多光谱多维复用数据:越多越好

R. Levenson, C. Hoyt, J. Mansfield, K. Gossage
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引用次数: 0

摘要

同时检测多种分子物种的能力正变得越来越重要。多光谱成像系统可用于捕获多路复用的分子信号,并可用于分析在明场模式下的显色染色载玻片和在荧光模式下用各种发光染料(从可见光到近红外范围)染色的样品。量子点与这种成像技术特别匹配,这对识别和消除干扰性自身荧光也非常有帮助。在现场准确测定染料光谱质量的能力也很有价值。多光谱成像已被证明可用于多色FISH,用于分辨具有重叠发射光谱的多种GFP,以及用于分辨红色/棕色双标记的组织病理学染色。光谱成像在临床病理学中的应用仍在探索中,需要与适当的软件工具相匹配。最近开发了适当约束的线性解混算法和新颖的自动化工具,以提供简单,准确的分析程序。传统的苏木精-伊红染色或巴氏染色病理切片可以有足够的光谱内容,以允许不同谱系的细胞分类或将正常细胞与肿瘤细胞分开。使用光谱“特征”和简单的分割算法可以成功地分析这些标本。丰富的数据集也鼓励使用更高级的分析技术。这些方法可以包括为遥感目的开创的一些方法,如光谱相似度制图、n维自动聚类算法、主成分分析以及其他更复杂的技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multispectral multidimensional multiplexed data: the more, the merrier
The ability to detect multiple molecular species at once is becoming increasingly important. Multispectral imaging systems can be used to capture multiplexed molecular signals, and can be applied to the analysis of chromogenically stained slides in brightfield mode and of samples stained with a variety of light-emitting dyes (from the visible to the NIR range) in fluorescence mode. Quantum dots make a particularly good match with this imaging technology, which is also extremely helpful for the identification and elimination of interfering autofluorescence. The ability to accurately determine the spectral qualities of dyes in-situ is also valuable. Multispectral imaging has proven to be useful for multicolor FISH, for resolving multiple species of GFP with overlapping emission spectra and for resolving red/brown double-labeled histopathology stains. The uses of spectral imaging in clinical pathology are still being explored and need to be matched to appropriate software tools. Appropriately constrained linear unmixing algorithms and novel automated tools have recently been developed to provide simple, accurate analysis procedures. Conventional hematoxylin-and-eosin-or Papanicolaou-stained pathology sections can have sufficient spectral content to allow the classification of cells of different lineage or to separate normal from neoplastic cells. Analysis of such specimens may succeed using spectral "signatures" and simple segmentation algorithms. The rich data sets also reward the use of more advanced analysis techniques. These can include a number of approaches pioneered for remote sensing purposes, such as spectral similarity mapping, automated clustering algorithms in n dimensions, principal component analysis, as well as other more sophisticated techniques.
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