Direct Infusion Based Metabolomics Identifies Metabolic Disease in Patients’ Dried Blood Spots and Plasma
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...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-01-01
|
| Series: | Metabolites |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2218-1989/9/1/12 |
| _version_ | 1818130055391346688 |
|---|---|
| author | Hanneke A. Haijes Marcel Willemsen Maria Van der Ham Johan Gerrits Mia L. Pras-Raves Hubertus C. M. T. Prinsen Peter M. Van Hasselt Monique G. M. De Sain-van der Velden Nanda M. Verhoeven-Duif Judith J. M. Jans |
| author_facet | Hanneke A. Haijes Marcel Willemsen Maria Van der Ham Johan Gerrits Mia L. Pras-Raves Hubertus C. M. T. Prinsen Peter M. Van Hasselt Monique G. M. De Sain-van der Velden Nanda M. Verhoeven-Duif Judith J. M. Jans |
| author_sort | Hanneke A. Haijes |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T07:58:57Z |
| format | Article |
| id | doaj.art-f11723bf334b4ab69cbe6cb8e48d9633 |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-12-11T07:58:57Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-f11723bf334b4ab69cbe6cb8e48d96332022-12-22T01:15:10ZengMDPI AGMetabolites2218-19892019-01-01911210.3390/metabo9010012metabo9010012Direct Infusion Based Metabolomics Identifies Metabolic Disease in Patients’ Dried Blood Spots and PlasmaHanneke A. Haijes0Marcel Willemsen1Maria Van der Ham2Johan Gerrits3Mia L. Pras-Raves4Hubertus C. M. T. Prinsen5Peter M. Van Hasselt6Monique G. M. De Sain-van der Velden7Nanda M. Verhoeven-Duif8Judith J. M. Jans9Section Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diseases, Department of Child Health, Wilhelmina Children’s Hospital, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsIn 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.http://www.mdpi.com/2218-1989/9/1/12metabolomicsinborn errors of metabolismdirect-infusion mass spectrometryIEMDIMS |
| spellingShingle | Hanneke A. Haijes Marcel Willemsen Maria Van der Ham Johan Gerrits Mia L. Pras-Raves Hubertus C. M. T. Prinsen Peter M. Van Hasselt Monique G. M. De Sain-van der Velden Nanda M. Verhoeven-Duif Judith J. M. Jans Direct Infusion Based Metabolomics Identifies Metabolic Disease in Patients’ Dried Blood Spots and Plasma Metabolites metabolomics inborn errors of metabolism direct-infusion mass spectrometry IEM DIMS |
| title | Direct Infusion Based Metabolomics Identifies Metabolic Disease in Patients’ Dried Blood Spots and Plasma |
| title_full | Direct Infusion Based Metabolomics Identifies Metabolic Disease in Patients’ Dried Blood Spots and Plasma |
| title_fullStr | Direct Infusion Based Metabolomics Identifies Metabolic Disease in Patients’ Dried Blood Spots and Plasma |
| title_full_unstemmed | Direct Infusion Based Metabolomics Identifies Metabolic Disease in Patients’ Dried Blood Spots and Plasma |
| title_short | Direct Infusion Based Metabolomics Identifies Metabolic Disease in Patients’ Dried Blood Spots and Plasma |
| title_sort | direct infusion based metabolomics identifies metabolic disease in patients dried blood spots and plasma |
| topic | metabolomics inborn errors of metabolism direct-infusion mass spectrometry IEM DIMS |
| url | http://www.mdpi.com/2218-1989/9/1/12 |
| work_keys_str_mv | AT hannekeahaijes directinfusionbasedmetabolomicsidentifiesmetabolicdiseaseinpatientsdriedbloodspotsandplasma AT marcelwillemsen directinfusionbasedmetabolomicsidentifiesmetabolicdiseaseinpatientsdriedbloodspotsandplasma AT mariavanderham directinfusionbasedmetabolomicsidentifiesmetabolicdiseaseinpatientsdriedbloodspotsandplasma AT johangerrits directinfusionbasedmetabolomicsidentifiesmetabolicdiseaseinpatientsdriedbloodspotsandplasma AT mialprasraves directinfusionbasedmetabolomicsidentifiesmetabolicdiseaseinpatientsdriedbloodspotsandplasma AT hubertuscmtprinsen directinfusionbasedmetabolomicsidentifiesmetabolicdiseaseinpatientsdriedbloodspotsandplasma AT petermvanhasselt directinfusionbasedmetabolomicsidentifiesmetabolicdiseaseinpatientsdriedbloodspotsandplasma AT moniquegmdesainvandervelden directinfusionbasedmetabolomicsidentifiesmetabolicdiseaseinpatientsdriedbloodspotsandplasma AT nandamverhoevenduif directinfusionbasedmetabolomicsidentifiesmetabolicdiseaseinpatientsdriedbloodspotsandplasma AT judithjmjans directinfusionbasedmetabolomicsidentifiesmetabolicdiseaseinpatientsdriedbloodspotsandplasma |