Poster #RP224
A New Sample and Data Analysis Workflow for Label-free, Iterative Bottom-up Discovery of Protein Differential Expression
Jennifer Sutton*, Michael Athanas**, Rovshan Sadygov***, Celeste Ptak****, Leo Bonilla*
*Thermo BRIMS Center, Cambridge, USA; **The BioTeam, Cambridge, USA; ***Thermo Electron, San Jose, USA; ****Cornell University, Ithaca, USA
Identification of protein/peptide biomarkers in plasma is a daunting
analytical task due to the extremely large concentration dynamic range
and complexity of this biological fluid. Intrinsic to traditional
bottom-up strategies is the further increase in complexity of the
original protein sample as well in the volume and dimensionality of the
mass spectrometry data resulting from such experiments. In this work,
we present an alternative sample and data analysis scheme for the
discovery of protein differential expression in human plasma. This
strategy is based on a robust label-free pipeline (SIEVE) that
comprisises new elements of high-performance LC-FTMS, as well as a
unique integration of the data analysis steps including the use of
new algorithms for chromatographic retention-time alignment, recursive
base peak framing, and targetted Sequest searches.