MALDI IMS-Derived Molecular Contour Maps: Augmenting Histology Whole-Slide Images.

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.

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

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.

Spatially Targeted Proteomics of the Host-Pathogen Interface during Staphylococcal Abscess Formation.

Staphylococcus aureus is a common cause of invasive and life-threatening infections that are often multidrug resistant. To develop novel treatment approaches, a detailed understanding of the complex host-pathogen interactions during infection is essential. This is particularly true for the molecular processes that govern the formation of tissue abscesses, as these heterogeneous structures are important contributors to staphylococcal pathogenicity. To fully characterize the developmental process leading to mature abscesses, temporal and spatial analytical approaches are required. Spatially targeted proteomic technologies such as micro-liquid extraction surface analysis offer insight into complex biological systems including detection of bacterial proteins and their abundance in the host environment. By analyzing the proteomic constituents of different abscess regions across the course of infection, we defined the immune response and bacterial contribution to abscess development through spatial and temporal proteomic assessment. The information gathered was mapped to biochemical pathways to characterize the metabolic processes and immune strategies employed by the host. These data provide insights into the physiological state of bacteria within abscesses and elucidate pathogenic processes at the host-pathogen interface.

Sex Differences in the Genetic Architecture of Alzheimer's Disease.

PURPOSE OF REVIEW: Summarize sex-specific contributors to the genetic architecture of Alzheimer's disease (AD). RECENT FINDINGS: There are sex differences in the effects of Apolipoprotein E (APOE), genes along the APOE pathway, and genes along the neurotrophic signaling pathway in predicting AD. Reported sex differences are largely driven by stronger associations among females. Evidence also suggests that genetic predictors of amyloidosis are largely shared across sexes, while sex-specific genetic effects emerge downstream of amyloidosis and drive the clinical manifestation of AD. SUMMARY: There is a lack of comprehensive assessments of sex differences in genome-wide analyses of AD and a need for more systematic reporting a sex-stratified genetic effects. The emerging emphasis on sex as a biological variable provides an opportunity for transdisciplinary collaborations aimed at addressing major analytical challenges that have hampered advancements in the field. Ultimately, sex-specific genetic association studies represent a logical first step towards precision medicine.

Beyond discrimination: A comparison of calibration methods and clinical usefulness of predictive models of readmission risk.

BACKGROUND: Prior to implementing predictive models in novel settings, analyses of calibration and clinical usefulness remain as important as discrimination, but they are not frequently discussed. Calibration is a model's reflection of actual outcome prevalence in its predictions. Clinical usefulness refers to the utilities, costs, and harms of using a predictive model in practice. A decision analytic approach to calibrating and selecting an optimal intervention threshold may help maximize the impact of readmission risk and other preventive interventions. OBJECTIVES: To select a pragmatic means of calibrating predictive models that requires a minimum amount of validation data and that performs well in practice. To evaluate the impact of miscalibration on utility and cost via clinical usefulness analyses. MATERIALS AND METHODS: Observational, retrospective cohort study with electronic health record data from 120,000 inpatient admissions at an urban, academic center in Manhattan. The primary outcome was thirty-day readmission for three causes: all-cause, congestive heart failure, and chronic coronary atherosclerotic disease. Predictive modeling was performed via L1-regularized logistic regression. Calibration methods were compared including Platt Scaling, Logistic Calibration, and Prevalence Adjustment. Performance of predictive modeling and calibration was assessed via discrimination (c-statistic), calibration (Spiegelhalter Z-statistic, Root Mean Square Error [RMSE] of binned predictions, Sanders and Murphy Resolutions of the Brier Score, Calibration Slope and Intercept), and clinical usefulness (utility terms represented as costs). The amount of validation data necessary to apply each calibration algorithm was also assessed. RESULTS: C-statistics by diagnosis ranged from 0.7 for all-cause readmission to 0.86 (0.78-0.93) for congestive heart failure. Logistic Calibration and Platt Scaling performed best and this difference required analyzing multiple metrics of calibration simultaneously, in particular Calibration Slopes and Intercepts. Clinical usefulness analyses provided optimal risk thresholds, which varied by reason for readmission, outcome prevalence, and calibration algorithm. Utility analyses also suggested maximum tolerable intervention costs, e.g., $1720 for all-cause readmissions based on a published cost of readmission of $11,862. CONCLUSIONS: Choice of calibration method depends on availability of validation data and on performance. Improperly calibrated models may contribute to higher costs of intervention as measured via clinical usefulness. Decision-makers must understand underlying utilities or costs inherent in the use-case at hand to assess usefulness and will obtain the optimal risk threshold to trigger intervention with intervention cost limits as a result.

Manipulating the NF-κB pathway in macrophages using mannosylated, siRNA-delivering nanoparticles can induce immunostimulatory and tumor cytotoxic functions.

Tumor-associated macrophages (TAMs) are critically important in the context of solid tumor progression. Counterintuitively, these host immune cells can often support tumor cells along the path from primary tumor to metastatic colonization and growth. Thus, the ability to transform protumor TAMs into antitumor, immune-reactive macrophages would have significant therapeutic potential. However, in order to achieve these effects, two major hurdles would need to be overcome: development of a methodology to specifically target macrophages and increased knowledge of the optimal targets for cell-signaling modulation. This study addresses both of these obstacles and furthers the development of a therapeutic agent based on this strategy. Using ex vivo macrophages in culture, the efficacy of mannosylated nanoparticles to deliver small interfering RNA specifically to TAMs and modify signaling pathways is characterized. Then, selective small interfering RNA delivery is tested for the ability to inhibit gene targets within the canonical or alternative nuclear factor-kappaB pathways and result in antitumor phenotypes. Results confirm that the mannosylated nanoparticle approach can be used to modulate signaling within macrophages. We also identify appropriate gene targets in critical regulatory pathways. These findings represent an important advance toward the development of a novel cancer therapy that would minimize side effects because of the targeted nature of the intervention and that has rapid translational potential.