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

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

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
13 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)1394-1405
JournalJournal of Proteome Research
Volume22
Issue number5
DOIs
Publication statusPublished - 2023

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • abscess formation
  • bioinformatics
  • computational proteomics
  • host-pathogen interface
  • machine learning
  • mass spectrometry
  • microLESA
  • proteomics
  • spatially targeted proteomics
  • Staphylococcus aureus

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