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

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.

Prioritizing disease-causing metabolic genes by integrating metabolomics with whole exome sequencing data

“…The study was conducted in accordance with the Declaration of Helsinki. Z-scores were calculated using two different approaches: 1) metabolites which were annotated in at least 7 batches were merged, a Box-Cox transform was applied, normalized using Metchalizer (Bongaerts, et al, 2020) and the Z-scores were determined using a regression model with age and sex as covariates (Bongaerts, et al, 2020), 2) for metabolites which were annotated in less than 7 batches, the Z-scores were determined from 15 within-batch samples, where abundancies were first Box-Cox transformed and normalized using Probabilistic Quotient Normalization (PQN). When a metabolite was annotated in both positive- and negative ion mode, the Z-score of the ion mode with the largest median abundancy (over all samples) was taken.…”

Integration of metabolomics with genomics: Metabolic gene prioritization using metabolomics data and genomic variant (CADD) scores

“…Z-scores were calculated using two different approaches. Metabolites that were annotated in at least 7 batches were merged, a Box-Cox transform was applied and normalized using Metchalizer [16]. Z-scores were determined using a regression model with age and sex as covariates [16].…”

“…Metabolites that were annotated in at least 7 batches were merged, a Box-Cox transform was applied and normalized using Metchalizer [16]. Z-scores were determined using a regression model with age and sex as covariates [16]. For metabolites that were annotated in less than 7 batches, the Z-scores were determined from 15 within-batch samples, where abundancies were first normalized using Probabilistic Quotient Normalization [17] and Box-Cox transformed.…”

Application of metabolite set enrichment analysis on untargeted metabolomics data prioritises relevant pathways and detects novel biomarkers for inherited metabolic disorders

“…NGMS allows the simultaneous measurement of hundreds of metabolites, circumventing the need for targeted metabolic tests based on patient phenotype as a first‐tier screening for IMDs. Several studies have demonstrated the effectiveness of NGMS for screening IMDs in individual patients with a well‐described set of diagnostic biomarkers 2–7 . However, many patients still lack a definitive diagnosis when restricting the analysis to known biomarkers.…”

“…Several studies have demonstrated the effectiveness of NGMS for screening IMDs in individual patients with a well-described set of diagnostic biomarkers. [2][3][4][5][6][7] However, many patients still lack a definitive diagnosis when restricting the analysis to known biomarkers. Looking for ways to systematically analyse a larger set of metabolites present within the NGMS data is the next logical step.…”