Development of a nanoscale, near-infrared spectroscopy imaging tool for in situ, rapid and label-free analysis of single extracellular vesicles

开发纳米级近红外光谱成像工具，用于单个细胞外囊泡的原位、快速、无标记分析

• 批准号: BB/X004449/1
• 负责人: Wayne Nishio Ayre
• 金额: $ 23.83万
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX004449%2F1
• 关键词: Development; nanoscale; near; infrared; spectroscopy

Cells release small spheres, known as extracellular vesicles (EVs), which are approximately 1000 times smaller than the width of a human hair (nanoscale). Recent research has shown that these EVs contain a cargo of signalling molecules that can act to either maintain health (e.g. blood vessel formation, immune response) or encourage disease progression (e.g. cancer, Parkinson's disease). As a result, the biological role and therapeutic potential of EVs has gained significant interest. Moreover, the discovery that EVs are present in circulating blood and elevated in certain diseases has also increased their potential use in diagnostics. A major limitation to the evolution of this field of research however has been the limited techniques available to easily analyse EVs. Many of the research techniques to study EVs require specialist equipment and training as well as significant time, sample processing and labelling, which may induce artefacts or bias results. Due to the low abundance of EVs and their contents, many existing techniques also pool thousands to millions of EVs for single analysis, assuming a homogenous population, when in fact studies have shown from a single cell type, several different sub-populations of EVs are present. To truly understand the biological role of EVs in health and disease and their therapeutic and diagnostic potential, a closer look at the heterogeneity of single EVs is needed in a manner that is both high-throughput and label-free whilst simultaneously maintaining EVs in their natural state. IR spectroscopy may offer a solution to this problem. It is based on the fact that molecules vibrate at specific frequencies due to the stretching and bending of the chemical bonds between atoms. When a chemical bond is exposed to an infrared light at the same frequency as it vibrates, the bond will absorb the energy. Although this technique has provided scientists with the ability to study chemical bonds in great detail, only recently has the technology advanced to the point where it can be applied to samples at a nanoscale. This cutting-edge approach, known as photo-induced force microscopy (PiFM), uses an extremely fine tip to measure the vibrational energy of molecules whilst they are excited by infrared light. Such an approach allows the chemical composition and topography of dry samples to be characterised at a nanoscale. However infrared light is highly prone to absorption in water and the movement of EVs due to their surface charge (Brownian motion) would make locating EVs in liquids using PiFM extremely challenging as well as prone to high levels of background noise. These technical challenges will be overcome in this project using two novel approaches. Firstly, IR light with a shorter wavelength, known as near-IR, will be used as it is less affected by water. Secondly, devices with unique surface features and different materials will be manufactured that can amplify the near-IR signal in water. These surfaces will also be chemically functionalised to capture the EVs for easy localisation and analysis with the PiFM. Analysing near-IR absorption is a complex task as this region of the IR spectrum consists of signals arising from combinations of chemical group vibrations as well as overtones (multiples of chemical vibrations). The project will therefore require a simplified model of EVs (empty vesicles composed of lipids, also known as liposomes) and advanced computational techniques (i.e. machine learning) to develop a database of near-IR chemical signals. Once the technology is optimised and refined, it will be validated and tested using cell derived EVs. This project will therefore develop a label-free, non-invasive, rapid technology to analyse the size and chemical composition of EVs in their natural state at a single vesicle level, providing information on the heterogeneity of EV populations and helping discover potential future therapeutic and diagnostic markers.

细胞释放被称为细胞外小泡(EVS)的小球体，它大约比人类头发(纳米级)的宽度小1000倍。最近的研究表明，这些电动汽车含有大量信号分子，可以起到维持健康(例如血管形成、免疫反应)或促进疾病进展(例如癌症、帕金森氏病)的作用。因此，EVS的生物学作用和治疗潜力引起了人们的极大兴趣。此外，发现EVS存在于循环血液中，并在某些疾病中升高，这也增加了它们在诊断中的潜在用途。然而，这一研究领域发展的一个主要限制是可用于轻松分析电动汽车的有限技术。研究电动汽车的许多研究技术需要专门的设备和培训，以及大量的时间、样品处理和标签，这可能会导致伪影或偏差结果。由于EV及其内容物的丰度较低，许多现有技术还汇集了数千至数百万个EV用于单一分析，假设种群相同，而事实上研究表明，从单个细胞类型来看，存在几个不同的EV亚群。为了真正了解电动汽车在健康和疾病中的生物学作用及其治疗和诊断潜力，需要以高通量和无标签的方式更仔细地研究单一电动汽车的异质性，同时保持电动汽车的自然状态。红外光谱分析可能会为这个问题提供一个解决方案。它是基于这样一个事实，即由于原子之间化学键的伸展和弯曲，分子以特定的频率振动。当化学键暴露在与其振动频率相同的红外光中时，该键将吸收能量。尽管这项技术为科学家提供了非常详细地研究化学键的能力，但直到最近，这项技术才发展到可以应用于纳米级样品的程度。这种尖端的方法被称为光感应力显微镜(PiFM)，它使用一个极其精细的尖端来测量分子在红外线激发时的振动能量。这种方法使得干燥样品的化学成分和形貌可以在纳米尺度上进行表征。然而，红外光在水中很容易被吸收，而电动汽车由于其表面电荷(布朗运动)而移动，这将使使用PIFM定位液体中的电动汽车具有极大的挑战性，并且容易受到高水平的背景噪声。该项目将使用两种新的方法克服这些技术挑战。首先，将使用波长较短的红外光，称为近红外，因为它受水的影响较小。其次，将制造具有独特表面特征和不同材料的器件，可以放大水中的近红外信号。这些表面还将进行化学功能化处理，以捕获电动汽车，以便使用PIFM进行轻松的本地化和分析。分析近红外吸收是一项复杂的任务，因为红外光谱的这一区域由化学基团振动和泛音(化学振动的倍数)组合产生的信号组成。因此，该项目将需要EVS(由脂类组成的空泡，也称为脂质体)的简化模型和先进的计算技术(即机器学习)来开发近红外化学信号数据库。一旦该技术得到优化和改进，将使用电池衍生电动汽车进行验证和测试。因此，该项目将开发一种无标签、非侵入性的快速技术，在单个囊泡水平上分析电动汽车在自然状态下的大小和化学成分，提供有关电动汽车种群异质性的信息，并帮助发现潜在的未来治疗和诊断标记。