Ida Havikbotn,a, Barbora Pisklakova,b, Sander J. T. Guttorm,b, Steven R. H. Wilson,a and Katja B. P.
Elgstøen,b
a University of Oslo, Dept. of Chemistry
b Oslo University Hospital, Dept. of Medical Biochemistry, Section for Metabolomics and Lipidomics
Email: idahav@uio.no
Blood microsampling (BμS) holds great promise for the future of decentralized and home-based clinical sampling [1] (Figure 1). While dried blood spots (DBS) have traditionally been the preferred BμS worldwide, the emergence of quantitative dried blood spot (qDBS) devices may offer improved sampling and diagnostic accuracy [2]. Here, we compared the metabolome and lipidome in DBS and qDBS cards to assess whether qDBS offers analytical or practical advantages over traditional DBS in clinical metabolomics and lipidomics. Whole blood was collected onto DBS and qDBS cards, and samples were analyzed using global LC-ESI-MS workflows, optimized to maximize coverage of as many metabolites and lipids as possible [3,4]. Both matrices yielded similar total number of detected features after background exclusion. Nevertheless, principal component analysis (PCA) clearly separated DBS and qDBS, indicating systematic differences in their matrices. Volcano plots revealed specific compounds that differed significantly between the two matrices. These findings indicate that while DBS and qDBS yield comparable overall metabolomic and lipidomic coverage, they differ in the relative extraction of specific compounds. Ongoing studies will examine how typical sampling errors ((1) milking of finger to increase blood flow (2) lamination of blood drops (3) touching the filter paper (4) not wiping away the first drop of blood prior to sampling)) influence the metabolome and lipidome in both formats. Ultimately, these insights will help develop strategies for robust home sampling in the future.
Figure 1: From hospital to home sampling: Traditional venipuncture requires in-clinic collection, while home microsampling allows self-collection. Figure made using Biorender software.
References
[1] M. Thaitumu, et al. Analytical Science Advances 6 (2025) e70044.
[2] S. Velghe, et al. American Chemical Society Journal Analytical Chemistry 90 (2018) 12893–12899.
[3] S.J.T. Guttorm, et al. Analytica Chimica Acta 1371 (2025) 344426.
[4] H.B. Skogvold, et al. Clinical Metabolomics: Methods and Protocols, Springer US, New York, NY, 2025: pp. 23–39.