Towards Standardization of Data Normalization Strategies to Improve Urinary Metabolomics Studies by GC×GC-TOFMS
Urine is a popular biofluid for metabolomics studies due to its simple, non-invasive collection and its availability in large quantities, permitting frequent sampling, replicate analyses, and sample banking. The biggest disadvantage with using urine is that it exhibits significant variability in con...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-09-01
|
| Series: | Metabolites |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-1989/10/9/376 |
| _version_ | 1827705902890745856 |
|---|---|
| author | Seo Lin Nam A. Paulina de la Mata Ryan P. Dias James J Harynuk |
| author_facet | Seo Lin Nam A. Paulina de la Mata Ryan P. Dias James J Harynuk |
| author_sort | Seo Lin Nam |
| collection | DOAJ |
| description | Urine is a popular biofluid for metabolomics studies due to its simple, non-invasive collection and its availability in large quantities, permitting frequent sampling, replicate analyses, and sample banking. The biggest disadvantage with using urine is that it exhibits significant variability in concentration and composition within an individual over relatively short periods of time (arising from various external factors and internal processes regulating the body’s water and solute content). In treating the data from urinary metabolomics studies, one must account for the natural variability of urine concentrations to avoid erroneous data interpretation. Amongst various proposed approaches to account for broadly varying urine sample concentrations, normalization to creatinine has been widely accepted and is most commonly used. MS total useful signal (MSTUS) is another normalization method that has been recently reported for mass spectrometry (MS)-based metabolomics studies. Herein, we explored total useful peak area (TUPA), a modification of MSTUS that is applicable to GC×GC-TOFMS (and data from other separations platforms), for sample normalization in urinary metabolomics studies. Performance of TUPA was compared to the two most common normalization approaches, creatinine adjustment and Total Peak Area (TPA) normalization. Each normalized dataset was evaluated using Principal Component Analysis (PCA). The results showed that TUPA outperformed alternative normalization methods to overcome urine concentration variability. Results also conclusively demonstrate the risks in normalizing data to creatinine. |
| first_indexed | 2024-03-10T16:13:09Z |
| format | Article |
| id | doaj.art-f830026696ae48318d412b2ba29a5cfc |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-03-10T16:13:09Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-f830026696ae48318d412b2ba29a5cfc2023-11-20T14:22:01ZengMDPI AGMetabolites2218-19892020-09-0110937610.3390/metabo10090376Towards Standardization of Data Normalization Strategies to Improve Urinary Metabolomics Studies by GC×GC-TOFMSSeo Lin Nam0A. Paulina de la Mata1Ryan P. Dias2James J Harynuk3Department of Chemistry, University of Alberta, Edmonton, AB T6G 2G2, CanadaDepartment of Chemistry, University of Alberta, Edmonton, AB T6G 2G2, CanadaDepartment of Chemistry, University of Alberta, Edmonton, AB T6G 2G2, CanadaDepartment of Chemistry, University of Alberta, Edmonton, AB T6G 2G2, CanadaUrine is a popular biofluid for metabolomics studies due to its simple, non-invasive collection and its availability in large quantities, permitting frequent sampling, replicate analyses, and sample banking. The biggest disadvantage with using urine is that it exhibits significant variability in concentration and composition within an individual over relatively short periods of time (arising from various external factors and internal processes regulating the body’s water and solute content). In treating the data from urinary metabolomics studies, one must account for the natural variability of urine concentrations to avoid erroneous data interpretation. Amongst various proposed approaches to account for broadly varying urine sample concentrations, normalization to creatinine has been widely accepted and is most commonly used. MS total useful signal (MSTUS) is another normalization method that has been recently reported for mass spectrometry (MS)-based metabolomics studies. Herein, we explored total useful peak area (TUPA), a modification of MSTUS that is applicable to GC×GC-TOFMS (and data from other separations platforms), for sample normalization in urinary metabolomics studies. Performance of TUPA was compared to the two most common normalization approaches, creatinine adjustment and Total Peak Area (TPA) normalization. Each normalized dataset was evaluated using Principal Component Analysis (PCA). The results showed that TUPA outperformed alternative normalization methods to overcome urine concentration variability. Results also conclusively demonstrate the risks in normalizing data to creatinine.https://www.mdpi.com/2218-1989/10/9/376urinemetabolomicsnormalizationmass spectrometryGC×GC-TOFMScreatinine |
| spellingShingle | Seo Lin Nam A. Paulina de la Mata Ryan P. Dias James J Harynuk Towards Standardization of Data Normalization Strategies to Improve Urinary Metabolomics Studies by GC×GC-TOFMS Metabolites urine metabolomics normalization mass spectrometry GC×GC-TOFMS creatinine |
| title | Towards Standardization of Data Normalization Strategies to Improve Urinary Metabolomics Studies by GC×GC-TOFMS |
| title_full | Towards Standardization of Data Normalization Strategies to Improve Urinary Metabolomics Studies by GC×GC-TOFMS |
| title_fullStr | Towards Standardization of Data Normalization Strategies to Improve Urinary Metabolomics Studies by GC×GC-TOFMS |
| title_full_unstemmed | Towards Standardization of Data Normalization Strategies to Improve Urinary Metabolomics Studies by GC×GC-TOFMS |
| title_short | Towards Standardization of Data Normalization Strategies to Improve Urinary Metabolomics Studies by GC×GC-TOFMS |
| title_sort | towards standardization of data normalization strategies to improve urinary metabolomics studies by gc gc tofms |
| topic | urine metabolomics normalization mass spectrometry GC×GC-TOFMS creatinine |
| url | https://www.mdpi.com/2218-1989/10/9/376 |
| work_keys_str_mv | AT seolinnam towardsstandardizationofdatanormalizationstrategiestoimproveurinarymetabolomicsstudiesbygcgctofms AT apaulinadelamata towardsstandardizationofdatanormalizationstrategiestoimproveurinarymetabolomicsstudiesbygcgctofms AT ryanpdias towardsstandardizationofdatanormalizationstrategiestoimproveurinarymetabolomicsstudiesbygcgctofms AT jamesjharynuk towardsstandardizationofdatanormalizationstrategiestoimproveurinarymetabolomicsstudiesbygcgctofms |