拉曼光谱
协议(科学)
样品(材料)
样品制备
计算机科学
生物系统
纳米技术
生物标本
光谱学
材料科学
分析化学(期刊)
化学
光学
物理
生物
色谱法
量子力学
医学
替代医学
病理
作者
Holly J. Butler,Lorna Ashton,Benjamin Bird,Gianfelice Cinque,Kelly Curtis,Jennifer Dorney,Karen A. Esmonde‐White,Nigel J. Fullwood,Benjamin Gardner,Pierre L. Martin‐Hirsch,Michael J. Walsh,Martin R. McAinsh,Nicholas Stone,Francis L. Martin
出处
期刊:Nature Protocols
[Springer Nature]
日期:2016-03-10
卷期号:11 (4): 664-687
被引量:937
标识
DOI:10.1038/nprot.2016.036
摘要
Raman microspectroscopy is useful for the analysis of biological samples, because chemical and structural information can be obtained without using labels. This protocol brings together practical guidelines from expert research groups. Raman spectroscopy can be used to measure the chemical composition of a sample, which can in turn be used to extract biological information. Many materials have characteristic Raman spectra, which means that Raman spectroscopy has proven to be an effective analytical approach in geology, semiconductor, materials and polymer science fields. The application of Raman spectroscopy and microscopy within biology is rapidly increasing because it can provide chemical and compositional information, but it does not typically suffer from interference from water molecules. Analysis does not conventionally require extensive sample preparation; biochemical and structural information can usually be obtained without labeling. In this protocol, we aim to standardize and bring together multiple experimental approaches from key leaders in the field for obtaining Raman spectra using a microspectrometer. As examples of the range of biological samples that can be analyzed, we provide instructions for acquiring Raman spectra, maps and images for fresh plant tissue, formalin-fixed and fresh frozen mammalian tissue, fixed cells and biofluids. We explore a robust approach for sample preparation, instrumentation, acquisition parameters and data processing. By using this approach, we expect that a typical Raman experiment can be performed by a nonspecialist user to generate high-quality data for biological materials analysis.
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