关键词: 2D-COS Raman spectroscopy augmented spectra biochemical analyses biophysical analyses correlated changes correlation spectroscopy k-means clustering mammalian cells multisource correlations perturbation domain decomposition two-dimensional

Mesh : Glucagon Humans Insulin Spectrum Analysis, Raman

来  源:   DOI:10.1177/0003702820979331   PDF(Sci-hub)

Abstract:
Here, we present an augmented form of two-dimensional correlation spectroscopy, that integrates in a single format data from spectroscopic and multiple non-spectroscopic sources for analysis. The integration is affected by augmenting every spectrum in a hyperspectral data set with relevant non-spectroscopic data to permit two-dimensional correlation analysis(2D-COS) of the ensemble of augmented spectra. A k-means clustering is then applied to the results of the perturbation domain decomposition to determine which Raman peaks cluster with any of the non-spectroscopic data. We introduce and explain the method with the aid of synthetic spectra and synthetic non-spectroscopic data. We then demonstrate this approach with data using Raman spectra from human embryonic stem cell aggregates undergoing directed differentiation toward pancreatic endocrine cells and parallel bioassays of hormone mRNA expression and C-peptide levels in spent medium. These pancreatic endocrine cells generally contain insulin or glucagon. Insulin has disulfide bonds that produce Raman scattering near 513 cm-1, but no tryptophan. For insulin-positive cells, we found that the application of multisource correlation analysis revealed a high correlation between insulin mRNA and Raman scattering in the disulfide region. In contrast, glucagon has no disulfide bonds but does contain tryptophan. For glucagon-positive cells, we also observed a high correlation between glucagon mRNA and tryptophan Raman scattering (∼757 cm-1). We conclude with a discussion of methods to enhance spectral resolution and its effects on the performance of multisource correlation analysis.
摘要:
这里,我们提出了二维相关光谱学的增强形式,将来自光谱和多个非光谱源的单一格式数据集成在一起进行分析。通过用相关的非光谱数据增强高光谱数据集中的每个光谱来影响积分,以允许对增强光谱的集合进行二维相关分析(2D-COS)。然后将k均值聚类应用于扰动域分解的结果,以确定哪个拉曼峰与任何非光谱数据聚类。我们借助合成光谱和合成非光谱数据来介绍和解释该方法。然后,我们使用来自经历向胰腺内分泌细胞定向分化的人类胚胎干细胞聚集体的拉曼光谱和在废培养基中激素mRNA表达和C肽水平的平行生物测定来证明这种方法。这些胰腺内分泌细胞通常含有胰岛素或胰高血糖素。胰岛素具有二硫键,可在513cm-1附近产生拉曼散射,但没有色氨酸。对于胰岛素阳性细胞,我们发现多源相关分析的应用揭示了胰岛素mRNA与二硫化物区拉曼散射之间的高度相关性。相比之下,胰高血糖素没有二硫键,但含有色氨酸。对于胰高血糖素阳性细胞,我们还观察到胰高血糖素mRNA与色氨酸拉曼散射之间的高度相关性(〜757cm-1)。最后,我们讨论了增强光谱分辨率的方法及其对多源相关分析性能的影响。
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