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dc.contributor.authorHeinemann, Joshua
dc.contributor.authorNoon, Brigit
dc.contributor.authorMohigmi, Mohammad J.
dc.contributor.authorMazurie, Aurélien J.
dc.contributor.authorDickensheets, David L.
dc.contributor.authorBothner, Brian
dc.date.accessioned2015-03-26T20:14:12Z
dc.date.available2015-03-26T20:14:12Z
dc.date.issued2014-10
dc.identifier.citationHeinemann, Joshua, Brigit Noon, Mohammad J. Mohigmi, Aurélien Mazurie, David L. Dickensheets, and Brian Bothner. "Real-Time Digitization of Metabolomics Patterns from a Living System Using Mass Spectrometry."Journal of The American Society for Mass Spectrometry 25, no. 10 (2014): 1755-1762. http://dx.doi.org/10.1007/s13361-014-0922-zen_US
dc.identifier.issn1044-0305
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/8953
dc.description.abstractThe 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.en_US
dc.subjectCellular biologyen_US
dc.subjectMaterials scienceen_US
dc.titleReal-Time Digitization of Metabolomics Patterns from a Living System Using Mass Spectrometryen_US
dc.typeArticleen_US
mus.citation.extentfirstpage1755en_US
mus.citation.extentlastpage1762en_US
mus.citation.issue10en_US
mus.citation.journaltitleJournal of The American Society for Mass Spectrometryen_US
mus.citation.volume25en_US
mus.identifier.categoryChemical & Material Sciencesen_US
mus.identifier.categoryEngineering & Computer Scienceen_US
mus.identifier.categoryLife Sciences & Earth Sciencesen_US
mus.identifier.doi10.1007/s13361-014-0922-zen_US
mus.relation.collegeCollege of Letters & Science
mus.relation.collegeCollege of Engineering
mus.relation.collegeCollege of Letters & Scienceen_US
mus.relation.collegeCollege of Engineeringen_US
mus.relation.departmentChemistry & Biochemistry.en_US
mus.relation.departmentMicrobiology & Immunology.en_US
mus.relation.departmentElectrical & Computer Engineering.en_US
mus.relation.universityMontana State University - Bozemanen_US
mus.contributor.orcidBothner, Brian|0000-0003-1295-9609en_US


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