Polymer coated surfaces for the target-agnostic diagnosis of diseases - an artificial nose approach

用于与目标无关的疾病诊断的聚合物涂层表面——人工鼻方法

• 批准号: 2767061
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2767061
• 关键词: Polymer; coated; surfaces; target; agnostic

Surface-enhanced Raman spectroscopy (SERS) has emerged in recent years as a very promising technique for sensing applications due to its high sensitivity and multiplex capability.1 In the medical field, this has been applied as both an imaging and diagnosis tool, for diseases ranging from cancer to bacterial infections.1,2,3 Additionally, SERS is also compatible with 'target-agnostic' diagnosis of diseases: in contrast with techniques that rely on the detection of a specific molecules or targets, label-free methods do not require pre-specified analytes to be present in the sample, hence reducing the synthetic difficulties and costs associated with attaching Raman-probes to target-binding molecules (e.g. antibodies).4 Within Prof Molly Stevens' group, an artificial nose approach based on SERS was developed by covering gold surfaces with small molecules forming a self-assembling monolayer (SAM).4 When different analytes were layered on this surface, their affinity to the SAM determined their orientation with respect to the surface enhancing selectively different vibrational modes. The spectra could then be analysed and the data from 9 different SAMs combined using PCA (principal component analysis) to extract information regarding the chemical composition of complex samples. With the successes obtained from biological media, we are interested in improving this technique by expanding the chemical functionalities used to decorate the gold surface and provide further discrimination between samples from healthy and diseased patients suffering from illnesses that are hard to diagnose and rely on early diagnosis for low mortality. The synthetic work will be accompanied by computational techniques which will allow the analysis of the data by artificial intelligence and the discrimination of healthy and unhealthy populations. Bibliography:(1) Wang, J.; Liang, D.; Jin, Q.; Feng, J.; Tang, X. Bioorthogonal SERS Nanotags as a Precision Theranostic Platform for in Vivo SERS Imaging and Cancer Photothermal Therapy. Bioconjug. Chem. 2020, 31 (2), 182-193. https://doi.org/10.1021/acs.bioconjchem.0c00022.(2) Haroon, M.; Tahir, M.; Nawaz, H.; Majeed, M. I.; Al-Saadi, A. A. Surface-Enhanced Raman Scattering (SERS) Spectroscopy for Prostate Cancer Diagnosis: A Review. Photodiagnosis Photodyn. Ther. 2022, 37, 102690. https://doi.org/10.1016/j.pdpdt.2021.102690.(3) Tahir, M. A.; Dina, N. E.; Cheng, H.; Valev, V. K.; Zhang, L. Surface-Enhanced Raman Spectroscopy for Bioanalysis and Diagnosis. Nanoscale 2021, 13 (27), 11593-11634. https://doi.org/10.1039/D1NR00708D.(4) Kim, N.; Thomas, M. R.; Bergholt, M. S.; Pence, I. J.; Seong, H.; Charchar, P.; Todorova, N.; Nagelkerke, A.; Belessiotis-Richards, A.; Payne, D. J.; Gelmi, A.; Yarovsky, I.; Stevens, M. M. Surface Enhanced Raman Scattering Artificial Nose for High Dimensionality Fingerprinting. Nat. Commun. 2020, 11 (1), 207. https://doi.org/10.1038/s41467-019-13615-2.

表面增强拉曼光谱（Sers）由于其高灵敏度和多路复用能力，近年来已成为一种非常有前途的传感应用技术。1在医疗领域，它已被用作成像和诊断工具，用于从癌症到细菌感染的疾病。1，2，3此外，Sers还与疾病的“目标不可知”诊断兼容：与依赖于检测特定分子或靶的技术相比，无标记方法不需要样品中存在预先指定的分析物，因此减少了与将拉曼探针连接到靶结合分子相关的合成困难和成本（例如抗体）。4在Molly Stevens教授的小组中，开发了一种基于Sers的人工鼻方法，通过用小分子覆盖金表面形成自组装单层（SAM）。4当不同的分析物在该表面上分层时，它们对SAM的亲和力决定了它们相对于表面的取向，从而选择性地增强不同的振动模式。然后可以分析光谱，并使用PCA（主成分分析）将来自9种不同SAM的数据结合起来，以提取有关复杂样品化学成分的信息。 随着从生物介质中获得的成功，我们有兴趣通过扩展用于装饰金表面的化学功能来改进这项技术，并进一步区分健康和患病患者的样本，这些患者患有难以诊断的疾病，并依赖于早期诊断以降低死亡率。综合工作将伴随着计算技术，这将允许通过人工智能分析数据并区分健康和不健康的人群。 参考文献：（1）王俊; Liang，D.; Jin，Q.;冯，J.;唐，X.生物正交Sers纳米标签作为体内Sers成像和癌症光热治疗的精确治疗诊断平台。Bioconjug. 2020，31（2），182-193中所述。https://doi.org/10.1021/acs.bioconjchem.0c00022. (2)Haroon，M.; Tahir，M.; Nawaz，H.; Majeed，M.一、Al-Saadi，A. A.前列腺癌的表面增强拉曼散射光谱诊断光诊断Ther. 2022，37，102690。https://doi.org/10.1016/j.pdpdt.2021.102690. (3)塔希尔，M。一、Dina，N. E.的;郑，H.; Valev，V. K.;张丽用于生物分析和诊断的表面增强拉曼光谱。Nanoscale 2021，13（27），11593-11634. https://doi.org/10.1039/D1NR00708D. (4)金，N。;托马斯，M.的R.; Bergholt，M. S.的;彭斯岛J.道：Seong，H.; Charchar，P.; Todorova，N.; Nagelkerke，A.; Belessiotis-Richards，A.; Payne，D. J.道：Gelmi，A.;亚罗夫斯基岛;史蒂文斯，M。M.用于高分辨率指纹识别的表面增强拉曼散射人工鼻。国家通信2020，11（1），207. https://doi.org/10.1038/s41467-019-13615-2.