Using Fourier transform IR spectroscopy to analyze biological materials.
Using Fourier transform IR spectroscopy to analyze biological materials.
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使用傅立叶变换红外光谱法分析生物材料。
DOI：
10.1038/nprot.2014.110
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发表时间：
2014-08
期刊：
影响因子：
14.8
通讯作者：
中科院分区：
文献类型：
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作者：
红外光谱是一种极好的生物分析方法。它能够以非扰动、无标记的方式提取生化信息和图像，以进行细胞功能的诊断和评估。虽然严格来说不是传统意义上的显微镜，但它允许通过各种计算算法传递光谱数据来构建组织或细胞结构的图像。由于此类图像是根据指纹光谱构建的，因此它们可以客观反映所分析样本的潜在健康状况。该领域的主要困难之一是就光谱预处理和数据分析达成共识。该手稿汇集了该领域的一些领导者作为合著者，以实现方法和程序的标准化，以将多阶段方法调整为可应用于各种细胞生物学问题或在临床环境中用于疾病筛查或诊断的方法。我们描述了从生物样品（例如固定细胞学和组织切片、活细胞或生物流体）收集红外光谱和图像的协议，该协议评估可用的仪器选项、适当的样品制备、不同的采样模式以及光谱数据采集方面的重要进展。采集后，数据处理由一系列步骤组成，包括质量控制、光谱预处理、特征提取以及监督或非监督类型的分类。典型的实验可以在数小时内完成并分析。给出了使用红外光谱与多变量数据处理相结合的示例结果。
IR spectroscopy is an excellent method for biological analyses. It enables the nonperturbative, label-free extraction of biochemical information and images toward diagnosis and the assessment of cell functionality. Although not strictly microscopy in the conventional sense, it allows the construction of images of tissue or cell architecture by the passing of spectral data through a variety of computational algorithms. Because such images are constructed from fingerprint spectra, the notion is that they can be an objective reflection of the underlying health status of the analyzed sample. One of the major difficulties in the field has been determining a consensus on spectral pre-processing and data analysis. This manuscript brings together as coauthors some of the leaders in this field to allow the standardization of methods and procedures for adapting a multistage approach to a methodology that can be applied to a variety of cell biological questions or used within a clinical setting for disease screening or diagnosis. We describe a protocol for collecting IR spectra and images from biological samples (e.g., fixed cytology and tissue sections, live cells or biofluids) that assesses the instrumental options available, appropriate sample preparation, different sampling modes as well as important advances in spectral data acquisition. After acquisition, data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type. A typical experiment can be completed and analyzed within hours. Example results are presented on the use of IR spectra combined with multivariate data processing.