Abstract
Inborn errors of metabolism (IEM) are inherited conditions caused by genetic defects in enzymes or cofactors. These defects result in a specific metabolic fingerprint in patient body fluids, showing accumulation of substrate or lack of an end-product of the defective enzymatic step. Untargeted metabolomics has evolved as a high throughput methodology offering a comprehensive readout of this metabolic fingerprint. This makes it a promising tool for diagnostic screening of IEM patients. However, the size and complexity of metabolomics data have posed a challenge in translating this avalanche of information into knowledge, particularly for clinical application. We have previously established next-generation metabolic screening (NGMS) as a metabolomics-based diagnostic tool for analyzing plasma of individual IEM-suspected patients. To fully exploit the clinical potential of NGMS, we present a computational pipeline to streamline the analysis of untargeted metabolomics data. This pipeline allows for time-efficient and reproducible data analysis, compatible with ISO:15189 accredited clinical diagnostics. The pipeline implements a combination of tools embedded in a workflow environment for large-scale clinical metabolomics data analysis. The accompanying graphical user interface aids end-users from a diagnostic laboratory for efficient data interpretation and reporting. We also demonstrate the application of this pipeline with a case study and discuss future prospects.
Highlights
Licensee MDPI, Basel, Switzerland.Inborn errors of metabolism (IEMs) are genetically determined biochemical disorders that have severe clinical consequences, which mostly present at neonatal or childhood age, and milder presentations are known in adult patients
We introduce our automated bioinformatics pipeline for metabolomics data analysis, its software architecture, and the user interface that complements the next-generation metabolic screening (NGMS)
We describe our bioinformatics approach for embedding untargeted metabolomics in the clinical diagnostic process of our metabolic laboratory in an efficient and robust manner
Summary
We recently demonstrated the application of untargeted metabolomics for diagnostic screening for IEM, an approach we termed next-generation metabolic screening (NGMS) [10] This approach uses ultra-high-performance liquid chromatography quadrupole time-offlight mass spectrometry (UHPLC-QTOF-MS) for holistic metabolic profiling in the plasma of individual IEM-suspected patients. We introduce our automated bioinformatics pipeline for metabolomics data analysis, its software architecture, and the user interface that complements the NGMS analytical workflow for application in the diagnostic screening of IEMs. We demonstrate its application in a case study of an IEM diagnosed patient, shedding light on the complete process from sample measurement until interpretation, and how this workflow enables laboratory specialists to perform data processing and analysis in a highly efficient, reproducible, and traceable manner, adhering to ISO:15189 regulations. The focus of this article does not include details on the NGMS analytical approach, data processing algorithms and statistical methods in the context of our workflow; for details on such information, please see [10]
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