Untargeted Metabolomics-Based Screening Method for Inborn Errors of Metabolism using Semi-Automatic Sample Preparation with an UHPLC- Orbitrap-MS Platform

Abstract

Routine diagnostic screening of inborn errors of metabolism (IEM) is currently performed by different targeted analyses of known biomarkers. This approach is time-consuming, targets a limited number of biomarkers and will not identify new biomarkers. Untargeted metabolomics generates a global metabolic phenotype and has the potential to overcome these issues. We describe a novel, single platform, untargeted metabolomics method for screening IEM, combining semi-automatic sample preparation with pentafluorophenylpropyl phase (PFPP)-based UHPLC- Orbitrap-MS. We evaluated analytical performance and diagnostic capability of the method by analysing plasma samples of 260 controls and 53 patients with 33 distinct IEM. Analytical reproducibility was excellent, with peak area variation coefficients below 20% for the majority of the metabolites. We illustrate that PFPP-based chromatography enhances identification of isomeric compounds. Ranked z-score plots of metabolites annotated in IEM samples were reviewed by two laboratory specialists experienced in biochemical genetics, resulting in the correct diagnosis in 90% of cases. Thus, our untargeted metabolomics platform is robust and differentiates metabolite patterns of different IEMs from those of controls. We envision that the current approach to diagnose IEM, using numerous tests, will eventually be replaced by untargeted metabolomics methods, which also have the potential to discover novel biomarkers and assist in interpretation of genetic data.

Screening for inborn errors of metabolism using untargeted metabolomics and out-of-batch controls

“…Method 15in: best matching controls within batch: Z-scores were calculated by selecting 15 control 280 samples originating from the same batch as the patient based on age and sex as described in Bonte et al 281 (Bonte, et al, 2019). 282 283 Method 15out: best matching controls from other batches: Z-scores were calculated similarly as in 284 method 15in using explicitly 15 out-of-batch controls.…”

“…Spectrometry (HRAM LC-MS) can detect hundreds to thousands of metabolites within one test, and, as a 50 consequence, receives increasing interest to be used in IEM screening (Miller, et al, 2015) (Coene,et al,51 2018) (Körver-Keularts, et al, 2018) (Haijes, et al, 2019) (Bonte, et al, 2019). Moreover, untargeted 52 metabolomics can also reveal new biomarkers or increase our understanding of disease mechanism when 53 exploited in epidemiological studies (Glinton, et al, 2019).…”

“…Human plasma samples of 260 control samples and 53 IEM patients were measured over eight batches over 103 the period 10-12-2018 to 03-05-2019 (Bonte, et al, 2019) having in total 33 unique IEMs. For every patient 104 a technical triplicate was included.…”

“…Since the QC sample was a commercial 106 sample, the sample differed in concentration of several metabolites when compared to the (average) 107 concentrations of the human plasma samples analyzed in these datasets. Features were annotated as 108 described in Bonte et al (Bonte, et al, 2019). Note that within each batch about 30 normal controls have 109 been measured, which allows us to establish reference values based on within-batch controls, whereas the 110 controls being measured for the other (seven) batches can be used for out-of-batch strategies.…”

“…Previous pre-processing steps (alignment, peak picking etc.) were performed per batch using Progenesis QI 122 v2.4 (Newcastle-upon-Tyne, UK) (Bonte, et al, 2019). In-house software was developed to match features 123 from each batch to a reference batch which in this case was the fifth batch when sorting on chronologically 124 order.…”