研究目的
To highlight the recent advances of Raman mapping and provide an overview on its emerging applications, which range from single cell and tissue imaging to medical diagnosis, including cancer detection.
研究成果
Raman mapping is a powerful technique for label‐free, noninvasive investigations of tissues, cells, and microorganisms. It has been successfully used for investigations at cellular and subcellular level, including identification of nucleus, nucleoli, mitochondria, and lipid‐rich structures. The technique has a great potential for becoming a leading method in a wide range of biomedical applications, owing to its high chemical specificity, good resolution, and noninvasive nature.
研究不足
The major drawback of Raman technique is the low efficiency of the inelastic scattering process, which limits its application in the medical field. Different strategies have been developed to overcome this difficulty, including using nonlinear imaging modes and multimodal integration of Raman with other techniques.
1：Experimental Design and Method Selection:
Raman mapping is used as a noninvasive, label‐free technique for imaging biological and biomedical samples. The technique involves scanning the sample area with a laser spot to acquire Raman spectra at every set point, followed by chemometric analysis to generate false color maps.
2：Sample Selection and Data Sources:
Biological samples including cells, tissues, and biofluids are used. The selection criteria focus on samples that can provide chemical and spatial information relevant to biomedical research.
3：List of Experimental Equipment and Materials:
Raman spectrometers with visible and near-infrared lasers, CCD detectors, and specific substrates like calcium and magnesium fluoride (CaF2 and MgF2) and quartz for sample preparation.
4：Experimental Procedures and Operational Workflow:
The sample is scanned with a laser spot to acquire Raman spectra pixel by pixel. The spectra are then analyzed using chemometric methods to generate images.
5：Data Analysis Methods:
Multivariate methods such as principal component analysis (PCA), self‐modeling curve resolution (SMCR), and hierarchical cluster analysis (HCA) are used to analyze the Raman spectra and generate images.
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