Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Feb 15;14(2):125.
doi: 10.3390/metabo14020125.

metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics

Hani Habra  1 , Jennifer L Meijer  2 , Tong Shen  3 , Oliver Fiehn  3 , David A Gaul  4 , Facundo M Fernández  4 , Kaitlin R Rempfert  5 , Thomas O Metz  5 , Karen E Peterson  6   7 , Charles R Evans  8 , Alla Karnovsky  1
Affiliations

metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics

Hani Habra et al. Metabolites. .

Abstract

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.

Keywords: LC-MS; R package; alignment; chromatography; metabolomics; software.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
metabCombiner 2.0 workflow.
Figure 2
Figure 2
A multi-dataset object consisting of three previously aligned feature lists (A, B, and C) is aligned to a new single dataset (D), with one constituent list (Data Set A, red columns) selected to represent the combined entity for feature matching with (Data Set D, blue columns).
Figure 3
Figure 3
Unknown lipids inter-laboratory study RT mapping from laboratory dataset I to datasets II, III, and IV in the positive (A) and negative (B) ionization modes.
Figure 4
Figure 4
Venn diagram of the shared compound annotations in the unknown lipids study for positive (A) and negative (B) mode data.
Figure 5
Figure 5
Inter-batch RT drifts in the ELEMENT study. (A) Boxplot of reported batch RT standard deviations across discrete chromatographic regions. (B) EIC of three similar-mass (m/z = 332.332) compounds, with a random shift observed in batch 4. (C) RT drifting of one compound (m/z = 650.6453) from batch 1 (29.4 min) to batch 8 (28.9 min).
See this image and copyright information in PMC

References

    1. Schrimpe-Rutledge A.C., Codreanu S.G., Sherrod S.D., McLean J.A. Untargeted Metabolomics Strategies—Challenges and Emerging Directions. J. Am. Soc. Mass. Spectrom. 2016;27:1897–1905. doi: 10.1007/s13361-016-1469-y. - DOI - PMC - PubMed
    1. Smith R., Ventura D., Prince J.T. LC-MS Alignment in Theory and Practice: A Comprehensive Algorithmic Review. Brief. Bioinform. 2015;16:104–117. doi: 10.1093/bib/bbt080. - DOI - PubMed
    1. Boswell P.G., Schellenberg J.R., Carr P.W., Cohen J.D., Hegeman A.D. A Study on Retention “Projection” as a Supplementary Means for Compound Identification by Liquid Chromatography-Mass Spectrometry Capable of Predicting Retention with Different Gradients, Flow Rates, and Instruments. J. Chromatogr. A. 2011;1218:6732–6741. doi: 10.1016/j.chroma.2011.07.105. - DOI - PubMed
    1. Christin C., Smilde A.K., Hoefsloot H.C.J., Suits F., Bischoff R., Horvatovich P.L. Optimized Time Alignment Algorithm for LC−MS Data: Correlation Optimized Warping Using Component Detection Algorithm-Selected Mass Chromatograms. Anal. Chem. 2008;80:7012–7021. doi: 10.1021/ac800920h. - DOI - PubMed
    1. Abate-Pella D., Freund D.M., Ma Y., Simón-Manso Y., Hollender J., Broeckling C.D., Huhman D.V., Krokhin O.V., Stoll D.R., Hegeman A.D., et al. Retention Projection Enables Accurate Calculation of Liquid Chromatographic Retention Times across Labs and Methods. J. Chromatogr. A. 2015;1412:43–51. doi: 10.1016/j.chroma.2015.07.108. - DOI - PMC - PubMed