This study presents the first comparison of a novel smart sampling method with conventional immunocapture using magnetic beads and ECLIA for hCG analysis from serum samples. Smart sampling demonstrates strong correlation and agreement with both methods, showcasing its potential as a convenient, efficient alternative in protein biomarker analysis.
ABSTRACT
Since the early 20th century, sampling biological matrices like blood on paper (dried blood spots [DBS]) has been vital in clinical analysis. While DBS microsampling is convenient for small molecules, extensive sample preparation can make LC-MS protein analysis impractical because of the time-consuming steps, especially for low-abundance proteins. Smart sampling, introduced in 2018, simplifies this by integrating sample preparation directly on the sampler. The work presented in this paper aims to compare a newly validated smart sampling method with two other methods: an in-house method based on immunocapture on magnetic beads and a commercial method that uses electrochemiluminescence immunoassay (ECLIA). The performance of the three hCG methods was compared using 21 single-blind serum samples. Linear regression analysis revealed strong correlations (all R
2 > 0.91) between the actual sample concentrations and the results obtained from all three methods. Immunocapture with magnetic beads showed the strongest linear correlation (R
2 = 0.974). To assess agreement between the methods, Bland–Altman analysis was conducted. The comparison between smart sampling and magnetic beads showed an average bias of −5.2, with no significant trend in variation across the sample concentration range of 0.5–75 ng/mL. The smart sampling and ECLIA comparison revealed a bias of 0.4 ± 4 ng/mL, indicating even better agreement and consistent results. This paper presents the first-ever comparison of a smart sampling method with existing methods. The results highlight smart sampling as a promising new approach for bioanalysis and boost the technique as a viable alternative in protein biomarker analysis from complex matrices using LC-MS.
