Targeted ultra performance liquid chromatography tandem mass spectrometry procedures for the diagnosis of inborn errors of metabolism: validation through ERNDIM external quality assessment schemes.
UPLC
external quality assessment
inborn errors of metabolism
tandem mass spectrometry
targeted metabolomics
Journal
Clinical chemistry and laboratory medicine
ISSN: 1437-4331
Titre abrégé: Clin Chem Lab Med
Pays: Germany
ID NLM: 9806306
Informations de publication
Date de publication:
11 Mar 2024
11 Mar 2024
Historique:
received:
14
11
2023
accepted:
22
02
2024
medline:
8
3
2024
pubmed:
8
3
2024
entrez:
8
3
2024
Statut:
aheadofprint
Résumé
Early diagnosis of inborn errors of metabolism (IEM) is crucial to ensure early detection of conditions which are treatable. This study reports on targeted metabolomic procedures for the diagnosis of IEM of amino acids, acylcarnitines, creatine/guanidinoacetate, purines/pyrimidines and oligosaccharides, and describes its validation through external quality assessment schemes (EQA). Analysis was performed on a Waters ACQUITY UPLC H-class system coupled to a Waters Xevo triple-quadrupole (TQD) mass spectrometer, operating in both positive and negative electrospray ionization mode. Chromatographic separation was performed on a CORTECS C18 column (2.1 × 150, 1.6 µm). Data were collected by multiple reaction monitoring. The internal and EQA results were generally adequate, with a few exceptions. We calculated the relative measurement error (RME) and only a few metabolites displayed a RME higher than 30 % (asparagine and some acylcarnitine species). For oligosaccharides, semi-quantitative analysis of an educational panel clearly identified the 8 different diseases included. Overall, we have validated our analytical system through an external quality control assessment. This validation will contribute to harmonization between laboratories, thus improving identification and management of patients with IEM.
Identifiants
pubmed: 38456798
pii: cclm-2023-1291
doi: 10.1515/cclm-2023-1291
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024 Walter de Gruyter GmbH, Berlin/Boston.
Références
Hertzog, A, Selvanathan, A, Devanapalli, B, Ho, G, Bhattacharya, K, Tolun, AA. A narrative review of metabolomics in the era of “-omics”: integration into clinical practice for inborn errors of metabolism. Transl Pediatr 2022;11:1704–16. https://doi.org/10.21037/tp-22-105 .
doi: 10.21037/tp-22-105
Wevers, RA, Blau, N. Think big — think omics. J Inherit Metab Dis 2018;41:281–3. https://doi.org/10.1007/s10545-018-0165-4 .
doi: 10.1007/s10545-018-0165-4
Chace, DH, Kalas, TA, Naylor, EW. Use of tandem mass spectrometry for multianalyte screening of dried blood specimens from newborns. Clin Chem 2003;49:1797–817. https://doi.org/10.1373/clinchem.2003.022178 .
doi: 10.1373/clinchem.2003.022178
Bongaerts, M, Bonte, R, Demirdas, S, Huidekoper, HH, Langendonk, J, Wilke, M, et al.. Integration of metabolomics with genomics: metabolic gene prioritization using metabolomics data and genomic variant (CADD) scores. Mol Genet Metabol 2022;136:199–218. https://doi.org/10.1016/j.ymgme.2022.05.002 .
doi: 10.1016/j.ymgme.2022.05.002
Coene, KLM, Kluijtmans, LAJ, van der Heeft, E, Engelke, UFH, de Boer, S, Hoegen, B, et al.. Next-generation metabolic screening: targeted and untargeted metabolomics for the diagnosis of inborn errors of metabolism in individual patients. J Inherit Metab Dis 2018;41:337–53. https://doi.org/10.1007/s10545-017-0131-6 .
doi: 10.1007/s10545-017-0131-6
Hoegen, B, Zammit, A, Gerritsen, A, Engelke, UFH, Castelein, S, van de Vorst, M, et al.. Metabolomics-based screening of inborn errors of metabolism: enhancing clinical application with a robust computational pipeline. Metabolites 2021;11:568. https://doi.org/10.3390/metabo11090568 .
doi: 10.3390/metabo11090568
ERNDIMQA – [Internet]. Erndimqa.nl. https://www.erndimqa.nl [Accessed 13 June 2023].
Saudubray, J-M, Baumgartner, MR, García-Cazorla, A, Walter, JH. Inborn metabolic diseases: diagnosis and treatment . Heidelberg: Springer; 2022.
