RSC Advances · 9 authors, 4 centres
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This study developed a low-cost HPLC system using a 278 nm LED for fluorescence detection to analyze tear fluid protein profiles. The system achieved high sensitivity (91.6%) and specificity (93.9%) in classifying dry eye versus control samples using artificial neural network analysis. This approach offers a minimally invasive, point-of-care diagnostic tool for dry eye syndrome.
**Background:** Dry eye syndrome is a multifactorial disease characterized by tear film instability and ocular surface inflammation. Current diagnostic methods (e.g., Schirmer test, questionnaires) often require multiple tests and lack sensitivity for early detection. Tear fluid contains proteins that change with disease, offering a potential biomarker source. This study aimed to develop a cost-effective HPLC system with UV LED-induced fluorescence detection for tear protein profiling and apply multivariate analysis for dry eye diagnosis.
**Methods:** Tear samples were collected from 33 control and 36 dry eye subjects using Schirmer strips. Proteins were extracted and analyzed using a reverse-phase HPLC system (Agilent 1260 Infinity II) with a ZORBAX 300SB-C8 column. A 278 nm LED (18 mW) excited fluorescence, detected at 340 nm via a monochromator-photomultiplier. Gradient elution used water with 0.1% TFA and acetonitrile with 0.1% TFA at 200 μL/min. Protein identification was performed via co-injection of lysozyme, lactoferrin, and human serum albumin. Data preprocessing included baseline correction, peak alignment, and vector normalization. Descriptive statistics, PCA, match/no-match test (using Mahalanobis distance and spectral residuals), and artificial neural network (ANN) with leave-one-out cross-validation were applied to classify control vs. dry eye samples.
**Key Results:** The system detected lactoferrin with a limit of detection of 0.015 μg/mL and a correlation uncertainty of 6.3%. Absorption and fluorescence spectra showed lower intensities in dry eye samples. The match/no-match test using a dry eye calibration set (M-distance acceptance 1.9) yielded 94.4% sensitivity, 87.8% specificity, and 90.1% accuracy for the protein retention time region 2285–2761 s. ANN binary classification with leave-one-out achieved 91.6% sensitivity, 93.9% specificity, and 92.7% accuracy (precision 94.2%, negative predictive value 91.1%). PCA score plots showed improved discrimination between groups when using the selected retention time region.
**Clinical Implications:** This HPLC-LED-IF system provides a minimally invasive, low-cost, and sensitive method for tear protein profiling. The high sensitivity and specificity demonstrate its potential as a point-of-care diagnostic tool for dry eye syndrome, enabling early detection and monitoring. The approach avoids the need for complex mass spectrometry and can be adapted for other ocular diseases. Further studies are needed to validate the method in larger, diverse populations and to explore its utility in disease progression and treatment response.