Discrimination of mycoplasma infection using machine learning models trained on autofluorescence signatures of host cells

Abstract

Cellular autofluorescence signatures are generated by fluorescent metabolites and structural cellular components and are considered to represent the physiological state of individual cells. Since optimal microscopic imaging of cellular autofluorescence signals is rapid, nondestructive, and minimally invasive, the extraction and classification of autofluorescence signatures can be a powerful tool for discriminating different physiological states. Here, we investigated whether changes in the cellular autofluorescence signature of host cells can be used to diagnose mycoplasma infection. Analysis of cellular autofluorescence signals from monkey cells infected with mycoplasma revealed attenuated autofluorescence at several wavelengths. Machine learning models trained on individual cellular autofluorescence signatures achieved more than 70% accuracy in binary classification tasks, demonstrating the utility of autofluorescence signatures in the discrimination of mycoplasma-infected cells. Finally, quantitative measurements of NADH, a major fluorescent metabolite, revealed a ∼20% reduction of cellular NADH in mycoplasma-infected cells that might underlie the attenuation, especially under 405 nm excitation.