Mapping the Blood Microsample Metabolome: A Multi-Platform Inter-Laboratory Study

Blood microsampling (BμS) has emerged as a promising approach for analyzing endogenous metabolites. Different BμS devices are currently available, and although they share similar concepts for sample collection, matrix variations may affect analytical outcomes. While some untargeted studies have been published, research comparing these devices to conventional matrices remains predominantly targeted; notably, there is a lack of studies specifically evaluating the qualitative annotations in untargeted workflows. This study aims to provide a comprehensive metabolic and lipidomic map of three BµS devices, namely Dried Blood Spots (Whatman), Volumetric Absorptive Microsampling (Mitra), and Capitainer cards, benchmarked against traditional plasma and whole blood.1

For this purpose, venous blood, plasma, and BµS samples from ten fasting volunteers were analyzed across five laboratories. Using a multi-platform approach (RP-LC-MS/MS, HILIC-LC-MS/MS, GC-MS, and CE-MS), laboratories analyzed the samples using their own in-house workflow and databases. Manually curated level 1 and 2 annotations were harmonized using RefMet and ChEBI identifiers to access the metabolic and pathway coverage of the different matrices.

A total of 350 metabolites were collectively annotated. High comparability between matrices was observed, with 252 metabolites (72%) detected across all five compared sample types. 20 metabolites were unique to BμS and absent in blood and plasma. Lipidomic analysis identified 37 distinct lipid subclasses, with phosphatidylcholines and sphingomyelins being most abundant. While profiles were largely similar across matrices, some compounds were device-specific, such as unique lipid species captured only by the Capitainer device.

Overall, the results demonstrate that BµS devices provide a highly comparable metabolic and lipidomic coverage to traditional blood samples. Furthermore, this study contributes to advancing the field by providing an initial qualitative reference (indicating presence or absence of metabolites/lipids) for the application of BµS in large-scale metabolic studies.