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J Chromatogr A. 2018 Sep 21;1568:229-234. doi: 10.1016/j.chroma.2018.07.019. Epub 2018 Jul 5.

Quantification of run order effect on chromatography - mass spectrometry profiling data.

Author information

1
Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, Linnaeus väg 10, 901 87 Umeå, Sweden. Electronic address: izabella.surowiec@umu.se.
2
Sartorius Stedim Data Analytics, Tvistevägen 48, 907 36 Umeå, Sweden.
3
Swedish Metabolomics Centre, Linnaeus väg 6, 901 87 Umeå, Sweden.
4
Department of Public Health and Clinical Medicine, Rheumatology, Umeå University Hospital, 901 87 Umeå, Sweden.
5
Department of Molecular Biology, Umeå University, 901 87 Umeå, Sweden.
6
Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, Linnaeus väg 10, 901 87 Umeå, Sweden; Sartorius Stedim Data Analytics, Tvistevägen 48, 907 36 Umeå, Sweden.

Abstract

Chromatographic systems coupled with mass spectrometry detection are widely used in biological studies investigating how levels of biomolecules respond to different internal and external stimuli. Such changes are normally expected to be of low magnitude and therefore all experimental factors that can influence the analysis need to be understood and minimized. Run order effect is commonly observed and constitutes a major challenge in chromatography-mass spectrometry based profiling studies that needs to be addressed before the biological evaluation of measured data is made. So far there is no established consensus, metric or method that quickly estimates the size of this effect. In this paper we demonstrate how orthogonal projections to latent structures (OPLS®) can be used for objective quantification of the run order effect in profiling studies. The quantification metric is expressed as the amount of variation in the experimental data that is correlated to the run order. One of the primary advantages with this approach is that it provides a fast way of quantifying run-order effect for all detected features, not only internal standards. Results obtained from quantification of run order effect as provided by the OPLS can be used in the evaluation of data normalization, support the optimization of analytical protocols and identification of compounds highly influenced by instrumental drift. The application of OPLS for quantification of run order is demonstrated on experimental data from plasma profiling performed on three analytical platforms: GCMS metabolomics, LCMS metabolomics and LCMS lipidomics.

KEYWORDS:

Instrumental drift; Mass spectrometry profiling; OPLS; Run order effect quantification

PMID:
30007791
DOI:
10.1016/j.chroma.2018.07.019
[Indexed for MEDLINE]

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