Phasor and neural network approaches for rapid fluorophore fraction analysis in temporal-spectral multiplexed data.

Abstract

Significance: The spatial and temporal distribution of fluorophore fractions in biological and environmental systems contains valuable information about the interactions and dynamics of these systems. To access this information, fluorophore fractions are commonly determined by means of their fluorescence emission spectrum (ES) or lifetime (LT). Combining both dimensions in temporal-spectral multiplexed data enables more accurate fraction determination while requiring advanced and fast analysis methods to handle the increased data complexity and size.

Aim: We introduce two methods, a phasor and a feedforward neural network (FNN) analysis, to extract fluorophore fractions from temporal-spectral data. These methods aim to handle the increased data complexity and size of temporal-spectral multiplexed data and therefore enable access to a more accurate and fast fraction determination.

Approach: The phasor analysis determines the fraction in each dimension and combines them, whereas the FNN is trained using artificially mixed data. Both methods are compared with the reference method using linear combination-based curve fitting (FIT). The methods are tested in a two-component scenario of exogenous fluorophores with different ES and LT and in a three-component scenario of endogenous fluorophores with similar ES and different LT.

Results: In this case, the phasor analysis showed the lowest absolute errors in the fraction determination (1.4% two-component, 4.7% three-component), outperforming the FNN (6.3%) and FIT (8.7%) analysis, which are both not able to recognize all fluorophores in the three-component scenario. The computational effort was reduced by roughly a factor of 6 (Phasor/FNN) compared with FIT.

Conclusions: Both methods demonstrate substantial advantages over common fitting, offering a faster and more accurate determination of fluorophore fractions. These advancements make temporal-spectral multiplexed data more accessible and practical, particularly for high-speed applications.