Multiple and simultaneous fluorophore detection using fluorescence spectrometry and partial least-squares regression...

Area of Science:

• Analytical Chemistry
• Biotechnology
• Spectroscopy

Background:

• Multiplexed fluorescent analyses are crucial for high-throughput biological and chemical studies.
• Current methods struggle with signal deconvolution, limiting multiplexing capabilities.
• Advanced data interpretation is needed to identify individual components in complex spectral signals.

Purpose of the Study:

• To investigate a novel approach for signal deconvolution in multiplexed fluorescent analysis.
• To develop a method that does not rely on traditional multivariate curve resolution.
• To enable accurate identification of individual fluorophores within complex spectral data.

Main Methods:

• Application of partial least-squares regression (PLSR) for multivariate prediction.
• Calculation of sample-specific confidence intervals for PLSR predictions.
• Utilizing total spectral signals for fluorophore presence/absence estimation.

Main Results:

• Successfully obtained concentrations for up to eight dye-labeled oligonucleotides (0.6-5.3 x 10^-6 M).
• Demonstrated good discrimination for the presence/absence of seven out of eight labeled oligonucleotides.
• Achieved high efficiencies, ranging from approximately 91% to 100%, in identifying fluorophores.

Conclusions:

• The developed approach offers a robust alternative for signal deconvolution in multiplexed measurements.
• This method bypasses the need for spectral peak resolution, simplifying complex data analysis.
• The technique shows significant potential for application across various multiplexed analytical systems.