Association of MicroRNAs and YRNAs With Platelet Function.

RATIONALE: Platelets shed microRNAs (miRNAs). Plasma miRNAs change on platelet inhibition. It is unclear whether plasma miRNA levels correlate with platelet function. OBJECTIVE: To link small RNAs to platelet reactivity. METHODS AND RESULTS: Next-generation sequencing of small RNAs in plasma revealed 2 peaks at 22 to 23 and 32 to 33 nucleotides corresponding to miRNAs and YRNAs, respectively. Among YRNAs, predominantly, fragments of RNY4 and RNY5 were detected. Plasma miRNAs and YRNAs were measured in 125 patients with a history of acute coronary syndrome who had undergone detailed assessment of platelet function 30 days after the acute event. Using quantitative real-time polymerase chain reactions, 92 miRNAs were assessed in patients with acute coronary syndrome on different antiplatelet therapies. Key platelet-related miRNAs and YRNAs were correlated with platelet function tests. MiR-223 (rp=0.28; n=121; P=0.002), miR-126 (rp=0.22; n=121; P=0.016), and other abundant platelet miRNAs and YRNAs showed significant positive correlations with the vasodilator-stimulated phosphoprotein phosphorylation assay. YRNAs, miR-126, and miR-223 were also among the small RNAs showing the greatest dependency on platelets and strongly correlated with plasma levels of P-selectin, platelet factor 4, and platelet basic protein in the population-based Bruneck study (n=669). A single-nucleotide polymorphism that facilitates processing of pri-miR-126 to mature miR-126 accounted for a rise in circulating platelet activation markers. Inhibition of miR-126 in mice reduced platelet aggregation. MiR-126 directly and indirectly affects ADAM9 and P2Y12 receptor expression. CONCLUSIONS: Levels of platelet-related plasma miRNAs and YRNAs correlate with platelet function tests in patients with acute coronary syndrome and platelet activation markers in the general population. Alterations in miR-126 affect platelet reactivity.

Cluster stability in the analysis of mass cytometry data.

Manual gating has been traditionally applied to cytometry data sets to identify cells based on protein expression. The advent of mass cytometry allows for a higher number of proteins to be simultaneously measured on cells, therefore providing a means to define cell clusters in a high dimensional expression space. This enhancement, whilst opening unprecedented opportunities for single cell-level analyses, makes the incremental replacement of manual gating with automated clustering a compelling need. To this aim many methods have been implemented and their successful applications demonstrated in different settings. However, the reproducibility of automatically generated clusters is proving challenging and an analytical framework to distinguish spurious clusters from more stable entities, and presumably more biologically relevant ones, is still missing. One way to estimate cell clusters' stability is the evaluation of their consistent re-occurrence within- and between-algorithms, a metric that is commonly used to evaluate results from gene expression. Herein we report the usage and importance of cluster stability evaluations, when applied to results generated from three popular clustering algorithms - SPADE, FLOCK and PhenoGraph - run on four different data sets. These algorithms were shown to generate clusters with various degrees of statistical stability, many of them being unstable. By comparing the results of automated clustering with manually gated populations, we illustrate how information on cluster stability can assist towards a more rigorous and informed interpretation of clustering results. We also explore the relationships between statistical stability and other properties such as clusters' compactness and isolation, demonstrating that whilst cluster stability is linked to other properties it cannot be reliably predicted by any of them. Our study proposes the introduction of cluster stability as a necessary checkpoint for cluster interpretation and contributes to the construction of a more systematic and standardized analytical framework for the assessment of cytometry clustering results. © 2016 International Society for Advancement of Cytometry.

Splicing imbalances in basal-like breast cancer underpin perturbation of cell surface and oncogenic pathways and are associated with patients' survival.

Despite advancements in the use of transcriptional information to understand and classify breast cancers, the contribution of splicing to the establishment and progression of these tumours has only recently starting to emerge. Our work explores this lesser known landscape, with special focus on the basal-like breast cancer subtype where limited therapeutic opportunities and no prognostic biomarkers are currently available. Using ExonArray analysis of 176 breast cancers and 9 normal breast tissues we demonstrate that splicing levels significantly contribute to the diversity of breast cancer molecular subtypes and explain much of the differences compared with normal tissues. We identified pathways specifically affected by splicing imbalances whose perturbation would be hidden from a conventional gene-centric analysis of gene expression. We found that a large fraction of them involve cell-to-cell communication, extracellular matrix and transport, as well as oncogenic and immune-related pathways transduced by plasma membrane receptors. We identified 247 genes in which splicing imbalances are associated with clinical patients' outcome, whilst no association was detectable at the gene expression level. These include the signaling gene TGFBR1, the proto-oncogene MYB as well as many immune-related genes such as CCR7 and FCRL3, reinforcing evidence for a role of immune components in influencing breast cancer patients' prognosis.

Modeling stem cell induction processes.

Technology for converting human cells to pluripotent stem cell using induction processes has the potential to revolutionize regenerative medicine. However, the production of these so called iPS cells is still quite inefficient and may be dominated by stochastic effects. In this work we build mass-action models of the core regulatory elements controlling stem cell induction and maintenance. The models include not only the network of transcription factors NANOG, OCT4, SOX2, but also important epigenetic regulatory features of DNA methylation and histone modification. We show that the network topology reported in the literature is consistent with the observed experimental behavior of bistability and inducibility. Based on simulations of stem cell generation protocols, and in particular focusing on changes in epigenetic cellular states, we show that cooperative and independent reaction mechanisms have experimentally identifiable differences in the dynamics of reprogramming, and we analyze such differences and their biological basis. It had been argued that stochastic and elite models of stem cell generation represent distinct fundamental mechanisms. Work presented here suggests an alternative possibility that they represent differences in the amount of information we have about the distribution of cellular states before and during reprogramming protocols. We show further that unpredictability and variation in reprogramming decreases as the cell progresses along the induction process, and that identifiable groups of cells with elite-seeming behavior can come about by a stochastic process. Finally we show how different mechanisms and kinetic properties impact the prospects of improving the efficiency of iPS cell generation protocols.