Automated capillary electrophoresis analyses of dried blood samples after patient-centric volumetric absorptive microsampling

An automated method for the analysis of dried blood samples collected by volumetric absorptive microsampling (VAMS) is presented for the first time. A single commercial capillary electrophoresis (CE) instrument (with no need for hardware or software modifications) was employed for the expeditious analyses of the dried material using several integral and novel features. These included the use of external pressure for sub-minute sample preparation from Mitra (R) VAMS polymeric tips, the use of the sampling tips as inherent micro-agitators for instant eluate homogenization, preparation/sampling at the CE outlet for more convenient capillary length adjustment, and short-end injection for sub-minute CE separation and quantification of lactate as a model analyte. The operational parameters for the sample preparation and analysis were comprehensively evaluated resulting in a baseline separation of lactate from all endogenous matrix compounds. The optimized method demonstrated excellent intra- and inter-day repeatability of peak areas (RSD values <= 7.2 %), linearity (R2 = 0.9990) over the clinically relevant concentration range (0.15 - 20 mM), the limits of detection and quantification at 0.03 and 0.1 mM, respectively, and a sample throughput >= 16 samples/hour. No ageing was observed for VAMS devices stored at ambient conditions for up to 14 days. Comparative analysis with a portable lactate analyser revealed slightly elevated concentrations in dried vs. liquid capillary blood due to the glycolysis of glucose during drying, nevertheless, lactate concentrations in Mitra (R) devices correlated well with those in blood plasma, which is typically used in clinical practice. The present study offers a robust, accurate, and environmentally benign concept for the automated analyses of VAMS-collected blood samples with its potential application in patient-centric sampling, clinical diagnostics and research, and personalized healthcare.