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Compensation for Systematic Cross-Contribution Improves Normalization of Mass Spectrometry Based Metabolomics Data

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Henning Redestig*^†, Atsushi Fukushima^†, Hans Stenlund^‡, Thomas Moritz^‡, Masanori Arita^†, Kazuki Saito^† and Miyako Kusano^†

RIKEN Plant Science Center, Tsurumi-ku, Suehiro-cho, 1-7-22 Yokohama, Kanagawa, 230-0045, Japan, and Umeå Plant Science Center, Umeå University, 901 87 Umeå, Sweden

Anal. Chem., 2009, 81 (19), pp 7974–7980

DOI: 10.1021/ac901143w

Publication Date (Web): September 10, 2009

* To whom correspondence should be addressed. E-mail: henning@psc.riken.jp., †

RIKEN Plant Science Center.

, ‡

Umeå Plant Science Center (UPSC).

Section:

Biochemical Methods

Abstract

Most mass spectrometry based metabolomics studies are semiquantitative and depend on efficient normalization techniques to suppress systematic error. A common approach is to include isotope-labeled internal standards (ISs) and then express the estimated metabolite abundances relative to the IS. Because of problems such as insufficient chromatographic resolution, however, the analytes may directly influence estimates of the IS, a phenomenon known as cross-contribution (CC). Normalization using ISs that suffer from CC effects will cause significant loss of information if the interfering analytes are associated with the studied factors. We present a novel normalization algorithm, which compensates for systematic CC effects that can be traced back to a linear association with the experimental design. The proposed method was found to be superior at purifying the signal of interest compared to current normalization methods when applied to two biological data sets and a multicomponent dilution mixture. Our method is applicable to data from randomized and designed experiments that use ISs to monitor the systematic error.

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This article has been cited by 2 ACS Journal articles (2 most recent appear below).

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