Mass Spectrometry Based Proteomics and Metabolomics
Proteomics and Metabolomics are fields of scientific study which combine techniques in purification and separation, mass spectrometry and bioinformatics. In our lab we utilize these tools for the identification and characterization of proteins and small molecules in a variety of biological systems. Our lab focuses on the use and application of analytical techniques such as liquid chromatography and mass spectrometry for the analysis and characterization of proteins and small molecules. Proteomics: The proteome is most commonly defined as the entire complement of proteins expressed in a given cell type or tissue under a given condition. A proteomics experiment involves the digestion of a protein or mixture of proteins into peptides using an enzyme such as trypsin and the analysis of these peptides by mass spectrometry. In a mass spectrometer we can measure the mass of the peptides as well as the masses of fragment ions formed during the analysis. Using bioinformatic tools we can use the resulting lists of experiment peptide masses and fragment ion masses to indentify the protein(s). These measured masses can also be used to identify and map sites of post translational modification or to monitor changes in protein expression under varying conditions. Metabolomics: Our primary focus is on discovery based metabolic profiling of biological fluids. Small molecule metabolites can be extracted from all means of biological fluids (plasma, serum, urine, etc.) to plant extracts and secreted fluids. Separation of metabolites is performed using ultra performance liquid chromatography (UPLC, HPLC or GC), and the mass of each metabolite is measured using mass spectrometry. For each component in each sample (e.g. serum from control vs diseased animals) a mass, intensity, and retention time is recorded and using univariate and multivariate statistical analysis we can identify molecules that are contributing to the separation of sample groups. We have developed open source bioinformatics tools (RAMClustR) to effectively cluster peaks into spectra representing each metabolite detected. These spectra are used to facilitate the process of metabolite annotation through spectral searching of in-house spectral libraries and public/commercial libraries (e.g. NIST LC-MS/MS, Golm, ect.). For more information visit www.PMF.colostate.edu
Ganna A, Fall T, Salihovic S, Lee W, Broeckling CD, Kumar J, Hagg S, Stenemo M, Magnusson PKE, Prenni JE, Lind L, Pawitan Y, Ingelsson E “Large-scale non-targeted metabolomic profiling in three human population-based studies” Metabolomics, (2016) 12:4.
Broeckling CD., Afsar, FA, Neumann S, Ben-Hur A., Prenni JE “RAMClust: A Novel Feature Clustering Method Enables Spectral-Matching-Based Annotation for Metabolomics Data” (2014) Analytical Chemistry 86(14), 6812-6817.
Ganna A, Salihovic S, Sundstrom J, Broeckling CD, Hedman AK, Magnusson PKE, Pedersen NL, Larsson A, Siegbahn A, Zilmer M, Prenni JE, Arnlov J, Lind L, Fall T, Ingelsson E. “Large-scale metabolomic profiling identifies novel biomarkers for incident coronary heart disease” (2014) PLOS Genetics, DOI: 10.1371/journal.pgen.1004801.
Szerlong H, Herman JA, Krause CM, Deluca JG, Skoultchi A, Winger QA, Prenni JE, Hansen JC (2015) “Proteomic characterization of the nucleolar linker histone H1 interaction network” Journal of Molecular Biology, 2015 Jun 5;427(11):2056-71. doi: 10.1016/j.jmb.2015.01.001. Epub 2015 Jan 10.
Adam L. Heuberger; Corey D Broeckling; Dana Sedin; Christian Holbrook; Lindsay Guerdrum; Kaylyn Kirkpatrick; Jessica E Prenni “Evaluation of non-volatile metabolites in beer stored at high temperature and utility as an accelerated method to predict flavor stability” (2016) Food Chemistry, 200, 301-307.
Heuberger, A.L., Broeckling, C.D., Kirkpatrick K.R. and Prenni J.E. (2013) Application of nontargeted metabolite profiling to discover novel markers of quality traits in an advanced population of malting barley. Plant Biotechnol. J., doi: 10.1111/pbi.12122.