Evaluation of chemotherapeutic retinoic acid effects on malignant cells using fluorescence spectroscopy with selective excitation wavelengths and machine learning

Native fluorescence spectra of retinoic acid (RA)-treated and untreated human breast cancer cells were measured using selective wavelengths of 300 nm and 340 nm for excitation. The spectral data of the two types of cells were analyzed using machine learning algorithms for linear unmixing and classification which yielded high accuracy. The results show that the concentrations of the native fluorophores such as tryptophan, NADH and flavins in the human malignant breast cells change when they are treated with RA. The study shows the dual-wavelength fluorescence spectroscopy aided by machine learning has potential clinical applications in drug development and chemotherapeutic studies.

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