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.

The molecular identification of species of interest is an important part of an imaging mass spectrometry (IMS) experiment. The high resolution accurate mass capabilities of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) have recently been shown to facilitate the identification of proteins in matrix-assisted laser desorption/ionization (MALDI) IMS. However, these experiments are typically limited to proteins giving rise to ions of relatively low m/z due to difficulties transmitting and measuring large molecular weight ions of low charge states. Herein we have modified the source gas manifold of a commercial MALDI FT-ICR MS to regulate the gas flow and pressure to maximize the transmission of large m/z protein ions through the ion funnel region of the instrument. By minimizing the contribution of off-axis gas disruption to ion focusing and maximizing the effective potential wall confining the ions through pressure optimization, the signal-to-noise ratios (S/N) of most protein species were improved by roughly 1 order of magnitude compared to normal source conditions. These modifications enabled the detection of protein standards up to m/z 24 000 and the detection of proteins from tissue up to m/z 22 000 with good S/N, roughly doubling the mass range for which high quality protein ion images from rat brain and kidney tissue could be produced. Due to the long time-domain transients (>4 s) required to isotopically resolve high m/z proteins, we have used these data as part of an FT-ICR IMS-microscopy data-driven image fusion workflow to produce estimated protein images with both high mass and high spatial resolutions.