An automated growth enclosure for metabolic labeling of Arabidopsis thaliana with 13C-carbon dioxide - an in vivo labeling system for proteomics and metabolomics research

Wen Ping Chen, Xiao Yuan Yang, Geoffrey L. Harms, William M. Gray, Adrian D. Hegeman, Jerry D. Cohen

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Background: Labeling whole Arabidopsis (Arabidopsis thaliana) plants to high enrichment with 13C for proteomics and metabolomics applications would facilitate experimental approaches not possible by conventional methods. Such a system would use the plant's native capacity for carbon fixation to ubiquitously incorporate 13C from 13CO2 gas. Because of the high cost of 13CO2 it is critical that the design conserve the labeled gas.Results: A fully enclosed automated plant growth enclosure has been designed and assembled where the system simultaneously monitors humidity, temperature, pressure and 13CO2 concentration with continuous adjustment of humidity, pressure and 13CO2 levels controlled by a computer running LabView software. The enclosure is mounted on a movable cart for mobility among growth environments. Arabidopsis was grown in the enclosure for up to 8 weeks and obtained on average >95 atom% enrichment for small metabolites, such as amino acids and >91 atom% for large metabolites, including proteins and peptides.Conclusion: The capability of this labeling system for isotope dilution experiments was demonstrated by evaluation of amino acid turnover using GC-MS as well as protein turnover using LC-MS/MS. Because this 'open source' Arabidopsis 13C-labeling growth environment was built using readily available materials and software, it can be adapted easily to accommodate many different experimental designs.

Original languageEnglish (US)
Article number9
JournalProteome Science
Volume9
DOIs
StatePublished - Feb 10 2011

Bibliographical note

Funding Information:
Protein mass spectrometry was conducted at the Center for Mass Spectrometry and Proteomics at the University of Minnesota and we thank both Todd Markowski and Bruce Witthuhn for their help with sample preparation and LC-MS/MS analysis. We also acknowledge Sanford Weisberg and Aaron Rendahl from the School of Statistics and Thomas F. McGowan from the Center for Mass Spectrometry and Proteomics for their contributions toward development of methods for amino acid and protein data analysis. We thank Michael Emerick for his help with the construction of the enclosure system and Doug Brinkman for the assistance with light spectrum measurement. We are grateful for funding provided by the NSF Plant Genome Research Program grants DBI-0606666 and IOS-0923960, as well as NSF grant IOS-0820940 and by the Gordon and Margaret Bailey Endowment for Environmental Horticulture.

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