Fluorescence and Photoacoustic Spectroscopy-Based Assessment of Mitochondrial Dysfunction in Oral Cancer Together with Machine Learning: A Pilot Study.
Chandavalli Ramappa RaghushakerJackson RodriguesSubramanya G NayakSatadru RayArun S UralaKapaettu SatyamoorthyKrishna Kishore MahatoPublished in: Analytical chemistry (2021)
The current study reports an integrated approach of machine learning and tryptophan fluorescence and photoacoustic spectral properties to assess the mitochondrial status under oral pathological conditions. The mitochondria in the study were isolated from oral cancer tissues and adjacent normal counterparts, and the corresponding fluorescence and photoacoustic spectra of tryptophan were recorded at 281 nm pulsed laser excitations. A set of features were selected from the pre-processed spectra and were used to classify the data using support vector machine (SVM) learning in the MATLAB platform. SVM analysis demonstrated clear differentiation between mitochondria isolated from normal and cancer tissues for fluorescence (sensitivity, 86.6%; specificity, 90%) and photoacoustic (sensitivity, 86.6%; specificity, 96.6%) measurements. Further investigation into the influence of change in protein conformation on the nature of tryptophan spectral properties was evaluated by 8-anilino-1-naphthalene sulfonic acid (ANS) fluorescence assay. The impact of protein structural changes on the mitochondrial functions was also estimated by mitochondrial membrane potential (MMP), reactive oxygen species (ROS), and cytochrome c oxidase (COX) assays, suggesting an altered mitochondrial function. The findings indicate that tryptophan fluorescence and photoacoustic spectral properties together with machine learning algorithms may delineate the mitochondrial functional status in vitro, indicating its translational potential.
Keyphrases
- machine learning
- single molecule
- reactive oxygen species
- energy transfer
- oxidative stress
- fluorescence imaging
- optical coherence tomography
- deep learning
- artificial intelligence
- big data
- high throughput
- gene expression
- cell death
- photodynamic therapy
- high resolution
- human health
- magnetic resonance
- electronic health record
- papillary thyroid
- risk assessment
- molecular dynamics simulations
- density functional theory
- single cell
- childhood cancer