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

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.

Analysis of a read-through promoting compound in a severe mouse model of spinal muscular atrophy.

Spinal muscular atrophy (SMA) is the leading genetic cause of infantile death and caused by the loss of functional Survival Motor Neuron 1 (SMN1). The remaining copy gene, SMN2, is unable to rescue from disease because the primary gene product lacks the final coding exon, exon 7, due to an alternative splicing event. While SMNΔ7 is a rapidly degraded protein, exon 7 is not specifically required in a sequence-specific manner to confer increased functionality to this truncated protein. Based upon this molecular observation, aminoglycosides have been examined to artificially elongate the C-terminus of SMNΔ7 by "read-through" of the stop codon. An SMNΔ7 read-through event benefits intermediate mouse models of SMA. Here we demonstrate that delivery of a read-through inducing compound directly to the CNS can partially lessen the severity of a severe model of SMA (Smn(-/-); SMN2(+/+)), albeit not to the extent seen in the less severe model. This further demonstrates the utility of read-through inducing compounds in SMA.