Direct Infusion Based Metabolomics Identifies Metabolic Disease in Patients' Dried Blood Spots and Plasma

Abstract

In metabolic diagnostics, there is an emerging need for a comprehensive test to acquire a complete view of metabolite status. Here, we describe a non-quantitative direct-infusion high-resolution mass spectrometry (DI-HRMS) based metabolomics method and evaluate the method for both dried blood spots (DBS) and plasma. 110 DBS of 42 patients harboring 23 different inborn errors of metabolism (IEM) and 86 plasma samples of 38 patients harboring 21 different IEM were analyzed using DI-HRMS. A peak calling pipeline developed in R programming language provided Z-scores for ~1875 mass peaks corresponding to ~3835 metabolite annotations (including isomers) per sample. Based on metabolite Z-scores, patients were assigned a 'most probable diagnosis' by an investigator blinded for the known diagnoses of the patients. Based on DBS sample analysis, 37/42 of the patients, corresponding to 22/23 IEM, could be correctly assigned a 'most probable diagnosis'. Plasma sample analysis, resulted in a correct 'most probable diagnosis' in 32/38 of the patients, corresponding to 19/21 IEM. The added clinical value of the method was illustrated by a case wherein DI-HRMS metabolomics aided interpretation of a variant of unknown significance (VUS) identified by whole-exome sequencing. In summary, non-quantitative DI-HRMS metabolomics in DBS and plasma is a very consistent, high-throughput and nonselective method for investigating the metabolome in genetic disease.

Keywords: DIMS; IEM; direct-infusion mass spectrometry; inborn errors of metabolism; metabolomics.

Figure 1

Flowchart of the non-quantitative direct-infusion high-resolution MS (DI-HRMS) method. DBS: dried blood spots, IEM: inborn error of metabolism, cs: control samples, ps: patient samples, DI-HRMS: direct infusion high resolution mass spectrometry, SD: standard deviation.

Figure 2

Representative examples of important biomarkers for the diagnosed IEM. For both dried blood spots (DBS) and plasma three acylcarnitine biomarkers, three amino acids and three other metabolites are demonstrated as representative examples of compounds with high Z-scores that contributed to the assigned most probable diagnosis. Each dot represents a unique sample. Black open circles represent both control samples and samples of other IEM patients, blue filled circles represent patients with that specific IEM. MCAD: medium-chain acyl-CoA dehydrogenase, GAMT: guanidinoacetate methyltransferase; MTHFR: methylenetetrahydrofolate reductase.

Figure 3

DI-HRMS metabolomics pathway analysis of the pathway of trimethyllysine hydroxylase. (A) Metabolic pathway of trimethyllysine hydroxylase, in italic the enzyme responsible for the metabolite conversion depicted by the arrow. (B) Z-scores of the five included dried blood spots (DBS) for each of the metabolites included in the pathway. Z-scores in red are significantly increased, Z-scores in blue are significantly decreased. (C) Z-scores of control samples, other patient samples and the samples of this patient for γ-butyrobetaine, the metabolite decreased in all patient samples. (D) Z-scores of control samples, other patient samples and the samples of this patient for the ratio of trimethyllysine over γ-butyrobetaine. De ratio is significantly increased in all patient samples and clearly separates this patient from control samples and samples of other patients, thereby supporting pathogenicity of the identified variant of uncertain significance (VUS) in the TMLHE gene.