Temporal Signal Pattern Recognition in Mass Spectrometry: A Method for Rapid Identification and Accurate Quantification of Biomarkers for Inborn Errors of Metabolism with Quality Assurance

Abstract

Mass spectrometry (MS)-based metabolomic initiatives that use conventional separation techniques are limited by low sample throughput and complicated data processing that contribute to false discoveries. Herein, we introduce a new strategy for unambiguous identification and accurate quantification of biomarkers for inborn errors of metabolism (IEM) from dried blood spots (DBS) with quality assurance. A multiplexed separation platform based on multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS) was developed to provide comparable sample throughput to flow injection analysis-tandem MS (FIA-MS/MS) but with greater selectivity as required for confirmatory testing and discovery-based metabolite profiling of volume-restricted biospecimens. Mass spectral information is encoded temporally within a separation by serial injection of three or more sample pairs, each having a unique dilution pattern, alongside a quality control (QC) that serves as a reference in every run to facilitate between-sample comparisons and/or batch correction due to system drift. Optimization of whole blood extraction conditions on DBS filter paper cut-outs was first achieved to maximize recovery of a wide range of polar metabolites from DBS extracts. An interlaboratory comparison study was also conducted using a proficiency test and retrospective neonatal DBS that demonstrated good agreement between MSI-CE-MS and validated FIA-MS/MS methods within an accredited facility. Our work demonstrated accurate identification of various IEM based on reliable measurement of a panel of primary or secondary biomarkers above an upper cutoff concentration limit for presumptive screen-positive cases without stable isotope-labeled reagents. Additionally, nontargeted metabolite profiling by MSI-CE-MS with temporal signal pattern recognition revealed new biomarkers for early detection of galactosemia, such as N-galactated amino acids, that are a novel class of pathognomonic marker due to galactose stress in affected neonates.

High‐fat diet intake modulates maternal intestinal adaptations to pregnancy and results in placental hypoxia, as well as altered fetal gut barrier proteins and immune markers

“…from the same litter) male and female tissue extracts from control and HFD placentas in addition to a quality control (QC) consisting of a pooled placental extract sample ( n = 7) to assess system drift and overall technical variance (DiBattista et al . ). Overall, fifty‐one metabolites from placental extracts were consistently detected in the majority of specimens analysed (>75%) at the same time as having adequate technical precision as determined by a relative standard deviation (RSD) of <40% based on repeated analysis of the pooled quality control throughout the study (Macedo et al .…”

Recent advances in capillary electrophoresis‐mass spectrometry: Instrumentation, methodology and applications

“…For identification and accurate quantification of biomarkers for inborn errors of metabolism from dried blood spots, DiBattista et al. developed a multiplexed separation platform . Based on multi‐segment injection (MSI)‐CE‐MS, the method provides comparable sample throughput to flow injection analysis‐tandem MS but with increased selectivity.…”

High-fat diet intake modulates maternal intestinal adaptations to pregnancy, and results in placental hypoxia and impaired fetal gut development

“…Each experimental run consisted of 6 sequential injections of placental tissue extracts that were paired based on maternal diet and placental sex. In addition, a quality control (QC) consisting of a pooled placental extract sample (n = 7) was included in each experimental run to assess system drift and overall technical variance(DiBattista et al, 2017). Overall, fifty-one metabolites from placental extracts were consistently detected in majority of specimens analyzed (> 75%) while also having adequate technical precision (average RSD < 40%) based on repeated analysis of the pooled QC throughout the study(Macedo et al, 2017).…”