Multiple and simultaneous fluorophore detection using fluorescence spectrometry and partial least-squares regression with sample-specific confidence intervals

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 dye-labeled 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 approximately 91 to 100%.