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. 2023 May 5;22(5):1394-1405.
doi: 10.1021/acs.jproteome.2c00206. Epub 2022 Jul 18.

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

Kavya Sharman  1   2 , Nathan Heath Patterson  1   3 , Andy Weiss  4 , Elizabeth K Neumann  1   3 , Emma R Guiberson  1   5 , Daniel J Ryan  6 , Danielle B Gutierrez  1 , Jeffrey M Spraggins  1   3   5   7 , Raf Van de Plas  1   3   8 , Eric P Skaar  4   9 , Richard M Caprioli  1   3   5   10
Affiliations

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

Kavya Sharman et al. J Proteome Res. .

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.

Keywords: Staphylococcus aureus; abscess formation; bioinformatics; computational proteomics; host−pathogen interface; machine learning; mass spectrometry; microLESA; proteomics; spatially targeted proteomics.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1.
Figure 1.
Pipeline for spatially targeted proteomics data acquisition and analysis. (A) Protein data were acquired from tissue samples using spatially targeted sample acquisition and then peptides were analyzed using LC-MS/MS. Data preprocessing involved protein identification and quantitation using MaxQuant software. (B) PCA was applied for dimensionality reduction and grouping of correlated and anticorrelated proteins among regions and time points. The PCA-processed data were clustered by k-means and cluster centers examined for protein identifications.
Figure 2.
Figure 2.
S. aureus-infected murine kidney. (A) Graphical depiction of the host–pathogen interface of S. aureus infection within a murine kidney. SAC: staphylococcal abscess community. (B) Summary of the total number of host and pathogen proteins detected.
Figure 3.
Figure 3.
Principal component analysis and k-means clustering results of proteins of an S. aureus-infected murine kidney. (A) PCA was performed on protein LFQ intensity values acquired from 3 regions and 2 time points. This unsupervised approach separates the SAC and interface (left) from the cortex samples with no visible infection (right). (B) Samples also seem to cluster based on biological replicate within the PCA space. (C) There is a separation among the samples 4 and 10 days postinfection within samples acquired from region of infection; this separation is not seen from samples acquired from the cortex where there was no visible infection. (D) k-Means clustering was used to cluster the samples after PCA; k = 4 was determined using silhouette scores as a metric. To aid in interpretation, clusters are labeled by the regions and time points from which samples were collected.
Figure 4.
Figure 4.
Molecular differentiators among regions of S. aureus-infected kidney. (A) All four cluster centers are overlaid with 10% of proteins (n = 29) with highest absolute values labeled. Underlined are the three proteins with overall highest absolute centroid values; * = proteins involved in the tricarboxylic acid cycle, ** = proteins involved in maintaining cell structure and facilitating tissue repair/remodeling.
Figure 5.
Figure 5.
Gene ontology analysis. Gene ontology analysis was performed using the 100 proteins with highest accumulated absolute centroid values. The LFQ intensity for these proteins were normalized across all samples and those with values above 0 were analyzed using the PANTHER classification system based on protein class. Panels A–D are sorted from no infection (cortex 4 DPI and 10 DPI) to most infection (interface 10 DPI and SAC 10 DPI). The total number of proteins in each cluster are as follows: (A) 31, (B) 28, (C) 47, and (D) 48. Panel E shows three protein classes with differences among regions of infection versus no infection, and the total number of proteins in each class.
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