Rapid Multivariate Analysis Approach to Explore Differential Spatial Protein Profiles in Tissue

Spatially targeted proteomics analyzes the proteome of
specific
cell types and functional regions within tissue. While spatial context
is often essential to understanding biological processes, interpreting
sub-region-specific protein profiles can pose a challenge due to the
high-dimensional nature of the data. Here, we develop a multivariate
approach for rapid exploration of differential protein profiles acquired
from distinct tissue regions and apply it to analyze a published spatially
targeted proteomics data set collected from Staphylococcus
aureus-infected murine kidney, 4 and 10 days postinfection.
The data analysis process rapidly filters high-dimensional proteomic
data to reveal relevant differentiating species among hundreds to
thousands of measured molecules. We employ principal component analysis
(PCA) for dimensionality reduction of protein profiles measured by
microliquid extraction surface analysis mass spectrometry. Subsequently, k-means clustering of the PCA-processed data groups samples
by chemical similarity. Cluster center interpretation revealed a subset
of proteins that differentiate between spatial regions of infection
over two time points. These proteins appear involved in tricarboxylic
acid metabolomic pathways, calcium-dependent processes, and cytoskeletal
organization. Gene ontology analysis further uncovered relationships
to tissue damage/repair and calcium-related defense mechanisms. Applying
our analysis in infectious disease highlighted differential proteomic
changes across abscess regions over time, reflecting the dynamic nature
of host–pathogen interactions.