Rebollido-Fernandez, MM, Castiñeiras, DE, Dolores Bóveda, M, Luz Couce, M, Cocho, JA, Fraga, JM. Development of electrospray ionization tandem mass spectrometry methods for the study of a high number of urine markers of inborn errors of metabolism. Rapid Commun Mass Spectrom 2012;26:2131–44. https://doi.org/10.1002/rcm.6325 .
doi: 10.1002/rcm.6325
Varma, VR, Oommen, AM, Varma, S, Casanova, R, An, Y, Andrews, RM, et al.. Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: a targeted metabolomics study. PLoS Med 2018;15:e1002482. https://doi.org/10.1371/journal.pmed.1002482 .
doi: 10.1371/journal.pmed.1002482
Carlsson, H, Abujrais, S, Herman, S, Khoonsari, PE, Åkerfeldt, T, Svenningsson, A, et al.. Targeted metabolomics of CSF in healthy individuals and patients with secondary progressive multiple sclerosis using high-resolution mass spectrometry. Metabolomics 2020;16:16–26. https://doi.org/10.1007/s11306-020-1648-5 .
doi: 10.1007/s11306-020-1648-5
Lee, J, Greaves, R, Hong, KM, Eggington, M, Srikumar, A, Kumar, M, et al.. Detecting inborn errors of metabolism by targeted metabolomics: a Victorian experience. Pathology 2023;55:S16. https://doi.org/10.1016/j.pathol.2022.12.059 .
doi: 10.1016/j.pathol.2022.12.059
Casado, M, Sierra, C, Batllori, M, Artuch, R, Ormazabal, A. A targeted metabolomic procedure for amino acid analysis in different biological specimens by ultra-high-performance liquid chromatography–tandem mass spectrometry. Metabolomics 2018;14:76–88. https://doi.org/10.1007/s11306-018-1374-4 .
doi: 10.1007/s11306-018-1374-4
Blau, N, Duran, M, Blaskovics, ME, Gibson, KM, editors. Physician’s guide to the laboratory diagnosis of metabolic diseases . Berlin, Heidelberg: Springer Berlin Heidelberg; 2003.
Arias, A, Ormazabal, A, Moreno, J, González, B, Vilaseca, MA, García-Villoria, J, et al.. Methods for the diagnosis of creatine deficiency syndromes: a comparative study. J Neurosci Methods 2006;156:305–9. https://doi.org/10.1016/j.jneumeth.2006.03.005 .
doi: 10.1016/j.jneumeth.2006.03.005
Cognat, S, Cheillan, D, Piraud, M, Roos, B, Jakobs, C, Vianey-Saban, C. Determination of guanidinoacetate and creatine in urine and plasma by liquid chromatography–tandem mass spectrometry. Clin Chem 2004;50:1459–61. https://doi.org/10.1373/clinchem.2004.034538 .
doi: 10.1373/clinchem.2004.034538
Monostori, P, Klinke, G, Hauke, J, Richter, S, Bierau, J, Garbade, SF, et al.. Extended diagnosis of purine and pyrimidine disorders from urine: LC MS/MS assay development and clinical validation. PLoS One 2019;14:e0212458. https://doi.org/10.1371/journal.pone.0212458 .
doi: 10.1371/journal.pone.0212458
Hartmann, S, Okun, JG, Schmidt, C, Langhans, C-D, Garbade, SF, Burgard, P, et al.. Comprehensive detection of disorders of purine and pyrimidine metabolism by HPLC with electrospray ionization tandem mass spectrometry. Clin Chem 2006;52:1127–37. https://doi.org/10.1373/clinchem.2005.058842 .
doi: 10.1373/clinchem.2005.058842
Sowell, J, Wood, T. Towards a selected reaction monitoring mass spectrometry fingerprint approach for the screening of oligosaccharidoses. Anal Chim Acta 2011;686:102–6. https://doi.org/10.1016/j.aca.2010.11.047 .
doi: 10.1016/j.aca.2010.11.047
U.S. Food and Drug Administration . Guidance for Industry – Bioanalytical method validation- . Rockville: FDA; 2018.
Sarmad, S, Viant, MR, Dunn, WB, Goodacre, R, Wilson, ID, Chappell, KE, et al.. A proposed framework to evaluate the quality and reliability of targeted metabolomics assays from the UK Consortium on Metabolic Phenotyping (MAP/UK). Nat Protoc 2023;18:1017–27. https://doi.org/10.1038/s41596-022-00801-8 .
doi: 10.1038/s41596-022-00801-8
CLSI. Liquid chromatography-mass spectrometry methods. CLSI guideline C62A . Wayne: Clinical and Laboratory Standards Institute; 2014.
Rigo-Bonnin, R. Cromatografía líquida de alta resolución y espectrometría de masas . Barcelona: SEQC ML; 2019.
