Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study
Large-scale metabolomic studies have become common, and the reliability of the peak data produced by the various instruments is an important issue. However, less attention has been paid to the large number of uncharacterized peaks in untargeted metabolomics data. In this study, we tested various cri...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-04-01
|
| Series: | Journal of Clinical Medicine |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0383/10/9/1826 |
| _version_ | 1797536738261860352 |
|---|---|
| author | Rintaro Saito Masahiro Sugimoto Akiyoshi Hirayama Tomoyoshi Soga Masaru Tomita Toru Takebayashi |
| author_facet | Rintaro Saito Masahiro Sugimoto Akiyoshi Hirayama Tomoyoshi Soga Masaru Tomita Toru Takebayashi |
| author_sort | Rintaro Saito |
| collection | DOAJ |
| description | Large-scale metabolomic studies have become common, and the reliability of the peak data produced by the various instruments is an important issue. However, less attention has been paid to the large number of uncharacterized peaks in untargeted metabolomics data. In this study, we tested various criteria to assess the reliability of 276 and 202 uncharacterized peaks that were detected in a gathered set of 30 plasma and urine quality control samples, respectively, using capillary electrophoresis-time-of-flight mass spectrometry (CE-TOFMS). The linear relationship between the amounts of pooled samples and the corresponding peak areas was one of the criteria used to select reliable peaks. We used samples from approximately 3000 participants in the Tsuruoka Metabolome Cohort Study to investigate patterns of the areas of these uncharacterized peaks among the samples and clustered the peaks by combining the patterns and differences in the migration times. Our assessment pipeline removed substantial numbers of unreliable or redundant peaks and detected 35 and 74 reliable uncharacterized peaks in plasma and urine, respectively, some of which may correspond to metabolites involved in important physiological processes such as disease progression. We propose that our assessment pipeline can be used to help establish large-scale untargeted clinical metabolomic studies. |
| first_indexed | 2024-03-10T12:05:03Z |
| format | Article |
| id | doaj.art-beca0ae835e642829c0a0b4826cf0942 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T12:05:03Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-beca0ae835e642829c0a0b4826cf09422023-11-21T16:42:24ZengMDPI AGJournal of Clinical Medicine2077-03832021-04-01109182610.3390/jcm10091826Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic StudyRintaro Saito0Masahiro Sugimoto1Akiyoshi Hirayama2Tomoyoshi Soga3Masaru Tomita4Toru Takebayashi5Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, JapanInstitute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, JapanInstitute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, JapanInstitute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, JapanInstitute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, JapanInstitute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, JapanLarge-scale metabolomic studies have become common, and the reliability of the peak data produced by the various instruments is an important issue. However, less attention has been paid to the large number of uncharacterized peaks in untargeted metabolomics data. In this study, we tested various criteria to assess the reliability of 276 and 202 uncharacterized peaks that were detected in a gathered set of 30 plasma and urine quality control samples, respectively, using capillary electrophoresis-time-of-flight mass spectrometry (CE-TOFMS). The linear relationship between the amounts of pooled samples and the corresponding peak areas was one of the criteria used to select reliable peaks. We used samples from approximately 3000 participants in the Tsuruoka Metabolome Cohort Study to investigate patterns of the areas of these uncharacterized peaks among the samples and clustered the peaks by combining the patterns and differences in the migration times. Our assessment pipeline removed substantial numbers of unreliable or redundant peaks and detected 35 and 74 reliable uncharacterized peaks in plasma and urine, respectively, some of which may correspond to metabolites involved in important physiological processes such as disease progression. We propose that our assessment pipeline can be used to help establish large-scale untargeted clinical metabolomic studies.https://www.mdpi.com/2077-0383/10/9/1826cohort studymetabolomicscapillary electrophoresis-mass spectrometry |
| spellingShingle | Rintaro Saito Masahiro Sugimoto Akiyoshi Hirayama Tomoyoshi Soga Masaru Tomita Toru Takebayashi Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study Journal of Clinical Medicine cohort study metabolomics capillary electrophoresis-mass spectrometry |
| title | Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study |
| title_full | Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study |
| title_fullStr | Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study |
| title_full_unstemmed | Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study |
| title_short | Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study |
| title_sort | quality assessment of untargeted analytical data in a large scale metabolomic study |
| topic | cohort study metabolomics capillary electrophoresis-mass spectrometry |
| url | https://www.mdpi.com/2077-0383/10/9/1826 |
| work_keys_str_mv | AT rintarosaito qualityassessmentofuntargetedanalyticaldatainalargescalemetabolomicstudy AT masahirosugimoto qualityassessmentofuntargetedanalyticaldatainalargescalemetabolomicstudy AT akiyoshihirayama qualityassessmentofuntargetedanalyticaldatainalargescalemetabolomicstudy AT tomoyoshisoga qualityassessmentofuntargetedanalyticaldatainalargescalemetabolomicstudy AT masarutomita qualityassessmentofuntargetedanalyticaldatainalargescalemetabolomicstudy AT torutakebayashi qualityassessmentofuntargetedanalyticaldatainalargescalemetabolomicstudy |