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Multiple and Simultaneous Fluorophore Detection Using Fluorescence Spectrometry and Partial Least-Squares Regression with Sample-Specific Confidence Intervals

机译:使用荧光光谱法同时进行荧光团检测和具有特定样本置信区间的偏最小二乘回归

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摘要

Fluorescent labeling is widely used in biological and chemical analysis, and the drive for increased throughput is stretching multiplexing capabilities to the limit. The limiting factor in multiplexed analyses is the ability to subsequently deconvolute the signals. Consequently, alternative approaches for interpreting complex data sets are required to allow individual components to be identified. Here we have investigated the application of a novel approach to multiplexed analysis that does not rely on multivariate curve resolution to achieve signal deconvolution. The approach calculates a sample-specific confidence interval for a multivariate (partial least-squares regression (PLSR)) prediction, thereby enabling the estimation of the presence or absence of each fluorophore based on the total spectral signal. This approach could potentially be applied to any multiplexed measurement system and has the advantage over the current algorithm-based methods that the requirement for resolution of spectral peaks is not central to the method. Here, PLSR was used to obtain the concentrations for up to eight dyelabeled oligonucleotides at levels of (0.6-5.3) X 10~(-6) M. The sample-specific prediction intervals show good discrimination for the presence/absence of seven of the eight labeled oligonucleotides with efficiencies ranging from approx91 to 100percent.
机译:荧光标记已广泛用于生物和化学分析中,提高通量的动力是将多路复用功能扩展到极限。复用分析中的限制因素是随后对信号进行去卷积的能力。因此,需要用于解释复杂数据集的替代方法以允许识别各个组件。在这里,我们研究了一种不依赖于多元曲线分辨率即可实现信号去卷积的新颖方法在多元分析中的应用。该方法为多变量(偏最小二乘回归(PLSR))预测计算特定于样本的置信区间,从而可以根据总光谱信号估算每个荧光团的存在与否。该方法可以潜在地应用于任何多路复用测量系统,并且具有优于当前基于算法的方法的优势,即对光谱峰分辨率的要求不是该方法的中心。在这里,PLSR用于获得最多八个染料标记的寡核苷酸的浓度,浓度为(0.6-5.3)X 10〜(-6)M。样品特异性的预测间隔对7个寡核苷酸的存在/不存在有很好的区分八个标记的寡核苷酸,效率从大约91%到100%。

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