Statistical Bioinformatics Webinar: Guo -- Statistical Method for Spatial Proteomics

Presented by Dr. George Guo, University of Auckland

The unique set of expressed proteins, specific to a particular cell type, location, or
place in time or space, critically underpins organ function and disease states.
The liquid-chromatography mass spectrometry (LC-MS/MS) proteomic method allows for
global characterisation of these proteomes at the expense of spatial information, 
which limits our understanding of disease mechanisms. Matrix-assisted laser
desorption/ionisation mass spectrometry imaging (MALDI-MSI) can survey this
spatial proteomic complexity. But identification and quantification of peptides are
mutually exclusive, primarily due to the typically lower amount of evidence provided
within a given MS imaging coordinate. To address this, we developed HIT-MAP
(High-resolution Informatics Toolbox in MALDI mass-spectrometry imaging Proteomics),
an R-based pipeline for the automated annotation and visualization of proteomic
MALDI-MSI datasets. HIT-MAP uses statistical methods for spatially aware pixel clustering
and m/z feature summarization to perform proteomics annotation via a false discovery
rate-controlled peptide mass fingerprinting and protein coverage analysis pipeline.