Department of Chemistry, Humboldt-Universität zu Berlin, Brook-Taylor-Str. 2, 12489 Berlin, Germany.
Hamburg School of Food Science, Department of Chemistry, Universität Hamburg, Grindelallee 117, 20146 Hamburg, Germany.
Chem Soc Rev. 2024 Jul 29;53(15):7641-7656. doi: 10.1039/d4cs00460d.
Surface enhanced Raman scattering (SERS) spectra of biomaterials such as cells or tissues can be used to obtain biochemical information from nanoscopic volumes in these heterogeneous samples. This tutorial review discusses the factors that determine the outcome of a SERS experiment in complex bioorganic samples. They are related to the SERS process itself, the possibility to selectively probe certain regions or constituents of a sample, and the retrieval of the vibrational information in order to identify molecules and their interaction. After introducing basic aspects of SERS experiments in the context of biocompatible environments, spectroscopy in typical microscopic settings is exemplified, including the possibilities to combine SERS with other linear and non-linear microscopic tools, and to exploit approaches that improve lateral and temporal resolution. In particular the great variation of data in a SERS experiment calls for robust data analysis tools. Approaches will be introduced that have been originally developed in the field of bioinformatics for the application to omics data and that show specific potential in the analysis of SERS data. They include the use of simulated data and machine learning tools that can yield chemical information beyond achieving spectral classification.
生物材料(如细胞或组织)的表面增强拉曼散射(SERS)光谱可用于从这些异质样品的纳米体积中获取生物化学信息。本教程综述讨论了决定复杂生物有机样品中 SERS 实验结果的因素。这些因素与 SERS 过程本身、选择性探测样品中某些区域或成分的可能性以及获取振动信息以识别分子及其相互作用有关。在介绍了生物相容环境中 SERS 实验的基本方面之后,本文举例说明了典型的微观环境中的光谱学,包括将 SERS 与其他线性和非线性微观工具结合的可能性,以及利用提高横向和时间分辨率的方法。特别是 SERS 实验中数据的巨大变化需要稳健的数据分析工具。本文将介绍最初在生物信息学领域为组学数据应用而开发的方法,并展示在 SERS 数据分析中具有特定潜力的方法。其中包括使用模拟数据和机器学习工具,这些工具除了实现光谱分类之外,还可以提供化学信息。