Novel Approaches in Bottom-Up Proteomic Sample Preparation, Acquisition, and Analysis
The use of proteomic mass spectrometry has become a pervasive component of modern biological and biochemical research. The experimental detection and quantitation of proteins is largely accomplished via liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). In this work, we explore approaches to common problems which pervade three core facets of contemporary proteomic biochemistry by LC-MS/MS: sample preparation, data acquisition, and bioinformatic analysis workflow management.
A common step in the sample-preparatory framework is the affinity purification of protein targets, often through the use of antibodies or the protein streptavidin. Avidin proteins such as streptavidin are capable of binding the small molecule biotin with high affinity and specificity. The binding of biotin to streptavidin is oft exploited due to the extremely high affinity and near irreversibility of the interaction. Elution of biotinylated proteins remains inefficient, and many rely on enzymatic digestion, ultimately releasing a large amount of contaminating streptavidin peptides. We explore a method of chemical derivatization which protects streptavidin from tryptic proteolysis, dramatically reducing sample contamination while retaining biotin binding. The method appears generalizable to immunoglobulins antibodies like those against the hemagglutinin epitope.
Relative quantitation of proteins and peptides is often performed by comparing the intensities of many samples in a single chromatographic run through multiplexing provided by isobaric tagging reagents. Quantitation of these isobaric tags is observed after fragmentation of a purified analyte, typically selected by Data Dependent Acquisition in a semi-stochastic manner. We explore a new method of acquisition, Sequential Windowed Acquisition of Reporter Masses (SWARM), a Data Independent Acquisition-like approach to isobaric tagged peptide quantitation. This approach biases machine acquisition toward analytes based on their quantitative trends, allowing biologists to focus instrument time on putative analytes of interest.
Data produced from the multitude of proteomic experiments must be rigorously analyzed to deconvolute the complex aggregate of mass signals before returning actionable interpretation. The expansion of computational tools the for interrogation of LC-MS/MS data has been a boon to the field, and has made many sophisticated and statistically robust analyses available. However, these tools have been left in unfortunately disjointed sets of software packages lacking convenient interoperability. To help address this problem, we created MilkyWay. MilkyWay is a label free proteomic data analysis platform for quantitative comparisons. Powered by an assemblage of utilities wrapped into the Galaxy bioinformatic workflow management system, MilkyWay contains a R/Shiny web application for the interactive definition of experimental design, file upload, and data exploration.