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Improved experimental data processing for UHPLC–HRMS/MS lipidomics applied to nonalcoholic fatty liver disease

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Abstract

Introduction

Untargeted metabolomics workflows include numerous points where variance and systematic errors can be introduced. Due to the diversity of the lipidome, manual peak picking and quantitation using molecule specific internal standards is unrealistic, and therefore quality peak picking algorithms and further feature processing and normalization algorithms are important. Subsequent normalization, data filtering, statistical analysis, and biological interpretation are simplified when quality data acquisition and feature processing are employed.

Objectives

Metrics for QC are important throughout the workflow. The robust workflow presented here provides techniques to ensure that QC checks are implemented throughout sample preparation, data acquisition, pre-processing, and analysis.

Methods

The untargeted lipidomics workflow includes sample standardization prior to acquisition, blocks of QC standards and blanks run at systematic intervals between randomized blocks of experimental data, blank feature filtering (BFF) to remove features not originating from the sample, and QC analysis of data acquisition and processing.

Results

The workflow was successfully applied to mouse liver samples, which were investigated to discern lipidomic changes throughout the development of nonalcoholic fatty liver disease (NAFLD). The workflow, including a novel filtering method, BFF, allows improved confidence in results and conclusions for lipidomic applications.

Conclusion

Using a mouse model developed for the study of the transition of NAFLD from an early stage known as simple steatosis, to the later stage, nonalcoholic steatohepatitis, in combination with our novel workflow, we have identified phosphatidylcholines, phosphatidylethanolamines, and triacylglycerols that may contribute to disease onset and/or progression.

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Abbreviations

BFF:

Blank feature filtering

Cer:

Ceramide

DG:

Diacylglyceride

LC:

Liquid chromatography

MS:

Mass spectrometry

NAFLD:

Nonalcoholic fatty liver disease

NASH:

Nonalcoholic steatohepatitis

QC:

Quality control

RT:

Retention time

SS:

Simple steatosis

TG:

Triacylglyceride

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Acknowledgements

The authors would like to thank Miguel Ibarra, Oleksandr Moskalenko, and Justin Richardson for their computer programming expertise. We would also like to thank funding sources including the Southeastern Center for Integrated Metabolomics (NIH grant U24 DK097209), the UF Clinical Translational Science Institute (CTSI NIH Grant UL1 TR000064), and the Eastman Chemical Company Analytical Summer Fellowship.

Funding

This study was funded by NIH grant U24 DK097209, UF Clinical Translational Science Institute (CTSI NIH Grant UL1 TR000064).

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Authors and Affiliations

  1. Southeast Center for Integrated Metabolomics (SECIM), University of Florida, Gainesville, FL, USA

    R. E. Patterson, J. P. Koelmel & R. A. Yost

  2. Department of Chemistry, University of Florida, Gainesville, FL, USA

    R. E. Patterson, J. P. Koelmel & R. A. Yost

  3. Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, USA

    A. S. Kirpich, A. M. Morse & L. M. McIntyre

  4. Department of Endocrinology, Diabetes, and Metabolism, College of Medicine, University of Florida, Gainesville, FL, USA

    S. Kalavalapalli, K. Cusi & N. E. Sunny

  5. Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, USA

    T. J. Garrett & R. A. Yost

  6. Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA

    N. E. Sunny

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  1. R. E. Patterson
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Correspondence to T. J. Garrett.

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All applicable international, national, and institutional guidelines for the care and use of animals were followed. This article does not contain any studies with human participants performed by any of the authors.

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Patterson, R.E., Kirpich, A.S., Koelmel, J.P. et al. Improved experimental data processing for UHPLC–HRMS/MS lipidomics applied to nonalcoholic fatty liver disease. Metabolomics 13, 142 (2017). https://doi.org/10.1007/s11306-017-1280-1

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