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Real-Time Digitization of Metabolomics Patterns from a Living System Using Mass Spectrometry

  • Research Article
  • Published:
Journal of The American Society for Mass Spectrometry

Abstract

The real-time quantification of changes in intracellular metabolic activities has the potential to vastly improve upon traditional transcriptomics and metabolomics assays for the prediction of current and future cellular phenotypes. This is in part because intracellular processes reveal themselves as specific temporal patterns of variation in metabolite abundance that can be detected with existing signal processing algorithms. Although metabolite abundance levels can be quantified by mass spectrometry (MS), large-scale real-time monitoring of metabolite abundance has yet to be realized because of technological limitations for fast extraction of metabolites from cells and biological fluids. To address this issue, we have designed a microfluidic-based inline small molecule extraction system, which allows for continuous metabolomic analysis of living systems using MS. The system requires minimal supervision, and has been successful at real-time monitoring of bacteria and blood. Feature-based pattern analysis of Escherichia coli growth and stress revealed cyclic patterns and forecastable metabolic trajectories. Using these trajectories, future phenotypes could be inferred as they exhibit predictable transitions in both growth and stress related changes. Herein, we describe an interface for tracking metabolic changes directly from blood or cell suspension in real-time.

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Acknowledgments

The authors would like to thank Jonathan Hilmer for technical assistance in Mass Spectrometry facility. The authors acknowledge support for this work by National Science Foundation, MCB0646499, MCB102248, Kopriva Graduate Fellowship, and Howard Hughes Medical Institute (HHMI). Montana Microfabrication Facility (MMF). Mass Spectrometry, Proteomics, and Metabolomics Core Facility supported by the Murdock Charitable Trust, INBRE MT grant no. P20 RR-16455-08, NIH grant nos. P20 RR-020185, and P20 RR-024237 from the COBRE Program of the National Center for Research Resources.

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Authors and Affiliations

  1. Department of Chemistry and Biochemistry, Montana State University, 126 Chemistry Biochemistry Bldg., Bozeman, MT, 59717, USA

    Joshua Heinemann, Brigit Noon & Brian Bothner

  2. Electrical and Computer Engineering Department, Montana State University, Bozeman, MT, 59717, USA

    Mohammad J. Mohigmi & David L. Dickensheets

  3. Bioinformatics Core, Montana State University, Bozeman, MT, 59717, USA

    Aurélien Mazurie

  4. Montana Microfabrication Facility, Montana State University, Bozeman, MT, 59717, USA

    David L. Dickensheets

  5. Mass spectrometry, Metabolomics, and Proteomics Core Facility, Montana State University, Bozeman, MT, 59717, USA

    Brian Bothner

Authors
  1. Joshua Heinemann
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  2. Brigit Noon
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  3. Mohammad J. Mohigmi
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  4. Aurélien Mazurie
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  5. David L. Dickensheets
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  6. Brian Bothner
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Correspondence to Brian Bothner.

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Heinemann, J., Noon, B., Mohigmi, M.J. et al. Real-Time Digitization of Metabolomics Patterns from a Living System Using Mass Spectrometry. J. Am. Soc. Mass Spectrom. 25, 1755–1762 (2014). https://doi.org/10.1007/s13361-014-0922-z

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  • DOI: https://doi.org/10.1007/s13361-014-0922-z

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