Levenson Richard M
CRI, Woburn, Massachussets 01801, USA.
Cytometry A. 2006 Jul;69(7):592-600. doi: 10.1002/cyto.a.20292.
Cytomics involves the analysis of cellular morphology and molecular phenotypes, with reference to tissue architecture and to additional metadata. To this end, a variety of imaging and nonimaging technologies need to be integrated. Spectral imaging is proposed as a tool that can simplify and enrich the extraction of morphological and molecular information. Simple-to-use instrumentation is available that mounts on standard microscopes and can generate spectral image datasets with excellent spatial and spectral resolution; these can be exploited by sophisticated analysis tools.
This report focuses on brightfield microscopy-based approaches. Cytological and histological samples were stained using nonspecific standard stains (Giemsa; hematoxylin and eosin (H&E)) or immunohistochemical (IHC) techniques employing three chromogens plus a hematoxylin counterstain. The samples were imaged using the Nuance system, a commercially available, liquid-crystal tunable-filter-based multispectral imaging platform. The resulting data sets were analyzed using spectral unmixing algorithms and/or learn-by-example classification tools.
Spectral unmixing of Giemsa-stained guinea-pig blood films readily classified the major blood elements. Machine-learning classifiers were also successful at the same task, as well in distinguishing normal from malignant regions in a colon-cancer example, and in delineating regions of inflammation in an H&E-stained kidney sample. In an example of a multiplexed ICH sample, brown, red, and blue chromogens were isolated into separate images without crosstalk or interference from the (also blue) hematoxylin counterstain.
Cytomics requires both accurate architectural segmentation as well as multiplexed molecular imaging to associate molecular phenotypes with relevant cellular and tissue compartments. Multispectral imaging can assist in both these tasks, and conveys new utility to brightfield-based microscopy approaches.
细胞组学涉及细胞形态学和分子表型分析，并参考组织结构和其他元数据。为此，需要整合多种成像和非成像技术。光谱成像被认为是一种能够简化和丰富形态学及分子信息提取的工具。现有简单易用的仪器，可安装在标准显微镜上，能生成具有出色空间和光谱分辨率的光谱图像数据集；这些数据集可由先进的分析工具加以利用。
本报告聚焦于基于明场显微镜的方法。使用非特异性标准染色剂（吉姆萨染色；苏木精和伊红染色（H&E））或采用三种显色剂加苏木精复染的免疫组织化学（IHC）技术对细胞学和组织学样本进行染色。使用Nuance系统对样本进行成像，该系统是一种基于液晶可调滤光片的商用多光谱成像平台。使用光谱解混算法和/或示例学习分类工具对所得数据集进行分析。
吉姆萨染色的豚鼠血涂片的光谱解混能够轻松对主要血液成分进行分类。机器学习分类器在同一任务中也取得了成功，在结肠癌示例中区分正常区域和恶性区域以及在苏木精和伊红染色的肾脏样本中描绘炎症区域时同样如此。在一个多重免疫组织化学样本的示例中，棕色、红色和蓝色显色剂被分离成单独的图像，没有受到（同样为蓝色的）苏木精复染的串扰或干扰。
细胞组学既需要精确的结构分割，也需要多重分子成像，以便将分子表型与相关的细胞和组织区域相关联。多光谱成像可协助完成这两项任务，并为基于明场的显微镜方法带来新的效用。
