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Abstract Liposomes are effective therapeutic nanocarriers due to their ability to encapsulate and enhance the pharmacokinetic properties of a wide range of drugs and diagnostic agents. A primary area in which improvement is needed for liposomal drug delivery is to maximize the delivery of these nanocarriers to cells. Cell membrane glycans provide exciting targets for liposomal delivery since they are often densely clustered on cell membranes and glycan overabundance and aberrant glycosylation patterns are a common feature of diseased cells. Herein, we report a liposome platform incorporating bis‐boronic acid lipids (BBALs) to increase valency in order to achieve selective saccharide sensing and enhance cell surface recognition based on carbohydrate binding interactions. In order to vary properties, multiple BBALs (1 a–d) with variable linkers in between the binding units were designed and synthesized. Fluorescence‐based microplate screening of carbohydrate binding showed that these compounds exhibit varying binding properties depending on their structures. Additionally, fluorescence microscopy experiments indicated enhancements in cellular association when BBALs were incorporated within liposomes. These results demonstrate that multivalent BBALs serve as an exciting glycan binding liposome system for targeted delivery.
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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 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.
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- PAR ID:
- 10427237
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Sensor Research
- Volume:
- 2
- Issue:
- 12
- ISSN:
- 2751-1219
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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