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

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 a...

Full description

Saved in:
Bibliographic Details
Published in:Journal of the American Society for Mass Spectrometry 2014-10, Vol.25 (10), p.1755-1762
Main Authors: Heinemann, Joshua, Noon, Brigit, Mohigmi, Mohammad J., Mazurie, Aurélien, Dickensheets, David L., Bothner, Brian
Format: Article
Language:English
Subjects:
Abundance
Analytical Chemistry
Bacteria
Bioinformatics
Biotechnology
Chemistry
Chemistry and Materials Science
Digitization
E coli
Escherichia coli - metabolism
Extraction
Mass spectrometry
Mass Spectrometry - methods
Metabolism
Metabolites
Metabolomics - instrumentation
Metabolomics - methods
Microfluidic Analytical Techniques - instrumentation
Models, Biological
Monitoring
Organic Chemistry
Pattern analysis
Proteomics
Real time
Research Article
Scientific imaging
Signal processing
Spectroscopy
Trajectories
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. Figure ᅟ
ISSN:1044-0305
1879-1123
DOI:10.1007/s13361-014-0922-z