Rapid Multivariate Analysis Approach to Explore Differential Spatial Protein Profiles in Tissue
Kavya Sharman (Vanderbilt University Medical Center, VanderBilt University)
N. Heath Patterson (VanderBilt University)
Andy Weiss (Vanderbilt University Medical Center)
Elizabeth K. Neumann (VanderBilt University)
Emma R. Guiberson (VanderBilt University)
Daniel J. Ryan (Pfizer Inc.)
Danielle B. Gutierrez (VanderBilt University)
Jeffrey M. Spraggins (VanderBilt University)
R. Van de Plas (TU Delft - Team Raf Van de Plas, VanderBilt University)
Eric P. Skaar (Vanderbilt University Medical Center)
Richard M. Caprioli (VanderBilt University)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
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.