Analysis of tear fluid protein fractions using surface-enhanced Raman spectroscopy (SERS), followed by a high-performance liquid chromatography-light emitting diode-induced fluorescence (HPLC-LED-IF) method

The present study investigates the application of surface-enhanced Raman spectroscopy (SERS) in conjunction with high-performance liquid chromatography-light-emitting diode-induced fluorescence (HPLC-LED-IF) for analyzing tear proteins in the diagnosis of ophthalmological conditions. SERS is a highly sensitive technique capable of detecting low-concentration biomolecules in body fluids, enabling the monitoring of protein composition, presence, and relative variations associated with different health conditions. In this study, SERS substrates were fabricated by immobilizing gold nanostars on APTES-functionalized surfaces, leveraging the well-known electric field enhancement at sharp plasmonic edges. Tear protein fractions (PF1 and PF2) obtained from the HPLC system were drop-coated onto these substrates for SERS measurements. Tear fluid samples from control (C), moderate dry-eye (MDE), and primary open-angle glaucoma (POAG) subjects were studied, and the recorded SERS spectra revealed distinct Raman spectral patterns for each disease category. Multivariate analysis using principal component analysis (PCA) for PF1 and PF2 from the control and disease groups explained approximately 88% of the cumulative variance, indicating a clear separation between the groups. Further, classification using <i>K</i>-nearest neighbours (KNN) with leave-one-out cross-validation (LOOCV) approach for PF1 and PF2 showed 85% sensitivity, 90% specificity, and 88% accuracy, suggesting that this bimodal approach (HPLC-LED-IF + SERS) has the potential to improve the classification of disease states compared to HPLC-LED-IF alone.

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Publication Details

Journal
RSC Advances
Published
2025-01-01
DOI
https://doi.org/10.1039/d5ra05549k
Citations
1
Primary Topic
Ocular Surface and Contact Lens
Type
article
Field-Weighted Citation Impact
1.88

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article

Analysis of tear fluid protein fractions using surface-enhanced Raman spectroscopy (SERS), followed by a high-performance liquid chromatography-light emitting diode-induced fluorescence (HPLC-LED-IF) method

Sajan D. George, Santhosh Chidangil, V. B. Kartha, Sphurti S. Adigal et al.
1 citations
RSC Advances
Ocular Surface and Contact Lens
1.88
article

Analysis of tear fluid protein fractions using surface-enhanced Raman spectroscopy (SERS), followed by a high-performance liquid chromatography-light emitting diode-induced fluorescence (HPLC-LED-IF) method

Sajan D. George, Santhosh Chidangil, V. B. Kartha, Sphurti S. Adigal, Neetha I. R. Kuzhuppilly, Sulatha V. Bhandary
article en
1 citations

Abstract

The present study investigates the application of surface-enhanced Raman spectroscopy (SERS) in conjunction with high-performance liquid chromatography-light-emitting diode-induced fluorescence (HPLC-LED-IF) for analyzing tear proteins in the diagnosis of ophthalmological conditions. SERS is a highly sensitive technique capable of detecting low-concentration biomolecules in body fluids, enabling the monitoring of protein composition, presence, and relative variations associated with different health conditions. In this study, SERS substrates were fabricated by immobilizing gold nanostars on APTES-functionalized surfaces, leveraging the well-known electric field enhancement at sharp plasmonic edges. Tear protein fractions (PF1 and PF2) obtained from the HPLC system were drop-coated onto these substrates for SERS measurements. Tear fluid samples from control (C), moderate dry-eye (MDE), and primary open-angle glaucoma (POAG) subjects were studied, and the recorded SERS spectra revealed distinct Raman spectral patterns for each disease category. Multivariate analysis using principal component analysis (PCA) for PF1 and PF2 from the control and disease groups explained approximately 88% of the cumulative variance, indicating a clear separation between the groups. Further, classification using <i>K</i>-nearest neighbours (KNN) with leave-one-out cross-validation (LOOCV) approach for PF1 and PF2 showed 85% sensitivity, 90% specificity, and 88% accuracy, suggesting that this bimodal approach (HPLC-LED-IF + SERS) has the potential to improve the classification of disease states compared to HPLC-LED-IF alone.

RSC AdvancesVol. 15(50)
Manipal Academy of Higher Education (IN)
Kasturba Medical College, Manipal, Department of Biotechnology, Ministry of Science and Technology, India, Department of Science and Technology, Ministry of Science and Technology, India, Vision Group on Science and Technology
Openalex Percentile: Top 11%
Ocular Surface and Contact Lens
1.88
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