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Updated: Jan 17, 2026

Microsampling in Targeted Mass Spectrometry-Based Protein Analysis of Low-Abundance Proteins
Published on: January 13, 2023
Comparative Evaluation of Smart Sampling for hCG Determination: A New Potential Direction in Protein Biomarker
Ago Mrsa1, Marijana Matijevic1, Yvette Dehnes1,2
1Department of Pharmacy, University of Oslo, Oslo, Norway.
None:
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 R2 > 0.91) between the actual sample concentrations and the results obtained from all three methods. Immunocapture with magnetic beads showed the strongest linear correlation (R2 = 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.

