Distinguishing Healthy and Carcinoma Cell Cultures Using Fluorescence Spectra Decomposition with a...

Area of Science:

• Biophysics
• Optical Spectroscopy
• Computational Biology

Background:

• Cellular fluorescence spectroscopy is crucial for distinguishing cell types.
• Traditional deconvolution algorithms do not account for light scatter, a significant factor in cell suspensions.
• Genetic-algorithm-spectra-decomposition software (GASpeD) offers an improved approach.

Purpose of the Study:

• To analyze steady-state fluorescence spectra of healthy and carcinoma fibroblast mouse cells.
• To evaluate the effectiveness of GASpeD in deconvoluting spectra, considering light scatter.
• To identify spectral differences indicative of cellular health or malignancy.

Main Methods:

• Analysis of steady-state fluorescence spectra from cell suspensions using GASpeD.
• Normalization, smoothing, and deconvolution of spectra into four peaks and background.
• Comparison of fluorescence intensities, particularly the free/bound NAD(P)H (AF/AB) ratio, between healthy and carcinoma cells at varying pH.

Main Results:

• Deconvoluted spectra showed characteristic peaks for lipopigments (LR), FAD, and free/bound NAD(P)H (AF/AB), matching published data.
• The AF/AB ratio was consistently higher in healthy cells compared to carcinoma cells at pH 7.
• The AF/AB ratio exhibited differential responses to pH changes in healthy versus carcinoma cells, with a notable decrease when carcinoma cells exceeded 13% in mixtures.

Conclusions:

• GASpeD effectively deconvolutes fluorescence spectra, accounting for light scatter.
• The AF/AB ratio serves as a potential biomarker for distinguishing healthy from carcinoma cells.
• This approach holds promise for developing cost-effective, user-friendly cancer biosensors using optical fibers.