Label-free discrimination and quantitative analysis of oxidative stress induced cytotoxicity and potential protection of antioxidants using Raman micro-spectroscopy and machine learning

• Published in: Analytica chimica acta Vol. 1128; pp. 221 - 230
• Main Authors: Zhang, Wei, Rhodes, Jake S., Garg, Ankit, Takemoto, Jon Y., Qi, Xiaojun, Harihar, Sitaram, Tom Chang, Cheng-Wei, Moon, Kevin R., Zhou, Anhong
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.09.2020
• Subjects: Antioxidant; Antioxidants - pharmacology; Humans; Machine Learning; Mutual information; Oxidative Stress; Particulate Matter; PHATE; Raman spectroscopy; Spectrum Analysis, Raman; Vehicle Emissions; PHATE; Raman spectroscopy; Antioxidant; Mutual information; Machine learning
• ISSN: 0003-2670; 1873-4324
• DOI: 10.1016/j.aca.2020.06.074

Diesel exhaust particles (DEPs) are major constituents of air pollution and associated with numerous oxidative stress-induced human diseases. In vitro toxicity studies are useful for developing a better understanding of species-specific in vivo conditions. Conventional in vitro assessments based on oxidative biomarkers are destructive and inefficient. In this study, Raman spectroscopy, as a non-invasive imaging tool, was used to capture the molecular fingerprints of overall cellular component responses (nucleic acid, lipids, proteins, carbohydrates) to DEP damage and antioxidant protection. We apply a novel data visualization algorithm called PHATE, which preserves both global and local structure, to display the progression of cell damage over DEP exposure time. Meanwhile, a mutual information (MI) estimator was used to identify the most informative Raman peaks associated with cytotoxicity. A health index was defined to quantitatively assess the protective effects of two antioxidants (resveratrol and mesobiliverdin IXα) against DEP induced cytotoxicity. In addition, a number of machine learning classifiers were applied to successfully discriminate different treatment groups with high accuracy. Correlations between Raman spectra and immunomodulatory cytokine and chemokine levels were evaluated. In conclusion, the combination of label-free, non-disruptive Raman micro-spectroscopy and machine learning analysis is demonstrated as a useful tool in quantitative analysis of oxidative stress induced cytotoxicity and for effectively assessing various antioxidant treatments, suggesting that this framework can serve as a high throughput platform for screening various potential antioxidants based on their effectiveness at battling the effects of air pollution on human health. [Display omitted] •Apply new algorithms (PHATE and MI) to visualize the Raman spectral data.•Raman spectroscopy was utilized to monitor cellular responses to oxidative stress.•The health index was proposed to quantitatively assess antioxidants protection.•A number of machine learning algorithms were applied to analyze Raman spectral data.•Correlation between Raman spectra and cytokine level was analyzed.