Insights into proteomic immune cell signaling and communication via data-driven modeling.

Over the past decade, studies applying data-driven modeling approaches have demonstrated significant contributions toward the integrative understanding of multivariate cell regulatory system operation. Here we review applications of several of these approaches, including principal component analysis, partial least squares regression, partial least squares discriminant analysis, decision trees, and Bayesian networks, and describe the advances they have offered in systems-level understanding of immune cell signaling and communication. We show how these approaches generate novel insights from high-throughput proteomic data, from classification to association to influence to mechanisms. Looking forward, new experimental technologies involving single-cell measurements of cytokine expression beckon extension of these modeling techniques to inference of immune cell-cell communication networks, with a goal of aiding development of improved vaccine therapeutics.

Cellular barcodes for efficiently profiling single-cell secretory responses by microengraving.

We present a method that uses fluorescent cellular barcodes to increase the number of unique samples that can be analyzed simultaneously by microengraving, a nanowell array-based technique for quantifying the secretory responses of thousands of single cells in parallel. Using n different fluorescent dyes to generate 2(n) unique cellular barcodes, we achieved a 2(n)-fold reduction in the number of arrays and quantity of reagents required per sample. The utility of this approach was demonstrated in three applications of interest in clinical and experimental immunology. Using barcoded human peripheral blood mononuclear cells and T cells, we constructed dose-response curves, profiled the secretory behavior of cells treated with mechanistically distinct stimuli, and tracked the secretory behaviors of different lineages of CD4(+) T helper cells. In addition to increasing the number of samples analyzed by generating secretory profiles of single cells from multiple populations in a time- and reagent-efficient manner, we expect that cellular barcoding in combination with microengraving will facilitate unique experimental opportunities for quantitatively analyzing interactions among heterogeneous cells isolated in small groups (~2-5 cells).

Hemodynamic systems analysis of capillary network remodeling during the progression of type 2 diabetes.

OBJECTIVE: Early alterations in the skeletal muscle microvasculature may contribute to the onset and progression of type 2 diabetes (DM2) by limiting insulin and glucose availability to skeletal muscle. Microvascular alterations reported with DM2 are numerous and include impaired endothelium-mediated vasodilation, increased arteriole wall stiffness, and decreased capillary density. Most previous analyses of skeletal muscle microvascular architecture have been limited to skeletal muscle cross sections and thus have not presented an integrated, quantitative analysis of the relative significance of observed alterations to elevated microvascular network resistance and decreased blood flow. In this work, we tested the hypothesis that the onset of diabetes would influence microvascular architecture in a manner that would significantly increase capillary network resistance and reduce blood flow. METHODS AND RESULTS: In whole-mount spinotrapezius muscle capillary networks from Zucker diabetic fatty (ZDF) rats before and after the onset of DM2, we found a significant 37% decrease in microvascular branching and a 19% decrease in microvessel length density associated with the onset of the disease. This was previously indiscernible in skeletal muscle cross-section data. Hemodynamic computational analysis revealed that the changes in DM2 capillary network connectivity result in a significant 44% decrease in computed capillary network flow compared to controls. A hemodynamic sensitivity analysis showed that DM2 networks were predicted to be less robust in their ability to maintain perfused network surface area in the event of upstream terminal arteriole constriction. CONCLUSIONS: This study illustrates that capillary network connectivity is altered by DM2 and this negatively impacts microvascular hemodynamics. This work can serve as a basis for a more quantitative approach to evaluating DM2 microvascular networks and their potential use as an early diagnostic aid and/or method for identifying therapeutic targets.

Systems analysis of small signaling modules relevant to eight human diseases.

Using eight newly generated models relevant to addiction, Alzheimer's disease, cancer, diabetes, HIV, heart disease, malaria, and tuberculosis, we show that systems analysis of small (4-25 species), bounded protein signaling modules rapidly generates new quantitative knowledge from published experimental research. For example, our models show that tumor sclerosis complex (TSC) inhibitors may be more effective than the rapamycin (mTOR) inhibitors currently used to treat cancer, that HIV infection could be more effectively blocked by increasing production of the human innate immune response protein APOBEC3G, rather than targeting HIV's viral infectivity factor (Vif), and how peroxisome proliferator-activated receptor alpha (PPARα) agonists used to treat dyslipidemia would most effectively stimulate PPARα signaling if drug design were to increase agonist nucleoplasmic concentration, as opposed to increasing agonist binding affinity for PPARα. Comparative analysis of system-level properties for all eight modules showed that a significantly higher proportion of concentration parameters fall in the top 15th percentile sensitivity ranking than binding affinity parameters. In infectious disease modules, host networks were significantly more sensitive to virulence factor concentration parameters compared to all other concentration parameters. This work supports the future use of this approach for informing the next generation of experimental roadmaps for known diseases.