Optimal inter-batch normalization method for GC/MS/MS-based targeted metabolomics with special attention to centrifugal concentration.

Clicks: 296
ID: 28193
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #44 of 90 articles by views in Analytical and bioanalytical chemistry

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
This study investigated the optimal inter-batch normalization method for gas chromatography/tandem mass spectrometry (GC/MS/MS)-based targeted metabolome analysis of rodent blood samples. The effect of centrifugal concentration on inter-batch variation was also investigated. Six serum samples prepared from a mouse and 2 quality control (QC) samples from pooled mouse serum were assigned to each batch, and the 3 batches were analyzed by GC/MS/MS at different days. The following inter-batch normalization methods were applied to metabolome data: QC-based methods with quadratic (QUAD)- or cubic spline (CS)-fitting, total signal intensity (TI)-based method, median signal intensity (MI)-based method, and isotope labeled internal standard (IS)-based method. We revealed that centrifugal concentration was a critical factor to cause inter-batch variation. Unexpectedly, neither the QC-based normalization methods nor the IS-based method was able to normalize inter-batch variation, though MI- or TI-based normalization methods were effective in normalizing inter-batch variation. For further validation, 6 disease model rat and 6 control rat plasma were evenly divided into 3 batches, and analyzed as different batches. Same as the results above, MI- or TI-based methods were able to normalize inter-batch variation. In particular, the data normalized by TI-based method showed similar metabolic profiles obtained from their intra-batch analysis. In conclusion, the TI-based normalization method is the most effective to normalize inter-batch variation for GC/MS/MS-based metabolome analysis. Graphical abstract.
Reference Key
zaitsu2019optimalanalytical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kei Zaitsu, Saki Noda, Tomomi Ohara, Tasuku Murata, Shinji Funatsu, Koretsugu Ogata, Akira Ishii, Akira Iguchi
Are you one of these authors? Claim your profile
Journal Analytical and bioanalytical chemistry
Year 2019
DOI
10.1007/s00216-019-02073-w
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.