1] Centre for Materials Science, Division of Chemistry, University of Central Lancashire, Preston, UK. [2] Present address: WestCHEM, Department of Pure and Applied Chemistry, University of Strathclyde, Glasgow, UK.
1] Centre for Biophotonics, Lancaster Environment Centre, Lancaster University, Lancaster, UK. [2] School of Computing and Communications, Lancaster University, Lancaster, UK.
Nat Protoc. 2014 Aug;9(8):1771-91. doi: 10.1038/nprot.2014.110. Epub 2014 Jul 3.
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.
红外光谱学是一种出色的生物分析方法。它能够非侵入性、无需标记地提取生化信息和图像,用于诊断和评估细胞功能。虽然从传统意义上讲它不属于显微镜技术,但它可以通过多种计算算法传递光谱数据来构建组织或细胞结构的图像。由于这些图像是由指纹光谱构建的,因此可以认为它们可以客观反映分析样本的潜在健康状况。该领域的主要困难之一是确定在光谱预处理和数据分析方面达成共识。本文汇集了该领域的一些领导者作为共同作者,以实现方法的标准化和规范化,将多阶段方法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。我们描述了一种从生物样本(例如固定细胞学和组织切片、活细胞或生物流体)中收集红外光谱和图像的方案,评估了可用的仪器选项、适当的样本制备、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,数据处理包括一系列步骤,包括质量控制、光谱预处理、特征提取以及监督或无监督类型的分类。一个典型的实验可以在数小时内完成和分析。本文还展示了将红外光谱与多元数据分析相结合的应用示例结果。