For decades, metabolic disorders have been diagnosed through enzymatic testing. However, once diagnosed, disease progression and potential treatment effects must be assessed on a regular basis, requiring frequent sampling of the concerned patients. Yet, issues often emerge while carrying out enzymatic assays, as these are affected by external factors, affecting the accuracy of these assays. This also applies to the follow-up of sphingolipidoses, a subgroup of the inherited lysosomal storage disorders where sphingolipid compounds accumulate due to a certain enzyme deficiency. Therefore, we plan to develop accurate bioanalytical methods that combine patient-centric regular sampling with accurate, high-throughput-amenable, fully automated analysis of an extensive panel of lysophingolipids, starting from dried blood microsamples. As a basis, this project will set out to optimize sensitive LC-MS/MS methods for quantitation of lysosphingolipid biomarkers in blood and plasma. Following the optimization of robust fully validated methodologies to determine an extensive lysosphingolipid panel in conventional dried blood spots and samples collected via volumetric
absorptive microsampling, automated microsample extraction methodologies will be set up that will be applied on healthy volunteer and patient samples. Hematocrit prediction methodologies will also be incorporated to cope with hematocrit-imposed issues known to impact reliable quantitation from dried blood microsamples.