{"title":"通过整合机器学习驱动的表面增强拉曼散射和硼酸阵列直接分析生物样本和完整糖蛋白","authors":"Qiang Hu, and , Hung-Jen Wu*, ","doi":"10.1021/acsmeasuresciau.4c00014","DOIUrl":null,"url":null,"abstract":"<p >Frequent monitoring of glycan patterns is a critical step in studying glycan-mediated cellular processes. However, the current glycan analysis tools are resource-intensive and less suitable for routine use in standard laboratories. We developed a novel glycan detection platform by integrating surface-enhanced Raman spectroscopy (SERS), boronic acid (BA) receptors, and machine learning tools. This sensor monitors the molecular fingerprint spectra of BA binding to <i>cis</i>-diol-containing glycans. Different types of BA receptors could yield different stereoselective reactions toward different glycans and exhibit unique vibrational spectra. By integration of the Raman spectra collected from different BA receptors, the structural information can be enriched, eventually improving the accuracy of glycan classification and quantification. Here, we established a SERS-based sensor incorporating multiple different BA receptors. This sensing platform could directly analyze the biological samples, including whole milk and intact glycoproteins (fetuin and asialofetuin), without tedious glycan release and purification steps. The results demonstrate the platform’s ability to classify milk oligosaccharides with remarkable classification accuracy, despite the presence of other non-glycan constituents in the background. This sensor could also directly quantify sialylation levels of a fetuin/asialofetuin mixture without glycan release procedures. Moreover, by selecting appropriate BA receptors, the sensor exhibits an excellent performance of differentiating between α2,3 and α2,6 linkages of sialic acids. 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引用次数: 0
摘要
频繁监测聚糖模式是研究聚糖介导的细胞过程的关键步骤。然而,目前的聚糖分析工具资源密集,不太适合标准实验室的常规使用。我们通过整合表面增强拉曼光谱(SERS)、硼酸(BA)受体和机器学习工具,开发了一种新型聚糖检测平台。这种传感器可监测硼酸与含顺式二醇聚糖结合的分子指纹谱。不同类型的硼酸受体会对不同的聚糖产生不同的立体选择性反应,并表现出独特的振动光谱。通过整合从不同 BA 受体收集到的拉曼光谱,可以丰富结构信息,最终提高聚糖分类和定量的准确性。在这里,我们建立了一种基于 SERS 的传感器,其中包含多种不同的 BA 受体。这种传感平台可以直接分析生物样品,包括全脂牛奶和完整的糖蛋白(胎盘素和asialofetuin),而无需繁琐的聚糖释放和纯化步骤。结果表明,尽管背景中存在其他非糖类成分,该平台仍能对牛奶低聚糖进行分类,且分类准确性极高。这种传感器还能直接量化胎盘素/胎盘素混合物的糖基化水平,而无需糖释放步骤。此外,通过选择适当的 BA 受体,该传感器在区分α2,3 和α2,6 连接的硅烷酸方面表现出色。这种低成本、快速且高度易用的传感器将为科学界提供一种在标准实验室中进行常规聚糖筛选的宝贵工具。
Direct Glycan Analysis of Biological Samples and Intact Glycoproteins by Integrating Machine Learning-Driven Surface-Enhanced Raman Scattering and Boronic Acid Arrays
Frequent monitoring of glycan patterns is a critical step in studying glycan-mediated cellular processes. However, the current glycan analysis tools are resource-intensive and less suitable for routine use in standard laboratories. We developed a novel glycan detection platform by integrating surface-enhanced Raman spectroscopy (SERS), boronic acid (BA) receptors, and machine learning tools. This sensor monitors the molecular fingerprint spectra of BA binding to cis-diol-containing glycans. Different types of BA receptors could yield different stereoselective reactions toward different glycans and exhibit unique vibrational spectra. By integration of the Raman spectra collected from different BA receptors, the structural information can be enriched, eventually improving the accuracy of glycan classification and quantification. Here, we established a SERS-based sensor incorporating multiple different BA receptors. This sensing platform could directly analyze the biological samples, including whole milk and intact glycoproteins (fetuin and asialofetuin), without tedious glycan release and purification steps. The results demonstrate the platform’s ability to classify milk oligosaccharides with remarkable classification accuracy, despite the presence of other non-glycan constituents in the background. This sensor could also directly quantify sialylation levels of a fetuin/asialofetuin mixture without glycan release procedures. Moreover, by selecting appropriate BA receptors, the sensor exhibits an excellent performance of differentiating between α2,3 and α2,6 linkages of sialic acids. This low-cost, rapid, and highly accessible sensor will provide the scientific community with an invaluable tool for routine glycan screening in standard laboratories.
期刊介绍:
ACS Measurement Science Au is an open access journal that publishes experimental computational or theoretical research in all areas of chemical measurement science. Short letters comprehensive articles reviews and perspectives are welcome on topics that report on any phase of analytical operations including sampling measurement and data analysis. This includes:Chemical Reactions and SelectivityChemometrics and Data ProcessingElectrochemistryElemental and Molecular CharacterizationImagingInstrumentationMass SpectrometryMicroscale and Nanoscale systemsOmics (Genomics Proteomics Metabonomics Metabolomics and Bioinformatics)Sensors and Sensing (Biosensors Chemical Sensors Gas Sensors Intracellular Sensors Single-Molecule Sensors Cell Chips Arrays Microfluidic Devices)SeparationsSpectroscopySurface analysisPapers dealing with established methods need to offer a significantly improved original application of the method.