were analyzed on BD Aria flow cytometer (BD Biosciences). Data were expressed as percent of gated events. Statistical analysis Since CT scores greater than 35 are considered non-specific [37], miRNAs in which 80% of individual observations had a raw CT score greater than 35 were not included in the final data analysis. Using these filtering criteria, we removed 128 and 152 miRNAs for the plasma and PBMC samples, respectively, from analysis. The internal controls (18S, 5S, snoRNA U38B, snoRNA U43, and snRNA U6) were highly variable in the plasma microvesicles and were significantly different in plasma microvesicles versus peripheral blood mononuclear cells (PBMC). To reduce bias caused by using an arbitrary miRNA as a normalization correction factor, the miRNAs were compared between plasma microvesicles and PBMC based on their relative expression to the overall miRNA expression on each array using median normalization analysis [33]. To test the difference of specific miRNA between plasma microvesicles and PBMC, linear mixed models were used and p-values were generated from the model based on the estimated difference and sample variation. Fold-change was calculated based on the estimated mean difference (2^(2DCT)). MiRNAs were subjected to hierarchical clustering using Euclidean distance based on their relative mean expression. MiRNAs were also ranked based on their raw CT score for each plasma microvesicles and PBMC. Pathway analysis and prediction Predicted miRNA targets were determined using the miRanda algorithm (http://microrna.sanger.ac.uk/targets/v5/) and Tar- getScan v4.2 (http://www.targetscan.org/). Common predicted targets as well as targets from each database were subjected to pathway exploration using the Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, Redwood City, CA). To avoid exceeding the maximum gene list size allowed by IPA program, we limited targets based on each programs assigned score. Therefore scores of at least 17 and 20.31 were considered for miRanda and TargetScan, respectively. Using this software and its accompany- ing interaction database, top-ranked pathways were determined based on the incidence of predicted miRNA targets in a list of canonical pathways provided by the software. IPA also generates the top-ranked networks where the predicted miRNA targets are found; according to gene ontology, the biological functions associated with these networks are provided. Results Peripheral blood microvesicle subpopulations To ensure that our isolation method purified microvesicles, we examined size of the microvesicles by flow cytomtery. To visualize the microvesicles by flow cytometry, the axis for forward scatter (FSC) vs. side scatter (SSC) plot required log-scale (Figure 1A). Flow cytometry gates were set using 2 micron beads (Figure 1A, inset). Notably, the size of the microvesicles analyzed in this study was in the range of the bead standards. We also examined the cellular origin of microvesicles within the peripheral blood of normal healthy individuals. Using flow cytometry, we found that the majority of the peripheral blood microvesicles are platelet-derived (Figure 1B), as previously reported [34]. We also observed a second large population of microvesicles that were derived from mononuclear phagocyte cell lineage. This population was immunostained with antibodies that detected surface antigens on mononuclear phago- cytes. Notably, only a small percentage of the peripheral blood microvesicles were derived from T-cells and neutrophils. We failed to detect vesicles that originated from B-cells (data not shown). Of miRNAs in Blood Microvesicles Figure 1. Analysis of the origin of peripheral blood microves- icles. Peripheral blood microvesicles from healthy donors (n = 10) were analyzed by flow cytometry. (A) The size of the microvesicles was determined by FSC vs. SSC. The gate was set according to 2 micron beads (inset). (B) To determine cell origin, microvesicles were stained for CD3, CD202b (Tie-2), CD66b, CD79a, or CD41a to determine those that originated from T-cells, endothelial cells, neutrophils, B-cells, or platelets. Mononuclear phagocyte-derived microvesicles were positive for CD14, CD206, CCR3, CCR2, or CCR5. Shown is the average % maximum of total gated events6S.E.M. doi:10.1371/journal.pone.0003694.g001 interest, we detected a small subpopulation of microvesicles that expressed surface antigens from endothelial cells. miRNA expression in plasma microvesicles and PBMC To test our hypothesis that miRNAs are contained in the microvesicle compartment within the peripheral blood to enable communication and influence genetic changes between different tissues within the body, we performed miRNA profiling on the purified microvesicles from all cellular origins in the plasma. We analyzed all subpopulations of microvesicles from 51 non-smoking healthy individuals comprising of 27 males and 24 females. Since we hypothesized that there would be differences in miRNA expression between microvesicles and PBMC, we also purified the PBMC from each donor. Real-time PCR analysis was performed to examine the expression of 420 miRNAs. Expression of the internal controls (18S, 5S, snoRNA U38B, snoRNA U43, and snRNA U6) was highly variable in the plasma microvesicles versus peripheral blood mononuclear cells (PBMC) (Figure 2A and 2B). To reduce bias caused by using an arbitrary miRNA as a normalization correction factor and to reduce the sample variations among qRT-PCR arrays, the miRNAs were compared PLoS ONE | www.plosone.org 3 November 2008 | Volume 3 | Issue 11 | e3694