Evidence map›Paper›PMID 31683650›Full record

ArticleMetabolites2019

Inter-Laboratory Comparison of Metabolite Measurements for Metabolomics Data Integration.

Yoshihiro Izumi, Fumio Matsuda, Akiyoshi Hirayama, Kazutaka Ikeda, Yoshihiro Kita, Kanta Horie, Daisuke Saigusa, Kosuke Saito, Yuji Sawada, Hiroki Nakanishi and 9 more

Abstract read
In one paragraph

Article in Metabolites, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Yoshihiro IzumiDivision of Metabolomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan. izumi@bioreg.kyushu-u.ac.jp.ORCID 0000-0003-4608-4652
Fumio MatsudaDepartment of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan. fmatsuda@ist.osaka-u.ac.jp.ORCID 0000-0003-1091-778X
Akiyoshi HirayamaInstitute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata 997-0052, Japan. hirayama@ttck.keio.ac.jp.ORCID 0000-0002-0440-6820
Kazutaka IkedaLaboratory for Metabolomics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama, Kanagawa 230-0045, Japan. kazutaka.ikeda@riken.jp.ORCID 0000-0001-9247-4593
Yoshihiro KitaDepartment of Lipidomics, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan. kita@m.u-tokyo.ac.jp.
Kanta HorieTranslational Science, Neurology Business Group, Eisai Co., Ltd., 5-1-3 Tokodai, Tsukuba, Ibaraki 300-2635, Japan. k2-horie@hhc.eisai.co.jp.
Daisuke SaigusaTohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8573, Japan. saigusa@m.tohoku.ac.jp.ORCID 0000-0001-9484-9870
Kosuke SaitoDivision of Medical Safety Science, National Institute of Health Science, 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 210-9501, Japan. saitok2@nihs.go.jp.ORCID 0000-0002-1035-3454
Yuji SawadaRIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. yuji.sawada@riken.jp.ORCID 0000-0002-7427-1749
Hiroki NakanishiResearch Center for Biosignal, Akita University, 1-1-1 Hondo, Akita-city, Akita 010-8543, Japan. hnakani@med.akita-u.ac.jp.
Nobuyuki OkahashiDepartment of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan. n-okahashi@ist.osaka-u.ac.jp.
Masatomo TakahashiDivision of Metabolomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan. m-takahashi@bioreg.kyushu-u.ac.jp.ORCID 0000-0002-2493-2684
Motonao NakaoDivision of Metabolomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan. nakao@bioreg.kyushu-u.ac.jp.ORCID 0000-0003-0927-7762
Kosuke HataDivision of Metabolomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan. k-hata@bioreg.kyushu-u.ac.jp.ORCID 0000-0002-2278-4796
Yutaro HoshiPharmacokinetic Research Laboratories, Ono Pharmaceutical Co., Ltd., 17-2 Wadai, Tsukuba, Ibaraki 300-4247, Japan. y.hoshi@ono.co.jp.ORCID 0000-0003-0497-5094
Motohiko MoriharaTranslational Research Laboratories, Ono Pharmaceutical Co., Ltd., 3-1-1 Sakurai Shimamoto-cho, Mishima-gun, Osaka 618-8585, Japan. morihara@ono.co.jp.ORCID 0000-0001-6204-3944
Kazuhiro TanabeMedical Solution Promotion Department, Medical Solution Segment, LSI Medience Corporation, 3-30-1, Shimura, Itabashi-ku, Tokyo 174-8555, Japan. tanabe.kazuhiro@mp.medience.co.jp.ORCID 0000-0003-2671-1217
Takeshi BambaDivision of Metabolomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan. bamba@bioreg.kyushu-u.ac.jp.ORCID 0000-0002-7442-2738
Yoshiya OdaDepartment of Lipidomics, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan. yoda@m.u-tokyo.ac.jp.

Funding

Japan Agency for Medical Research and Development 19ak0101043j0605Japan Agency for Medical Research and Development JP19km0105001Japan Agency for Medical Research and Development JP19km0105002Japan Society for the Promotion of Science 17H06303Japan Society for the Promotion of Science 17H06304Japan Society for the Promotion of Science 19K05167
6 · The paper itself

Abstract

backgroundOne of the current problems in the field of metabolomics is the difficulty in integrating data collected using different equipment at different facilities, because many metabolomic methods have been developed independently and are unique to each laboratory.

methodsIn this study, we examined whether different analytical methods among 12 different laboratories provided comparable relative quantification data for certain metabolites. Identical samples extracted from two cell lines (HT-29 and AsPc-1) were distributed to each facility, and hydrophilic and hydrophobic metabolite analyses were performed using the daily routine protocols of each laboratory.

resultsThe results indicate that there was no difference in the relative quantitative data (HT-29/AsPc-1) for about half of the measured metabolites among the laboratories and assay methods. Data review also revealed that errors in relative quantification were derived from issues such as erroneous peak identification, insufficient peak separation, a difference in detection sensitivity, derivatization reactions, and extraction solvent interference.

conclusionThe results indicated that relative quantification data obtained at different facilities and at different times would be integrated and compared by using a reference materials shared for data normalization.

Indexed as

data integrationinter-laboratory comparisonmetabolomicsmethod validationquality control samplerelative quantification

Identifiers

PMID31683650
PMCPMC6918145

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.