Using Out-of-Batch Reference Populations to Improve Untargeted Metabolomics for Screening Inborn Errors of Metabolism.

Michiel Bongaerts,Serwet Demirdas,George Ruijter,Marcel Reinders,Margreet Wagenmakers,Ans Van Der Ploeg,Ramon Bonte,Henk Blom,Esmee Oussoren,Robert Hofstra,Edwin Jacobs

doi:10.3390/metabo11010008

Michiel Bongaerts, Serwet Demirdas + Show 9 more

Open Access

https://doi.org/10.3390/metabo11010008

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Abstract

Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e., technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method that uses 10 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age and sex as covariates fitted on reference samples that were obtained from all nine batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal, as well as in the detection of 195 known biomarkers across 49 IEM patient samples and performed at least similar to an approach utilizing 15 within-batch reference samples. Furthermore, our regression model indicates that 6.5–37% of the considered features showed significant age-dependent variations. Our comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch reference samples to establish clinically-relevant reference values for metabolite concentrations. These findings open the possibilities to use large scale out-of-batch reference samples in a clinical setting, increasing the throughput and detection accuracy.

Highlights

The screening of patients suspected for inborn errors of metabolism (IEM) is currently based on measuring panels of specific groups of metabolites, like amino acids or organic acids using a number of different tests, and techniques, such as ion-exchange chromatography, liquid chromatography mass spectrometry (LC-MS) and gas chromatography mass spectrometry (GC-MS)
All nine batches were measured on a single mass spectrometer (Thermo Scientific Q Exactive Plus), while three separate Kinetex F5 columns for ultra-high performance liquid chromatography (UHPLC)
We investigated the performance of several normalization methods on batch effect removal by evaluating multiple metrics that are based on quantitative measurements, the Quality Control (QC) samples and PCA analysis (Section 4.4.5)

Summary

Introduction

The screening of patients suspected for inborn errors of metabolism (IEM) is currently based on measuring panels of specific groups of metabolites, like amino acids or organic acids using a number of different tests, and techniques, such as ion-exchange chromatography, liquid chromatography mass spectrometry (LC-MS) and gas chromatography mass spectrometry (GC-MS) This targeted approach with several different tests is time consuming and limited in the number of metabolites being analyzed. The establishment of reference values is complicated, due to the semi-quantitative nature of the data, owing to several sources of variation, like injection volume, retention time, temperature, or ionization efficiency in the mass spectrometer that cannot be amended These variations are even larger between different measurement runs in which a batch of samples is being measured simultaneously, hampering the resemblance between different batches.

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