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

Bongaerts, M.; Bonte, R.; Demirdas, S.; Jacobs, E.; Oussoren, E.; van der Ploeg, A.; Wagenmakers, M.; Hofstra, R.; Reinders, M.; Blom, H.; Ruijter, G.

doi:10.1101/2020.04.14.040469

bioRxiv · 10.1101/2020.04.14.040469

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

Bongaerts, M.·Bonte, R.·Demirdas, S.·Jacobs, E.·Oussoren, E.·van der Ploeg, A.·Wagenmakers, M.·Hofstra, R.·Reinders, M.·Blom, H.·Ruijter, G.

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Abstract

MotivationUntargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). In order to judge if metabolite levels are abnormal, analysis of a large number of reference samples is crucial to correct for variations in metabolite concentrations resulting from factors such as diet, age and gender. However, a large number of controls requires the use of out-of-batch controls, 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. Methods & resultsBased on six metrics, we compared existing normalization methods on their ability to reduce batch effects from eight independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method which uses 17 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 control samples obtained from all eight batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal as well as in the detection of 178 known biomarkers across 45 IEM patient samples and performed at least similar to an approach using 15 within-batch controls. Furthermore, our regression model indicates that 10-24% of the considered features showed significant age-dependent variations. ConclusionsOur comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch controls to establish clinically-relevant reference values for metabolite concentrations. These findings opens possibilities to use large scale out-of-batch control samples in a clinical setting, increasing throughput and detection accuracy. AvailabilityMetchalizer is available at https://github.com/mbongaerts/Metchalizer/

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Bongaerts, M., Bonte, R., Demirdas, S., Jacobs, E., Oussoren, E., van der Ploeg, A., Wagenmakers, M., Hofstra, R., Reinders, M., Blom, H., Ruijter, G.. 2020-04-15. Screening for inborn errors of metabolism using untargeted metabolomics and out-of-batch controls. https://doi.org/10.1101/2020.04.14.040469

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