Boronic Acid Ligands Can Target Multiple Subpopulations of Pancreatic Cancer Stem Cells via pH-Dependent Glycan-Terminal Sialic Acid Recognition

Miyazaki, Tatsuya; Khan, Thahomina; Tachihara, Yoshihiro; Ichii, Michiko; Miyazawa, Taiki; Suganami, Takayoshi; Miyahara, Yuji; Cabral, Horacio; Matsumoto, Akira

doi:10.1021/acsabm.1c00383

Cited by 14 publications

(6 citation statements)

References 30 publications

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“…[10][11][12][13] By designing intermolecular distances between boronic acid moieties, specially designed boronic acids could selectively bind to different types of glycans. [14] Researchers have used different types of boronic acids to detect sialic acids in cancer cells, [11,15] glycoproteins in serum samples, [16,17] and glycans on cell surfaces. [18,19] The combination of Raman spectroscopy and boronic acid receptors for sugar detection has been reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms

Kuai

Park

et al. 2023

Advanced Sensor Research

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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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Section: Introductionmentioning

confidence: 99%

Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms

Kuai

Park

et al. 2023

Advanced Sensor Research

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“…Liu’s group has done a series of outstanding works on the design of MIPs for glycan recognition. − PBA, as a small molecule that can specifically recognize cis -diol, has been widely used for glycan labeling. ,− On this basis, the group used a series of PBA-functionalized quantum dots with different emission wavelengths to imprint different monosaccharides and realized in situ labeling and multiplexed glycan imaging on the cell surface . They also cleaved polysialic acids from the surface of neuroblastoma for imprinting and loaded indocyanine green (ICG) into MIPs to integrate neuroblastoma-targeted glycan labeling and photothermal therapy .…”

Section: In Situ Glycan Labeling Toolsmentioning

confidence: 99%

In Situ Glycan Analysis and Editing in Living Systems

Li,

Wang,

Chen

et al. 2024

JACS Au

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Besides proteins and nucleic acids, carbohydrates are also ubiquitous building blocks of living systems. Approximately 70% of mammalian proteins are glycosylated. Glycans not only provide structural support for living systems but also act as crucial regulators of cellular functions. As a result, they are considered essential pieces of the life science puzzle. However, research on glycans has lagged far behind that on proteins and nucleic acids. The main reason is that glycans are not direct products of gene coding, and their synthesis is nontemplated. In addition, the diversity of monosaccharide species and their linkage patterns contribute to the complexity of the glycan structures, which is the molecular basis for their diverse functions. Research in glycobiology is extremely challenging, especially for the in situ elucidation of glycan structures and functions. There is an urgent need to develop highly specific glycan labeling tools and imaging methods and devise glycan editing strategies. This Perspective focuses on the challenges of in situ analysis of glycans in living systems at three spatial levels (i.e., cell, tissue, and in vivo) and highlights recent advances and directions in glycan labeling, imaging, and editing tools. We believe that examining the current development landscape and the existing bottlenecks can drive the evolution of in situ glycan analysis and intervention strategies and provide glycan-based insights for clinical diagnosis and therapeutics.

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“…By designing intermolecular distances between boronic acid moieties, boronic acids could selectively bind to different types of glycans 14 . Researchers have used different types of boronic acids to detect sialic acids in cancer cells 11, 15 , glycoproteins in serum samples 16, 17 , and glycans on cell surfaces 18, 19 . In addition, boronic acid analogs are typically attached to aryl groups that can offer fluorescent signals and distinct Raman vibrations 20 .…”

Section: Introductionmentioning

confidence: 99%

Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms

Kuai

Park

et al. 2023

Preprint

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Glycans are the most abundant and diverse fundamental biomolecules. Profiling glycans is essential to elucidate biomolecular mechanisms and to predict biological consequences; however, the analysis of glycans remains challenging due to their structural complexity. A novel glycan detection platform is established through the integration of surface-enhanced Raman spectroscopy (SERS), boronic acid receptors, and machine learning tools. SERS is able to distinguish isomeric structures and provide fingerprint information of molecules. Boronic acid receptors can selectively bind with glycans, and the reaction influences molecular vibrations, leading to unique Raman spectral patterns. For proof-of-concept, two boronic acids, 4-mercaptophenylboronic acid (4MBA) and 1-thianthrenylboronic acid (1TBA) were used for glycan detection. The spectra were analyzed by the machine learning algorithm. The sensing platform successfully recognized the stereoisomers (glucose, mannose, and galactose), the structural isomers with α-1,4 and α-1,6 glycosidic linkages, and the β-1,4 glycosidic linkage within lactose molecules. The collective spectra that combine the spectra from both boronic acid receptors could improve the performance of the support vector machine model due to the enrichment of the structural information of glycans. In addition to qualitative analysis, this new sensor could quantify the mole fraction of sialic acid in lactose background using the machine learning regression technique. The quantification accuracy reached a coefficient of determination (R2) value of 0.998 and a normalized mean square error (NMSE) of 0.00195. This low-cost, rapid, and highly accessible sensor will provide the scientific community with another option for frequent glycan screening in standard biological laboratories.

show abstract

Boronic Acid Ligands Can Target Multiple Subpopulations of Pancreatic Cancer Stem Cells via pH-Dependent Glycan-Terminal Sialic Acid Recognition

Cited by 14 publications

References 30 publications

Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms

Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms

In Situ Glycan Analysis and Editing in Living Systems

Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms

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