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

Journal article (2023)

Authors

Kavya Sharman VanderBilt University, Vanderbilt University Medical Center
N. Heath Patterson VanderBilt University
Richard M. Caprioli 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
Raf Van de Plas VanderBilt University, Team Raf Van de Plas - Mechanical, Maritime and Materials Engineering
Eric P. Skaar Vanderbilt University Medical Center
Research Group
Team Raf Van de Plas (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 Kavya Sharman, Nathan Heath Patterson, Andy Weiss, Elizabeth K. Neumann, Emma R. Guiberson, Daniel J. Ryan, Danielle B. Gutierrez, Jeffrey M. Spraggins, Raf Van de Plas, Eric P. Skaar, Richard M. Caprioli
DOI
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https://doi.org/10.1021/acs.jproteome.2c00206
Machine learning Bioinformatics Mass spectrometry Proteomics Staphylococcus aureus Abscess formation Computational proteomics Host-pathogen interface MicroLESA Spatially targeted proteomics
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Published Date

2023

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Raf Van de Plas

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.