Hanneke A Haijes1, Maria van der Ham2, Johan Gerrits2, Peter M van Hasselt3, Hubertus C M T Prinsen2, Monique G M de Sain-van der Velden2, Nanda M Verhoeven-Duif2, Judith J M Jans4. 1. Section Metabolic Diagnostics, Department of Biomedical Genetics, Centre for Molecular Medicine, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The Netherlands; Section Metabolic Diseases, Department of Child Health, Wilhelmina Children's Hospital, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The Netherlands. 2. Section Metabolic Diagnostics, Department of Biomedical Genetics, Centre for Molecular Medicine, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The Netherlands. 3. Section Metabolic Diseases, Department of Child Health, Wilhelmina Children's Hospital, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The Netherlands. 4. Section Metabolic Diagnostics, Department of Biomedical Genetics, Centre for Molecular Medicine, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The Netherlands. Electronic address: J.J.M.Jans@umcutrecht.nl.
Abstract
BACKGROUND: For inborn errors of metabolism (IEM), metabolomics is performed for three main purposes: 1) development of next generation metabolic screening platforms, 2) identification of new biomarkers in predefined patient cohorts and 3) for identification of new IEM. To date, plasma, urine and dried blood spots are used. We anticipate that cerebrospinal fluid (CSF) holds additional - valuable - information, especially for IEM with neurological involvement. To expand metabolomics to CSF, we here tested whether direct-infusion high-resolution mass spectrometry (DI-HRMS) based non-quantitative metabolomics could correctly capture the biochemical profile of patients with an IEM in CSF. METHODS: Eleven patient samples, harboring eight different IEM, and thirty control samples were analyzed using DI-HRMS. First we assessed whether the biochemical profile of the control samples represented the expected profile in CSF. Next, each patient sample was assigned a 'most probable diagnosis' by an investigator blinded for the known diagnoses of the patients. RESULTS: the biochemical profile identified using DI-HRMS in CSF samples resembled the known profile, with - among others - the highest median intensities for mass peaks annotated with glucose, lactic acid, citric acid and glutamine. Subsequent analysis of patient CSF profiles resulted in correct 'most probable diagnoses' for all eleven patients, including non-ketotic hyperglycinaemia, propionic aciduria, purine nucleoside phosphorylase deficiency, argininosuccinic aciduria, tyrosinaemia type I, hyperphenylalaninemia and hypermethioninaemia. CONCLUSION: We here demonstrate that DI-HRMS based non-quantitative metabolomics accurately captures the biochemical profile of this set of patients in CSF, opening new ways for using metabolomics in CSF in the metabolic diagnostic laboratory.
BACKGROUND: For inborn errors of metabolism (IEM), metabolomics is performed for three main purposes: 1) development of next generation metabolic screening platforms, 2) identification of new biomarkers in predefined patient cohorts and 3) for identification of new IEM. To date, plasma, urine and dried blood spots are used. We anticipate that cerebrospinal fluid (CSF) holds additional - valuable - information, especially for IEM with neurological involvement. To expand metabolomics to CSF, we here tested whether direct-infusion high-resolution mass spectrometry (DI-HRMS) based non-quantitative metabolomics could correctly capture the biochemical profile of patients with an IEM in CSF. METHODS: Eleven patient samples, harboring eight different IEM, and thirty control samples were analyzed using DI-HRMS. First we assessed whether the biochemical profile of the control samples represented the expected profile in CSF. Next, each patient sample was assigned a 'most probable diagnosis' by an investigator blinded for the known diagnoses of the patients. RESULTS: the biochemical profile identified using DI-HRMS in CSF samples resembled the known profile, with - among others - the highest median intensities for mass peaks annotated with glucose, lactic acid, citric acid and glutamine. Subsequent analysis of patient CSF profiles resulted in correct 'most probable diagnoses' for all eleven patients, including non-ketotic hyperglycinaemia, propionic aciduria, purine nucleoside phosphorylase deficiency, argininosuccinic aciduria, tyrosinaemia type I, hyperphenylalaninemia and hypermethioninaemia. CONCLUSION: We here demonstrate that DI-HRMS based non-quantitative metabolomics accurately captures the biochemical profile of this set of patients in CSF, opening new ways for using metabolomics in CSF in the metabolic diagnostic laboratory.
Authors: Hanneke A Haijes; Eline A J Willemse; Johan Gerrits; Wiesje M van der Flier; Charlotte E Teunissen; Nanda M Verhoeven-Duif; Judith J M Jans Journal: Metabolites Date: 2019-10-18
Authors: Hanneke A Haijes; Maria van der Ham; Hubertus C M T Prinsen; Melissa H Broeks; Peter M van Hasselt; Monique G M de Sain-van der Velden; Nanda M Verhoeven-Duif; Judith J M Jans Journal: Int J Mol Sci Date: 2020-02-01 Impact factor: 5.923
Authors: Tessa M A Peters; Udo F H Engelke; Siebolt de Boer; Ed van der Heeft; Cynthia Pritsch; Purva Kulkarni; Ron A Wevers; Michèl A A P Willemsen; Marcel M Verbeek; Karlien L M Coene Journal: J Inherit Metab Dis Date: 2020-05-23 Impact factor: 4.982
Authors: Nicole H Tobin; Aisling Murphy; Fan Li; Sean S Brummel; Taha E Taha; Friday Saidi; Maxie Owor; Avy Violari; Dhayendre Moodley; Benjamin Chi; Kelli D Goodman; Brian Koos; Grace M Aldrovandi Journal: Metabolomics Date: 2021-06-23 Impact factor: 4.747