Analytical validation of total mucin concentration assay using SEC MALLS dRI for diagnosing and monitoring mucoobstructive lung diseases
Year: 2025
Type: article
Abstract: Mucins play a pivotal role in the pathophysiology of mucoobstructive lung diseases. Accurate quantification of total mucin concentrations in clinical sputum samples is critical for developing objective biomarkers for diagnosis, prognosis, and therapeutic monitoring. By using sputum samples and mucin standards, the analytical performance of the measurements of total mucin concentration by Size Exclusion Chromatography coupled with Multi-Angle Laser Light Scattering and Differential Refractometer [SEC-(MALLS)-dRI] method was assessed using universal validation metrics, including precision, accuracy, recovery, parallelism, specificity, linearity, and sample stability. Possible sample contamination sources, such as saliva, blood, and DNA, were also evaluated. The method demonstrated excellent precision across low, medium, and high concentrations (CV% ≤ 2.6%) and high recovery (116%). It exhibited strong linearity over a broad dynamic range (~30-15,000 µg/mL) and stability for up to 12 months at - 20 °C in naïve samples and 4 °C in 4 M GuHCl. Measurement interference was negligible, up to 20% saliva, 2% blood, and 2% DNA. This study validates the SEC-(MALLS)-dRI method as a robust, reliable approach for quantifying total mucin concentrations in clinical sputum samples. The demonstrated analytical validity establishes its use as a biomarker platform for clinical and research applications, aiding in the diagnosis and management of hypersecretory/mucoobstructive lung diseases.
Source: Scientific Reports
Authors: Esin Ozkan, Stephanie Sue Livengood, Amina Ahmad Ford, Jade Kathryn Macdonald, Sophia Samir, Ian William Klevans, Mehmet Kesimer
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,690
Funder: National Institutes of Health
OpenAlex ID: W4409953190
