Discrimination between ricin and sulphur mustard toxicity in vitro using Raman spectroscopy.

Area of Science:

• Biomedical Engineering
• Spectroscopy
• Toxicology

Background:

• Raman spectroscopy enables non-invasive, label-free monitoring of living cells' biochemical properties over time.
• Existing methods for detecting toxic agents often lack speed, specificity, or require cell labeling.
• Distinguishing between different toxic agents and their effects on cells is crucial for rapid response and treatment.

Purpose of the Study:

• To evaluate the efficacy of a Raman spectroscopy cell-based biosensor for detecting and differentiating toxic agents.
• To identify specific biochemical changes induced by ricin and sulfur mustard in A549 lung cells.
• To develop a multivariate model for accurate classification and concentration prediction of toxic exposures.

Main Methods:

• Raman spectra of A549 lung cells exposed to ricin and sulfur mustard were acquired.
• A multivariate model combining Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) was applied to spectral data.
• Leave-one-out cross-validation was used to assess model performance for detection, identification, and concentration prediction.

Main Results:

• The PCA-LDA model achieved high sensitivity (98.9%) and specificity (87.7%) in detecting damaged cells.
• Accurate identification of toxic agents was achieved: 88.6% for sulfur mustard and 71.4% for ricin.
• Sulfur mustard concentrations were predicted with high accuracy (93-100%), while lower concentrations of both agents showed some prediction errors due to similar biochemical changes.

Conclusions:

• Biological Raman microspectroscopy combined with PCA-LDA analysis effectively distinguishes between viable and damaged cells.
• This technology can discriminate between different toxic challenges based on induced cellular biochemical and structural alterations.
• The study demonstrates the potential of Raman spectroscopy as a rapid, label-free biosensor for toxic agent detection and characterization.