Classification of Systemic Lupus Erythematosus Using Raman Spectroscopy of Blood and Automated Computational Detection Methods: A Novel Tool for Future Diagnostic Testing

Abstract

The aim of this study was to explore the proof of concept for using Raman spectroscopy as a diagnostic platform in the setting of systemic lupus erythematosus (SLE). We sought to identify unique Raman signatures in serum blood samples to successfully segregate SLE patients from healthy controls (HC). In addition, a retrospective audit was undertaken to assess the clinical utility of current testing platforms used to detect anti-double stranded DNA (dsDNA) antibodies (n = 600). We examined 234 Raman spectra to investigate key variances between SLE patients (n = 8) and HC (n = 4). Multi-variant analysis and classification model construction was achieved using principal component analysis (PCA), PCA-linear discriminant analysis and partial least squares-discriminant analysis (PLS-DA). We achieved the successful segregation of Raman spectra from SLE patients and healthy controls (p-value < 0.0001). Classification models built using PLS-DA demonstrated outstanding performance characteristics with 99% accuracy, 100% sensitivity and 99% specificity. Twelve statistically significant (p-value < 0.001) wavenumbers were identified as potential diagnostic spectral markers. Molecular assignments related to proteins and DNA demonstrated significant Raman intensity changes between SLE and HC groups. These wavenumbers may serve as future biomarkers and offer further insight into the pathogenesis of SLE. Our audit confirmed previously reported inconsistencies between two key methodologies used to detect anti-dsDNA, highlighting the need for improved laboratory testing for SLE. Raman spectroscopy has demonstrated powerful performance characteristics in this proof-of-concept study, setting the foundations for future translation into the clinical setting.

Innovative approach to soft tissue classification using fiber-optic Raman probe as a smart sensing tool

“…Encouraging advancements are observed in the utilization of intraoperative fiber point-probe Raman spectroscopy systems, offering rapid tissue characterization along with guiding surgical procedures through statistical models developed with machine learning techniques. [13][14][15] These results reinforce the capability of Raman spectroscopy as a real-time, in vivo tool for distinguishing between tissue types and identifying cancerous tissue in the operating room.…”

Vibrational Biospectroscopy in the Clinical Setting: Exploring the Impact of New Advances in the Field of Immunology

“…Raman spectroscopy has been applied in various felds such as forensic science [6] and pharmaceuticals [7]. In the feld of medicine, the technique has been applied to monitor diabetes [8], the detection of hyperbilirubinemia [9], the diagnosis of cancer [10,11], Alzheimer's disease [12], mental health conditions [13], and autoimmune disorders such as systemic lupus erythematosus (SLE) [14].…”

“…Raman spectroscopy and subsequent multivariate analysis demonstrated a reduced separation between the subgroups, further suggesting that current assays are not sufciently detecting SLE biomarkers for all patients. While this study was limited by sample size (n � 12), it set precedence for future work on the use of Raman for diagnosing patients with SLE and the possibility of using it in future clinical practice [14].…”