAThe Artie McFerrin Department of Chemical Engineering Texas A&M University College Station TX 77843 USADepartment of Biomedical Engineering Texas A&M University College Station TX 77843 USAThe Artie McFerrin Department of Chemical Engineering Texas A&M University College Station TX 77843 USAThe Artie McFerrin Department of Chemical Engineering Texas A&M University College Station TX 77843 USAThe Artie McFerrin Department of Chemical Engineering Texas A&M University College Station TX 77843 USAAbstract Glycans are the most abundant fundamental biomolecules, but profiling glycans is challenging due to their structural complexity. To address this, a novel glycan detection platform is developed by integrating surface‐enhanced Raman spectroscopy (SERS), boronic acid receptors, and machine learning tools. Boronic acid receptors bind with glycans, and the reaction influences molecular vibrations, leading to unique Raman spectral patterns. Unlike prior studies that focus on designing a boronic acid with high binding selectivity toward a target glycan, this sensor is designed to analyze overall changes in spectral patterns using machine learning algorithms. For proof‐of‐concept, 4‐mercaptophenylboronic acid (4MBA) and 1‐thianthrenylboronic acid (1TBA) are used for glycan detection. The sensing platform successfully recognizes the stereoisomers and the structural isomers with different glycosidic linkages. The collective spectra that combine the spectra from both boronic acid receptors improve the performance of the support vector machine model due to the enrichment of the structural information of glycans. In addition, this new sensor could quantify the mole fraction of sialic acid in lactose background using the machine learning regression technique. This low‐cost, rapid, and highly accessible sensor will provide the scientific community with another option for frequent comparative glycan screening in standard biological laboratories.https://doi.org/10.1002/adsr.202300052boronic acidschemometricsglycan detectionmachine learningsurface‐enhanced Raman scatteringsupport vector machine |
| spellingShingle | Qiang Hu Dacheng Kuai Hyundo Park Haley Clark Perla B. Balbuena Joseph Sang‐Il Kwon Hung‐Jen Wu Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms Advanced Sensor Research boronic acids chemometrics glycan detection machine learning surface‐enhanced Raman scattering support vector machine |
| title | Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms |
| title_full | Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms |
| title_fullStr | Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms |
| title_full_unstemmed | Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms |
| title_short | Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms |
| title_sort | advancing glycan analysis a new platform integrating sers boronic acids and machine learning algorithms |
| topic | boronic acids chemometrics glycan detection machine learning surface‐enhanced Raman scattering support vector machine |
| url | https://doi.org/10.1002/adsr.202300052 |
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