A Novel Spectral Barcoding and Classification Approach for Complex Biological Samples Using Multiexcitation Raman Spectroscopy (MX-Raman).

We report the development and application of a novel spectral barcoding approach that exploits our multiexcitation (MX) Raman spectroscopy-based methodology for improved label-free detection and classification of complex biological samples. To develop our improved MX-Raman methodology, we utilized post-mortem brain tissue from several neurodegenerative diseases (NDDs) that have considerable clinical overlap. For improving our methodology we used three sources of spectral information arising from distinct physical phenomena to assess which was most important for NDD classification. Spectral measurements utilized combinations of data from multiple, distinct excitation laser wavelengths and polarization states to differentially probe molecular vibrations and autofluorescence signals. We demonstrate that the more informative MX-Raman (532 nm-785 nm) spectra are classified with 96.7% accuracy on average, compared to conventional single-excitation Raman spectroscopy that resulted in 78.5% accuracy (532 nm) or 85.6% accuracy (785 nm) using linear discriminant analysis (LDA) on 5 NDD classes. By combining information from distinct laser polarizations we observed a nonsignificant increase in classification accuracy without the need of a second laser (785 nm-785 nm polarized), whereas combining Raman spectra with autofluorescence signals did not increase classification accuracy. Finally, by filtering out spectral features that were redundant for classification or not descriptive of disease class, we engineered spectral barcodes consisting of a minimal subset of highly disease-specific MX-Raman features that improved the unsupervised and cross-validated clustering of MX-Raman spectra. The results demonstrate that increasing spectral information content using our optical MX-Raman methodology enables enhanced identification and distinction of complex biological samples but only when that information is independent and descriptive of class. The future translation of such technology to biofluids could support diagnosis and stratification of patients living with dementia and potentially other clinical conditions such as cancer and infectious disease.