Vascular Health Profile predicts cardiovascular outcomes in patients with diabetes.

BACKGROUND: We previously reported the development of a novel high dimensional cytomic assay, the Vascular Health Profile (VHP) based on measurements of angiogenic circulating hematopoietic stem and progenitor cells (CHSPCAng ) and extracellular vesicles (EVs), that discovered a unique signature, differentiating the vascular status of diabetics and normal healthy controls. Here, we present data from a 3-year follow-up to evaluate the power of the VHP to identify individuals at risk for cardiovascular (CV) events. METHODS: The original data were generated as previously described by measuring a broad panel of progenitor cells and EVs and profiled using cytometric fingerprinting. Subjects were classified into groups according to the occurrence of adjudicated CV events including myocardial infarction, stroke, major adverse cardiovascular events, revascularization, and irregular rhythm. Cross-validated Linear Discriminate Analysis (LDA) models were constructed and used to predict the occurrence of events, and were evaluated for predictive accuracy (AUC, area under the curve) using receiver operating characteristic (ROC) analysis. RESULTS: Over the period of this analysis, follow-up data was obtained on 87 subjects, with 32 events occurring overall, and only in the diabetic group. In all cases, the VHP added significant predictive power, in the form of ROC analysis, for all evaluated outcomes with the exception of irregular rhythm. CONCLUSIONS: The VHP, a relatively simple blood test, can provide sensitive and clinically relevant information on the vascular status of a patient that may be useful for a variety of applications including drug development, clinical risk assessment, and companion diagnostics. © 2015 International Clinical Cytometry Society.

Endothelial cell-, platelet-, and monocyte/macrophage-derived microparticles are elevated in psoriasis beyond cardiometabolic risk factors.

BACKGROUND: Psoriasis, especially when severe, is a risk factor for cardiometabolic disease beyond traditional risk factors. The mechanism of atherogenesis in psoriasis remains unknown. Cell membrane vesicles (ie, microparticles), released upon cell activation or apoptosis, have recently been associated with cardiometabolic disease and may play a pathogenic role. Microparticle levels, particularly from endothelial cells and platelets, are elevated in patients with cardiovascular disorders, metabolic syndrome, other inflammatory diseases, autoimmune conditions, and have been shown to be predictive of cardiovascular outcomes. METHODS AND RESULTS: Concentrations of microparticles with positive expression for any of 7 cell surface markers (Annexin V, CD3, CD31, CD41a, CD64, CD105, and CD144) were measured in blood samples from psoriasis patients (n=53) and control subjects without psoriasis (n=41). Platelet-free plasma was separated from whole blood by one-step centrifugation for microparticle analysis. Microparticles were fluorescently labeled and characterized by flow cytometry. Higher concentrations of CD105 (5.5/µL versus 2.5/µL, P<0.001), CD31 (31/µL versus 18/µL, P=0.002), CD41a (50/µL versus 22/µL, P<0.001), and CD64 (5.0/µL versus 4.1/µL, P=0.02) singly positive microparticles corresponding to endothelial cell-, platelet-, and monocyte/macrophage-derived microparticles, respectively, were found in psoriasis patients compared with controls. These differences persisted after adjustment for traditional cardiometabolic risk factors including body mass index. CONCLUSIONS: Increased microparticle concentrations, independent of cardiometabolic risk factors, in patients with psoriasis suggest that the presence of increased endothelial cell, platelet, and monocyte/macrophage activation with cell turnover may contribute to the heightened atherogenesis associated with psoriasis.

Immunologic consequences of chemotherapy for ovarian cancer: impaired responses to the influenza vaccine.

OBJECTIVES: To examine the effect of chemotherapy for ovarian cancer on immunologic function and to define the effect on the serologic response to the influenza vaccine. METHODS: Under IRB approved protocols, patients with ovarian cancer were administered seasonal trivalent killed influenza vaccines. Peripheral blood was collected for immunologic assessments. Serum was analyzed for hemagglutination inhibition (HAI) antibody titers. Peripheral blood mononuclear cells were isolated to characterize T and B cell populations and function. RESULTS: Thirty-one patients were recruited: 13 in remission receiving a dendritic cell vaccine with or without a single dose of low-dose cyclophosphamide, 3 in remission not receiving treatment, and 15 undergoing standard therapy. Significant effects on T cell and B cell subset distributions were seen. Functional effects were also seen. Few patients were able to mount a 4-fold HAI antibody response. A 4-fold response was observed for H1N1 in 20%, for H3N2 in 26%, and for influenza B in 6%. Pre-existing exposure to influenza was predictive of responders. CONCLUSIONS: Despite CDC recommendations that patients undergoing chemotherapy receive influenza vaccine, there is little evidence to support its serologic effectiveness in this population. Patients with ovarian cancer are almost uniformly unable to mount a meaningful antibody response. These findings have serious implications for future resource allocation for both seasonal and novel pandemic influenza outbreak and understanding the immunologic deficits as a result of chemotherapy may improve patient care.

Endothelial microparticles: sophisticated vesicles modulating vascular function.

