Label-free discrimination and quantitative analysis of oxidative stress induced cytotoxicity and potential protection of antioxidants using Raman micro-spectroscopy and machine learning
| Autor: | Ankit Garg, Kevin R. Moon, Anhong Zhou, Jon Y. Takemoto, Sitaram Harihar, Xiaojun Qi, Cheng-Wei Tom Chang, Wei Zhang, Jake S. Rhodes |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Antioxidant
medicine.medical_treatment 02 engineering and technology Oxidative phosphorylation Resveratrol Spectrum Analysis Raman medicine.disease_cause Machine learning computer.software_genre 01 natural sciences Biochemistry Antioxidants Analytical Chemistry Machine Learning symbols.namesake chemistry.chemical_compound medicine Humans Environmental Chemistry Cytotoxicity Cell damage Spectroscopy Vehicle Emissions business.industry Chemistry 010401 analytical chemistry 021001 nanoscience & nanotechnology medicine.disease 0104 chemical sciences Oxidative Stress symbols Particulate Matter Artificial intelligence 0210 nano-technology business Raman spectroscopy Quantitative analysis (chemistry) computer Oxidative stress |
| Zdroj: | Analytica Chimica Acta. 1128:221-230 |
| ISSN: | 0003-2670 |
| DOI: | 10.1016/j.aca.2020.06.074 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect