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.

Imaging mass spectrometry (IMS) provides untargeted, highly multiplexed maps of molecular distributions in tissue. Ion images are routinely presented as heatmaps and can be overlaid onto complementary microscopy images that provide greater context. However, heatmaps use transparency blending to visualize both images, obscuring subtle quantitative differences and distribution gradients. Here, we developed a contour mapping approach that combines information from IMS ion intensity distributions with that of stained microscopy. As a case study, we applied this approach to imaging data from Staphylococcus aureus-infected murine kidney. In a univariate, or single molecular species, use-case of the contour map representation of IMS data, certain lipids colocalizing with regions of infection were selected using Pearson’s correlation coefficient. Contour maps of these lipids overlaid with stained microscopy showed enhanced visualization of lipid distributions and spatial gradients in and around the bacterial abscess as compared to traditional heatmaps. The full IMS data set comprising hundreds of individual ion images was then grouped into a smaller subset of representative patterns using non-negative matrix factorization (NMF). Contour maps of these multivariate NMF images revealed distinct molecular profiles of the major abscesses and surrounding immune response. This contour mapping workflow also enabled a molecular visualization of the transition zone at the host-pathogen interface, providing potential clues about the spatial molecular dynamics beyond what histological staining alone provides. In summary, we developed a new IMS-based contour mapping approach to augment classical stained microscopy images, providing an enhanced and more interpretable visualization of IMS-microscopy multimodal molecular imaging data sets.

Introduction: Although Staphylococcus aureus is the leading cause of biofilm-related infections, the lipidomic distributions within these biofilms is poorly understood. Here, lipidomic mapping of S. aureus biofilm cross-sections was performed to investigate heterogeneity between horizontal biofilm layers. Methods: S. aureus biofilms were grown statically, embedded in a mixture of carboxymethylcellulose/gelatin, and prepared for downstream matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS). Trapped ion mobility spectrometry (TIMS) was also applied prior to mass analysis. Results: Implementation of TIMS led to a ∼ threefold increase in the number of lipid species detected. Washing biofilm samples with ammonium formate (150 mM) increased signal intensity for some bacterial lipids by as much as tenfold, with minimal disruption of the biofilm structure. MALDI TIMS IMS revealed that most lipids localize primarily to a single biofilm layer, and species from the same lipid class such as cardiolipins CL(57:0) – CL(66:0) display starkly different localizations, exhibiting between 1.5 and 6.3-fold intensity differences between layers (n = 3, p < 0.03). No horizontal layers were observed within biofilms grown anaerobically, and lipids were distributed homogenously. Conclusions: High spatial resolution analysis of S. aureus biofilm cross-sections by MALDI TIMS IMS revealed stark lipidomic heterogeneity between horizontal S. aureus biofilm layers demonstrating that each layer was molecularly distinct. Finally, this workflow uncovered an absence of layers in biofilms grown under anaerobic conditions, possibly indicating that oxygen contributes to the observed heterogeneity under aerobic conditions. Future applications of this workflow to study spatially localized molecular responses to antimicrobials could provide new therapeutic strategies.

Bacterial pathogens have evolved virulence factors to colonize, replicate, and disseminate within the vertebrate host. Although there is an expanding body of literature describing how bacterial pathogens regulate their virulence repertoire in response to environmental signals, it is challenging to directly visualize virulence response within the host tissue microenvironment. Multimodal imaging approaches enable visualization of host-pathogen molecular interactions. Here we demonstrate multimodal integration of high spatial resolution imaging mass spectrometry and microscopy to visualize Staphylococcus aureus envelope modifications within infected murine and human tissues. Data-driven image fusion of fluorescent bacterial reporters and matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance imaging mass spectrometry uncovered S. aureus lysyl-phosphatidylglycerol lipids, localizing to select bacterial communities within infected tissue. Absence of lysyl-phosphatidylglycerols is associated with decreased pathogenicity during vertebrate colonization as these lipids provide protection against the innate immune system. The presence of distinct staphylococcal lysyl-phosphatidylglycerol distributions within murine and human infections suggests a heterogeneous, spatially oriented microbial response to host defenses.

Metals play an essential role in biological systems and are required as structural or catalytic co-factors in many proteins. Disruption of the homeostatic control and/or spatial distributions of metals can lead to disease. Imaging technologies have been developed to visualize elemental distributions across a biological sample. Measurement of elemental distributions by imaging mass spectrometry and imaging X-ray fluorescence are increasingly employed with technologies that can assess histological features and molecular compositions. Data from several modalities can be interrogated as multimodal images to correlate morphological, elemental, and molecular properties. Elemental and molecular distributions have also been axially resolved to achieve three-dimensional volumes, dramatically increasing the biological information. In this review, we provide an overview of recent developments in the field of metal imaging with an emphasis on multimodal studies in two and three dimensions. We specifically highlight studies that present technological advancements and biological applications of how metal homeostasis affects human health.

Diseases are characterized by distinct changes in tissue molecular distribution. Molecular analysis of intact tissues traditionally requires preexisting knowledge of, and reagents for, the targets of interest. Conversely, label-free discovery of disease-Associated tissue analytes requires destructive processing for downstream identification platforms. Tissue-based analyses therefore sacrifice discovery to gain spatial distribution of known targets or sacrifice tissue architecture for discovery of unknown targets. To overcome these obstacles, we developed a multimodality imaging platform for discovery-based molecular histology. We apply this platform to a model of disseminated infection triggered by the pathogen Staphylococcus aureus, leading to the discovery of infection-Associated alterations in the distribution and abundance of proteins and elements in tissue in mice. These data provide an unbiased, three-dimensional analysis of how disease affects the molecular architecture of complex tissues, enable culture-free diagnosis of infection through imaging-based detection of bacterial and host analytes, and reveal molecular heterogeneity at the host-pathogen interface.

An understanding of how cells respond to perturbation is essential for biological applications; however, most approaches for profiling cellular response are limited in scope to pre-established targets. Global analysis of molecular mechanism will advance our understanding of the complex networks constituting cellular perturbation and lead to advancements in areas, such as infectious disease pathogenesis, developmental biology, pathophysiology, pharmacology, and toxicology. We have developed a high-throughput multiomics platform for comprehensive, de novo characterization of cellular mechanisms of action. Platform validation using cisplatin as a test compound demonstrates quantification of over 10"000 unique, significant molecular changes in less than 30 days. These data provide excellent coverage of known cisplatin-induced molecular changes and previously unrecognized insights into cisplatin resistance. This proof-of-principle study demonstrates the value of this platform as a resource to understand complex cellular responses in a high-throughput manner.