Endothelial microparticles (EMPs) belong to a family of extracellular vesicles that are dynamic, mobile, biological effectors capable of mediating vascular physiology and function. The release of EMPs can impart autocrine and paracrine effects on target cells through surface interaction, cellular fusion, and, possibly, the delivery of intra-vesicular cargo. A greater understanding of the formation, composition, and function of EMPs will broaden our understanding of endothelial communication and may expose new pathways amenable for therapeutic manipulation.

Personalized cytomic assessment of vascular health: Evaluation of the vascular health profile in diabetes mellitus.

BACKGROUND: An inexpensive and accurate blood test does not currently exist that can evaluate the cardiovascular health of a patient. This study evaluated a novel high dimensional flow cytometry approach in combination with cytometric fingerprinting (CF), to comprehensively enumerate differentially expressed subsets of pro-angiogenic circulating progenitor cells (CPCs), involved in the repair of vasculature, and microparticles (MPs), frequently involved in inflammation and thrombosis. CF enabled discovery of a unique pattern, involving both MPs and CPCs and generated a personalized signature of vascular health, the vascular health profile (VHP). METHODS: Levels of CPCs and MPs were measured with a broad panel of cell surface markers in a population with atherosclerosis and type 2 diabetes mellitus (DM) and age-similar Healthy controls (HC) using an unbiased computational approach, termed CF. RESULTS: Circulating hematopoietic stem and progenitor cell (CHSPCAng) levels were detected at significantly lower concentrations in DM (P < 0.001), whereas levels of seven phenotypically distinct MPs were present at significantly higher concentrations in DM patients and one MP subset was present at significantly lower concentration in DM patients. Collectively, the combination of CHSPC(Ang) and MP levels was more informative than any one measure alone. CONCLUSIONS: This work provides the basis for a personalized cytomic vascular health profile that may be useful for a variety of applications including drug development, clinical risk assessment and companion diagnostics.

Analysis of B cell subsets following pancreatic islet cell transplantation in a patient with type 1 diabetes by cytometric fingerprinting.

Manual gating of bivariate plots remains the most frequently used data analysis method in flow cytometry. However, gating is operator-dependent and cumbersome, particularly with the increasing complexity of modern multicolor immunophenotyping data. A method that can remove operator bias, enable systematic and thorough analysis of complex high-dimensional data, correlate temporal changes in different subsets and lead to biomarker discovery is needed. Here we apply such a method, called cytometric fingerprinting (CF), to data obtained on peripheral blood B cells from an adult patient with type-1 diabetes who underwent pancreatic islet transplantation. We establish that CF can be used to analyze longitudinal trends in immunophenotypic data, and show that results from CF are comparable to those obtained with traditional gating methods. Both methods reveal the appearance of transitional B cells and subsequent accumulation of more mature B cells following immunosuppression and transplantation. This pattern is consistent with a temporally ordered process of B cell auto-reconstitution. We also show the comparative efficiency of fingerprinting in recognizing relative changes in B cell subsets with respect to time, its ability to couple the data with statistical methods (agglomerative clustering) and its potential to define novel subsets.

Isobaric labeling and tandem mass spectrometry: a novel approach for profiling and quantifying proteins differentially expressed in amniotic fluid in preterm labor with and without intra-amniotic infection/inflammation.

OBJECTIVE: Examination of the amniotic fluid (AF) proteome has been previously attempted to identify useful biomarkers in predicting the outcome of preterm labor (PTL). Isobaric Tag for Relative and Absolute Quantitation (iTRAQ) labeling allows direct ratiometric comparison of relative abundance of identified protein species among multiplexed samples. The purpose of this study was to apply, for the first time, the combination of iTRAQ and tandem mass spectrometry to identify proteins differentially regulated in AF samples of women with spontaneous PTL and intact membranes with and without intra-amniotic infection/inflammation (IAI). METHODS: A cross-sectional study was designed and included AF samples from patients with spontaneous PTL and intact membranes in the following groups: (1) patients without IAI who delivered at term (n = 26); (2) patients who delivered preterm without IAI (n = 25); and (3) patients with IAI (n = 24). Proteomic profiling of AF samples was performed using a workflow involving tryptic digestion, iTRAQ labeling and multiplexing, strong cation exchange fractionation, and liquid chromatography tandem mass spectrometry. Twenty-five separate 4-plex samples were prepared and analyzed. RESULTS: Collectively, 123,011 MS(2) spectra were analyzed, and over 25,000 peptides were analyzed by database search (X!Tandem and Mascot), resulting in the identification of 309 unique high-confidence proteins. Analysis of differentially present iTRAQ reporter peaks revealed many proteins that have been previously reported to be associated with preterm delivery with IAI. Importantly, many novel proteins were found to be up-regulated in the AF of patients with PTL and IAI including leukocyte elastase precursor, Thymosin-like 3, and 14-3-3 protein isoforms. Moreover, we observed differential expression of proteins in AF of patients who delivered preterm in the absence of IAI in comparison with those with PTL who delivered at term including Mimecan precursor, latent-transforming growth factor beta-binding protein isoform 1L precursor, and Resistin. These findings have been confirmed for Resistin in an independent cohort of samples using ELISA. Gene ontology enrichment analysis was employed to reveal families of proteins participating in distinct biological processes. We identified enrichment for host defense, anti-apoptosis, metabolism/catabolism and cell and protein mobility, localization and targeting. CONCLUSIONS: (1) Proteomics with iTRAQ labeling is a profiling tool capable of revealing differential expression of proteins in AF; (2) We discovered 82 proteins differentially expressed in three clinical subgroups of premature labor, 67 which were heretofore unknown. Of particular importance is the identification of proteins differentially expressed in AF from women who delivered preterm in the absence of IAI. This is the first report of the positive identification of biomarkers in this subgroup of patients.

