Infrared Metasurface Augmented by Deep Learning for Monitoring Dynamics between All Major Classes of Biomolecules,Advanced Materials

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Infrared Metasurface Augmented by Deep Learning for Monitoring Dynamics between All Major Classes of Biomolecules
Advanced Materials ( IF 27.4 ) Pub Date : 2021-02-22 , DOI: 10.1002/adma.202006054
Aurelian John-Herpin ₁ , Deepthy Kavungal ₁ , Lea von Mücke ₁ , Hatice Altug ₁

Affiliation

Insights into the fascinating molecular world of biological processes are crucial for understanding diseases, developing diagnostics, and effective therapeutics. These processes are complex as they involve interactions between four major classes of biomolecules, i.e., proteins, nucleic acids, carbohydrates, and lipids, which makes it important to be able to discriminate between all these different biomolecular species. In this work, a deep learning‐augmented, chemically‐specific nanoplasmonic technique that enables such a feat in a label‐free manner to not disrupt native processes is presented. The method uses a highly sensitive multiresonant plasmonic metasurface in a microfluidic device, which enhances infrared absorption across a broadband mid‐IR spectrum and in water, despite its strongly overlapping absorption bands. The real‐time format of the optofluidic method enables the collection of a vast amount of spectrotemporal data, which allows the construction of a deep neural network to discriminate accurately between all major classes of biomolecules. The capabilities of the new method are demonstrated by monitoring of a multistep bioassay containing sucrose‐ and nucleotides‐loaded liposomes interacting with a small, lipid membrane‐perforating peptide. It is envisioned that the presented technology will impact the fields of biology, bioanalytics, and pharmacology from fundamental research and disease diagnostics to drug development.

中文翻译：

通过深度学习增强红外超表面，用于监测所有主要生物分子类别之间的动态

深入了解生物过程的迷人分子世界对于了解疾病、开发诊断方法和有效的治疗方法至关重要。这些过程很复杂，因为它们涉及四大类生物分子（即蛋白质、核酸、碳水化合物和脂质）之间的相互作用，这使得能够区分所有这些不同的生物分子种类非常重要。在这项工作中，提出了一种深度学习增强的化学特异性纳米等离子体技术，该技术能够以无标记的方式实现这一壮举，而不会破坏本机过程。该方法在微流体装置中使用高度灵敏的多共振等离子体超表面，尽管其吸收带强烈重叠，但它增强了宽带中红外光谱和水中的红外吸收。光流控方法的实时格式可以收集大量的光谱时间数据，从而可以构建深度神经网络来准确区分所有主要类别的生物分子。通过监测含有蔗糖和核苷酸的脂质体与小的脂膜穿孔肽相互作用的多步生物测定，证明了新方法的功能。预计所提出的技术将影响生物学、生物分析和药理学领域，从基础研究和疾病诊断到药物开发。

更新日期：2021-04-08

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