Liu, N, Xiao, J, Gijavanekar, C, Pappan, KL, Glinton, KE, Shayota, BJ, et al.. Comparison of untargeted metabolomic profiling vs traditional metabolic screening to identify inborn errors of metabolism. JAMA Netw Open 2021;4:e2114155. https://doi.org/10.1001/jamanetworkopen.2021.14155 .
doi: 10.1001/jamanetworkopen.2021.14155
Loh, T, Cooke, B, Markus, C, Zakaria, R, Tran, M, Ho, C, et al.. On behalf of the IFCC working group on method evaluation protocols. Method evaluation in the clinical laboratory. Clin Chem Lab Med 2023;61:751–8. https://doi.org/10.1515/cclm-2022-0878 .
doi: 10.1515/cclm-2022-0878
Braga, F, Pasqualetti, S, Panteghini, M. The role of external quality assessment in the verification of in vitro medical diagnostics in the traceability era. Clin Biochem 2018;57:23–8. https://doi.org/10.1016/j.clinbiochem.2018.02.004 .
doi: 10.1016/j.clinbiochem.2018.02.004
Delmar, JA, Wang, J, Choi, SW, Martins, JA, Mikhail, JP. Machine learning enables accurate prediction of asparagine deamidation probability and rate. Mol Ther Methods Clin Dev 2019;15:264–74. https://doi.org/10.1016/j.omtm.2019.09.008 .
doi: 10.1016/j.omtm.2019.09.008
An, Z, Shi, C, Li, P, Liu, L. Stability of amino acids and related amines in human serum under different preprocessing and pre-storage conditions based on iTRAQ®-LC-MS/MS. Biol Open 2021;10:bio055020. https://doi.org/10.1242/bio.055020 .
doi: 10.1242/bio.055020
Carling, RS, Whyte, E, John, C, Garstone, R, Goddard, P, Greenfield, T, et al.. Improving harmonization and standardization of expanded newborn screening results by optimization of the legacy flow injection analysis tandem mass spectrometry methods and application of a standardized calibration approach. Clin Chem 2022;68:1075–83. https://doi.org/10.1093/clinchem/hvac070 .
doi: 10.1093/clinchem/hvac070
Casado, M, Altimira, L, Montero, R, Castejón, E, Nascimento, A, Pérez-Dueñas, B, et al.. A capillary electrophoresis procedure for the screening of oligosaccharidoses and related diseases. Anal Bioanal Chem 2014;406:4337–43. https://doi.org/10.1007/s00216-014-7832-6 .
doi: 10.1007/s00216-014-7832-6
Piraud, M, Pettazzoni, M, Menegaut, L, Caillaud, C, Nadjar, Y, Vianey-Saban, C, et al.. Development of a new tandem mass spectrometry method for urine and amniotic fluid screening of oligosaccharidoses. Rapid Commun Mass Spectrom 2017;31:951–63. https://doi.org/10.1002/rcm.7860 .
doi: 10.1002/rcm.7860
Mak, J, Cowan, TM. Detecting lysosomal storage disorders by glycomic profiling using liquid chromatography mass spectrometry. Mol Genet Metabol 2021;134:43–52. https://doi.org/10.1016/j.ymgme.2021.08.006 .
doi: 10.1016/j.ymgme.2021.08.006
Semeraro, M, Sacchetti, E, Deodato, F, Coşkun, T, Lay, I, Catesini, G, et al.. A new UHPLC-MS/MS method for the screening of urinary oligosaccharides expands the detection of storage disorders. Orphanet J Rare Dis 2021;16:24–34. https://doi.org/10.1186/s13023-020-01662-8 .
doi: 10.1186/s13023-020-01662-8
Zhang, A, Sun, H, Xu, H, Qiu, S, Wang, X. Cell metabolomics. OMICS 2013;17:495–501. https://doi.org/10.1089/omi.2012.0090 .
doi: 10.1089/omi.2012.0090
Yang, J, Zhao, X, Lu, X, Lin, X, Xu, G. A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis. Front Mol Biosci 2015;2:4–12. https://doi.org/10.3389/fmolb.2015.00004 .
doi: 10.3389/fmolb.2015.00004
Peters, TMA, Engelke, UFH, de Boer, S, van der Heeft, E, Pritsch, C, Kulkarni, P, et al.. Confirmation of neurometabolic diagnoses using age-dependent cerebrospinal fluid metabolomic profiles. J Inherit Metab Dis 2020;43:1112–20. https://doi.org/10.1002/jimd.12253 .
doi: 10.1002/jimd.12253
Bongaerts, M, Bonte, R, Demirdas, S, Jacobs, E, Oussoren, E, van der Ploeg, A, et al.. Using out-of-batch reference populations to improve untargeted metabolomics for screening inborn errors of metabolism. Metabolites 2020;11:8–47. https://doi.org/10.3390/metabo11010008 .
doi: 10.3390/metabo11010008
Almontashiri, NAM, Zha, L, Young, K, Law, T, Kellogg, MD, Bodamer, OA, et al.. Clinical validation of targeted and untargeted metabolomics testing for genetic disorders: a 3 year comparative study. Sci Rep 2020;10. https://doi.org/10.1038/s41598-020-66401-2 .
doi: 10.1038/s41598-020-66401-2
Steinbusch, LKM, Wang, P, Waterval, HWAH, Stassen, FAPM, Coene, KLM, Engelke, UFH, et al.. Targeted urine metabolomics with a graphical reporting tool for rapid diagnosis of inborn errors of metabolism. J Inherit Metab Dis 2021;44:1113–23. https://doi.org/10.1002/jimd.12385 .
doi: 10.1002/jimd.12385