We use sophisticated mass spectrometers to measure hundreds to thousands of proteins, lipids, and other metabolites from a variety of biological sources (cell culture, tissue, blood, etc.). We develop novel sample preparation and mass spectrometry methods and couple these with innovative computational approaches to identify and quantify bio-analytes of most interest.
Lipids are fundamental components of biological systems and play crucial roles in cellular systems. Besides their structural role as compartment boundaries, they play an intimate role in cell signaling, energy storage, and in modulating key mitochondrial activities such as electron transport and apoptosis initiation. Lipidomics, or the analysis of lipid composition, localization, and activity, is rapidly increasing in importance. The preeminent platform for lipid analysis from complex samples is Mass spectrometry (MS).
We currently use shotgun lipidomic techniques to analyze thousands of lipids in a complex sample. Current projects include
Proteins make up the sophisticated machinery responsible for maintaining cellular life and they form a significant portion of the structure of living organism (e.g., cytoskeleton). We analyze proteomes to identify proteins and track changes in different conditions. Current projects include:
Proteins interact with one another in order to transmit signals from the outside environment (signal transduction), form complex molecular machines, and localize to specific areas of the cell.
Using antibodies to pull down one member of a complex is a fantastic way to identify binding partners. An alternative to Western blotting is to use mass spectrometry to comprehensively identify binding partners. Our lab currently uses a sophisticated hierarchical Bayes estimation of generalized linear mixed effects model (Qspec) to report binding partners with the high sensitivity and robust measures of confidence.
We are working with the the Willardson lab to determine protein complex architecture using chemical crosslinking methods.
Mass spectrometry based proteomic data sets are challenging to analyze due to their enormous size, complexity, and changing specifications. As signatories of The Small Tools Manifesto for Bioinformatics we support the development of modular, well-tested tools that can be combined in new analytical approaches.
Besides a collection of small tools (github projects), we also are the creators/maintainers of: