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BACKGROUND: Given the growing interest in using microRNAs (miRNAs) as biomarkers of early disease, establishment of robust protocols and platforms for miRNA quantification in biological fluids is critical. OBJECTIVE: The goal of this multi-center pilot study was to evaluate the reproducibility of NanoString nCounter™ technology when analyzing the abundance of miRNAs in plasma and cystic fluid from patients with pancreatic lesions. METHODS: Using sample triplicates analyzed across three study sites, we assessed potential sources of variability (RNA isolation, sample processing/ligation, hybridization, and lot-to-lot variability) that may contribute to suboptimal reproducibility of miRNA abundance when using nCounterTM, and evaluated expression of positive and negative controls, housekeeping genes, spike-in genes, and miRNAs. RESULTS: Positive controls showed a high correlation across samples from each site (median correlation coefficient, r > 0.9). Most negative control probes had expression levels below background. Housekeeping and spike-in genes each showed a similar distribution of expression and comparable pairwise correlation coefficients of replicate samples across sites. A total of 804 miRNAs showed a similar distribution of pairwise correlation coefficients between replicate samples (p = 0.93). After normalization and selecting miRNAs with expression levels above zero in 80% of samples, 55 miRNAs were identified; heatmap and principal component analysis revealed similar expression patterns and clustering in replicate samples. CONCLUSIONS: Findings from this pilot investigation suggest the nCounter platform can yield reproducible results across study sites. This study underscores the importance of implementing quality control procedures when designing multi-center evaluations of miRNA abundance. [ABSTRACT FROM AUTHOR]

This study presents a voltage-extrapolative charge balance (VECB) controller with charge compensation for flyback converter operating in discontinuous conduction mode (DCM). Compared with conventional charge balance controller, the VECB controller benefits from voltage extrapolation, which improves the transient response to load disturbance. Based on the output voltage differential value, a voltage extrapolator calculates the output voltage after two switching cycles under the influence of discharge. Furthermore, the extrapolated voltage is used to calculate the reference charge for the next switching cycle. The VECB controller eliminates the lag in calculating the reference charge, which improves the transient response to load disturbance. Closed-loop small signal models under VECB and conventional charge balance controls are derived and compared to prove the improvement. Furthermore, charge damping and its influence to the output voltage are intensely studied. To minimise the influence, a constant factor is adopted to carry out charge compensation, which is proved to be adequate. Finally, the effectiveness of VECB controller is verified on a flyback converter prototype. [ABSTRACT FROM AUTHOR]

Summary: Single-cell technologies allow characterization of transcriptomes and epigenomes for individual cells under different conditions and provide unprecedented resolution for researchers to investigate cellular heterogeneity in cancer. The SinCHet (Single Cell Heterogeneity) toolbox is developed in MATLAB and has a graphical user interface (GUI) for visualization and user interaction. It analyzes both continuous (e.g. mRNA expression) and binary omics data (e.g. discretized methylation data). The toolbox does not only quantify cellular heterogeneity using Shannon Profile (SP) at different clonal resolutions but also detects heterogeneity differences using a D statistic between two populations. It is defined as the area under the Profile of Shannon Difference (PSD). This flexible tool provides a default clonal resolution using the change point of PSD detected by multivariate adaptive regression splines model; it also allows user-defined clonal resolutions for further investigation. This tool provides insights into emerging or disappearing clones between conditions, and enables the prioritization of biomarkers for follow-up experiments based on heterogeneity or marker differences between and/or within cell populations. [ABSTRACT FROM AUTHOR]

Background: Tyrosine kinases drive the proliferation and survival of many human cancers. Thus profiling the global state of tyrosine phosphorylation of a tumor is likely to provide a wealth of information that can be used to classify tumors for prognosis and prediction. However, the comprehensive analysis of tyrosine phosphorylation of large numbers of human cancer specimens is technically challenging using current methods. Methodology/Principal Findings: We used a phosphoproteomic method termed SH2 profiling to characterize the global state of phosphotyrosine (pTyr) signaling in human lung cancer cell lines. This method quantifies the phosphorylated binding sites for SH2 domains, which are used by cells to respond to changes in pTyr during signaling. Cells could be grouped based on SH2 binding patterns, with some clusters correlated with EGF receptor (EGFR) or K-RAS mutation status. Binding of specific SH2 domains, most prominently RAS pathway activators Grb2 and ShcA, correlated with EGFR mutation and sensitivity to the EGFR inhibitor erlotinib. SH2 binding patterns also reflected MET activation and could identify cells driven by multiple kinases. The pTyr responses of cells treated with kinase inhibitors provided evidence of distinct mechanisms of inhibition. Conclusions/Significance: This study illustrates the potential of modular protein domains and their proteomic binding profiles as powerful molecular diagnostic tools for tumor classification and biomarker identification. [ABSTRACT FROM AUTHOR]

Stat transcription factors are activated by cytokines and can activate pathways important in oncogenesis. Although previous studies have identified an oncogenic role of Stat3 in lung cancer cells, the role of Stat1 is unclear. Using a mutant of Stat1 with constitutive activity (Stat1C), we examined the effect of persistent Stat1 activity on lung cancer cell growth, survival and gene expression. We identified no significant effect of Stat1C alone or with interferon-γ(IFN-γ) on lung cancer cell growth or survival. Consistent with prior reports, Stat1C expression alone elicited minimal changes in gene expression and required costimulatory IFN-γfor full activity. Using oligonucleotide gene arrays and quantitative real-time PCR, we identified numerous proinflammatory gene products and chemokines regulated by IFN-γStat1C signaling. These results suggest the major role of IFN-γand Stat1 in lung cells is to direct a proinflammatory gene expression program rather than have major effects on cell growth or survival or both. [ABSTRACT FROM AUTHOR]