FlowFP: A Bioconductor Package for Fingerprinting Flow Cytometric Data.

A new software package called flowFP for the analysis of flow cytometry data is introduced. The package, which is tightly integrated with other Bioconductor software for analysis of flow cytometry, provides tools to transform raw flow cytometry data into a form suitable for direct input into conventional statistical analysis and empirical modeling software tools. The approach of flowFP is to generate a description of the multivariate probability distribution function of flow cytometry data in the form of a "fingerprint." As such, it is independent of a presumptive functional form for the distribution, in contrast with model-based methods such as Gaussian Mixture Modeling. FlowFP is computationally efficient and able to handle extremely large flow cytometry data sets of arbitrary dimensionality. Algorithms and software implementation of the package are described. Use of the software is exemplified with applications to data quality control and to the automated classification of Acute Myeloid Leukemia.

Proteomic analysis of amniotic fluid to identify women with preterm labor and intra-amniotic inflammation/infection: the use of a novel computational method to analyze mass spectrometric profiling.

OBJECTIVE: Examination of the amniotic fluid proteome has been used to identify biomarkers for intra-amniotic inflammation as well as those that may be useful in predicting the outcome of preterm labor. The purpose of this study was to combine a novel computational method of pattern discovery with mass spectrometric proteomic profiling of amniotic fluid to discover biomarkers of intra-amniotic infection/inflammation (IAI). METHODS: This cross-sectional study included patients with spontaneous preterm labor and intact membranes who delivered at term (n = 59) and those who delivered preterm with IAI (n = 60). Proteomic profiling was performed using surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. A proteomic profile was acquired through multiple simultaneous SELDI conditions, which were combined in a single proteomic 'fingerprint' using a novel computational approach. Classification of patients based on their associated surface-enhanced laser desorption/ionization-time of flight (SELDI-TOF) mass spectra as belonging to either the class of individuals with preterm delivery with IAI or term delivery was accomplished by constructing an empirical model. The first phase in the construction of this empirical model involved the selection of adjustable parameters utilizing a training/testing subset of data. The second phase tested the generalization of the model by utilizing a blinded validation set of patients who were not employed in parameter selection. RESULTS: Gestational age at amniocentesis was not significantly different between the groups. Thirty-nine unique mass spectrometric peaks discriminated patients with preterm labor/delivery with IAI from those with preterm labor and term delivery. In the testing/training dataset, the classification accuracies (averaged over 100 random draws) were: 91.4% (40.2/44) for patients with preterm delivery with IAI, and 91.2% (40.1/44) for term delivery. The overall accuracy of the classification of patients in the validation dataset was 90.3% (28/31). CONCLUSIONS: Proteomic analysis of amniotic fluid allowed the identification of mass spectrometry features, which can distinguish patients with preterm labor with IAI from those with preterm labor without inflammation or infection who subsequently delivered at term.

Cytometric fingerprinting: quantitative characterization of multivariate distributions.

Recent technological advances in flow cytometry instrumentation provide the basis for high-dimensionality and high-throughput biological experimentation in a heterogeneous cellular context. Concomitant advances in scalable computational algorithms are necessary to better utilize the information that is contained in these high-complexity experiments. The development of such tools has the potential to expand the utility of flow cytometric analysis from a predominantly hypothesis-driven mode to one of discovery, or hypothesis-generating research. A new method of analysis of flow cytometric data called Cytometric Fingerprinting (CF) has been developed. CF captures the set of multivariate probability distribution functions corresponding to list-mode data and then "flattens" them into a computationally efficient fingerprint representation that facilitates quantitative comparisons of samples. An experimental and synthetic data were generated to act as reference sets for evaluating CF. Without the introduction of prior knowledge, CF was able to "discover" the location and concentration of spiked cells in ungated analyses over a concentration range covering four orders of magnitude, to a lower limit on the order of 10 spiked events in a background of 100,000 events. We describe a new method for quantitative analysis of list-mode cytometric data. CF includes a novel algorithm for space subdivision that improves estimation of the probability density function by dividing space into nonrectangular polytopes. Additionally it renders a multidimensional distribution in the form of a one-dimensional multiresolution hierarchical fingerprint that creates a computationally efficient representation of high dimensionality distribution functions. CF supports both the generation and testing of hypotheses, eliminates sources of operator bias, and provides an increased level of automation of data analysis.