New methods to characterize marine organic matter
Marine organic matter is a complex collection of reduced carbon compounds that contain heteroatoms such as oxygen, nitrogen, phosphorus and sulfur. Given the heterogeneous composition of marine organic matter, it is not surprising that full compositional analysis has been elusive and that analytical challenges for organic matter characterization remain. We are actively improving our ability to understand marine organic matter through the development and application of novel analytical methods.
Our method development takes multiple paths. First, we seek to increase our extraction of low molecular weight organic compounds from seawater. Second, we are working to improve the methods used to characterize marine organic matter. We rely on ultrahigh resolution mass spectrometry to characterize intact polar molecules. In recent years the lab has evolved from using direct infusion mass spectrometry datasets (see for example Kujawinski et al. 2009) towards liquid chromatrography-based methods for marine organic matter (see details provided in Kido Soule et al. 2015).
Finally, ultrahigh resolution mass spectrometers produce large and complex datasets. Data analysis occupies a significant amount of time and effort. We are constantly assessing novel means to consider mass spectrometry data as we explore the role of marine organic matter within biogeochemical processes. Most recently, McLean and Kujawinski (2020) presents AutoTuner, a tool that facilitates data processing of untargeted metabolomics data.
Our sample analysis is all done within the WHOI FT-MS facility, a facility that is directed by Liz Kujawinski and managed by Melissa Kido Soule.
This work is funded by the Gordon and Betty Moore Foundation and the MIT Center for Microbiome Informatics and Therapeutics.
Kujawinski Lab members: Melissa Kido Soule, Krista Longnecker, Brittany Widner, Craig McLean
Collaborators: Mary Ann Moran (University of Georgia) and Eric Alm (Massachusetts Institute of Technology).
McLean, C. and E. B. Kujawinski (2020). AutoTuner: High fidelity, robust, and rapid parameter selection for metabolomics data processing. Analytical Chemistry. DOI: 10.1021/acs.analchem.9b04804 (link to publication)
Johnson W.M., Kido Soule M.C., and Kujawinski E.B. (2017) Interpreting the impact of matrix on extraction efficiency and instrument response in a targeted metabolomics method. Limnology and Oceanography Methods 15: 417-428. link to publication
Longnecker, K. and E. B. Kujawinski (2017). Mining mass spectrometry data: Using new computational tools to find novel organic compounds in complex environmental mixtures. Organic Geochemistry 110:92-99. link to publication
Kujawinski, E. B., K. Longnecker, K. L. Barott, R. J. M. Weber and M. C. Kido Soule (2016). Microbial community structure affects marine dissolved organic matter composition. Frontiers in Marine Science 3, link to publication
Longnecker, K. and E. B. Kujawinski (2016). Using network analysis to discern compositional patterns in ultrahigh resolution mass spectrometry data of dissolved organic matter. Rapid Communications in Mass Spectrometry 30: 2388-2394. link to publication.
Kido Soule, M. C., K. Longnecker, W. M. Johnson and E. B. Kujawinski (2015). Environmental metabolomics: analytical strategies. Marine Chemistry 177, Part 2: 374-387. link to publication.
Longnecker, K., J. Futrelle, E. Coburn, M. C. Kido Soule and E. B. Kujawinski (2015). Environmental metabolomics: databases and tools for data analysis. Marine Chemistry 177, Part 2:366-373. link to publication