ShinyCell: simple and sharable visualization of single-cell gene expression data.

MOTIVATION: As the generation of complex single-cell RNA sequencing datasets becomes more commonplace it is the responsibility of researchers to provide access to these data in a way that can be easily explored and shared. Whilst it is often the case that data is deposited for future bioinformatic analysis many studies do not release their data in a way that is easy to explore by non-computational researchers. RESULTS: In order to help address this we have developed ShinyCell, an R package that converts single-cell RNA sequencing datasets into explorable and shareable interactive interfaces. These interfaces can be easily customized in order to maximize their usability and can be easily uploaded to online platforms to facilitate wider access to published data. AVAILABILITY AND IMPLEMENTATION: ShinyCell is available at https://github.com/SGDDNB/ShinyCell and https://figshare.com/projects/ShinyCell/100439. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

EpiMogrify Models H3K4me3 Data to Identify Signaling Molecules that Improve Cell Fate Control and Maintenance.

The need to derive and culture diverse cell or tissue types in vitro has prompted investigations on how changes in culture conditions affect cell states. However, the identification of the optimal conditions (e.g., signaling molecules and growth factors) required to maintain cell types or convert between cell types remains a time-consuming task. Here, we developed EpiMogrify, an approach that leverages data from ∼100 human cell/tissue types available from ENCODE and Roadmap Epigenomics consortia to predict signaling molecules and factors that can either maintain cell identity or enhance directed differentiation (or cell conversion). EpiMogrify integrates protein-protein interaction network information with a model of the cell's epigenetic landscape based on H3K4me3 histone modifications. Using EpiMogrify-predicted factors for maintenance conditions, we were able to better potentiate the maintenance of astrocytes and cardiomyocytes in vitro. We report a significant increase in the efficiency of astrocyte and cardiomyocyte differentiation using EpiMogrify-predicted factors for conversion conditions.

Correction to: Molecular Interaction Networks to Select Factors for Cell Conversion.

This chapter was published without including the "Conflict of Interest" section given by the author along with the corrected proof.

Probe capture enrichment next-generation sequencing of complete foot-and-mouth disease virus genomes in clinical samples.

Next-generation sequencing (NGS) techniques offer an unprecedented "step-change" increase in the quantity and quality of sequence data rapidly generated from a sample and can be applied to obtain ultra-deep coverage of viral genomes. This is not possible with the routinely used Sanger sequencing method that gives the consensus reads, or by cloning approaches. In this study, a targeted-enrichment methodology for the simultaneous acquisition of complete foot-and-mouth disease virus (FMDV) genomes directly from clinical samples is presented. Biotinylated oligonucleotide probes (120 nt) were used to capture and enrich viral RNA following library preparation. To create a virus capture panel targeting serotype O and A simultaneously, 18 baits targeting the highly conserved regions of the 8.3 kb FMDV genome were synthesised, with 14 common to both serotypes, 2 specific to serotype O and 2 specific to serotype A. These baits were used to capture and enrich FMDV RNA (as cDNA) from samples collected during one pathogenesis and two vaccine efficacy trials, where pigs were infected with serotype O or A viruses. After enrichment, FMDV-specific sequencing reads increased by almost 3000-fold. The sequence data were used in variant call analysis to identify single nucleotide polymorphisms (SNPs). This methodology was robust in its ability to capture diverse sequences, was shown to be highly sensitive, and can be easily scaled for large-scale epidemiological studies.

Molecular Interaction Networks to Select Factors for Cell Conversion.

The process of identifying sets of transcription factors that can induce a cell conversion can be time-consuming and expensive. To help alleviate this, a number of computational tools have been developed which integrate gene expression data with molecular interaction networks in order to predict these factors. One such approach is Mogrify, an algorithm which ranks transcriptions factors based on their regulatory influence in different cell types and tissues. These ranks are then used to identify a nonredundant set of transcription factors to promote cell conversion between any two cell types/tissues. Here we summarize the important concepts and data sources that were used in the implementation of this approach. Furthermore, we describe how the associated web resource ( www.mogrify.net ) can be used to tailor predictions to specific experimental scenarios, for instance, limiting the set of possible transcription factors and including domain knowledge. Finally, we describe important considerations for the effective selection of reprogramming factors. We envision that such data-driven approaches will become commonplace in the field, rapidly accelerating the progress in stem cell biology.

Computational methods for direct cell conversion.

Directed cell conversion (or transdifferentiation) of one somatic cell-type to another can be achieved by ectopic expression of a set of transcription factors. Since the experimental identification of transcription factors for transdifferentiation is extremely time-consuming and expensive, there are still relatively few transdifferentiations achieved in comparison to the number of human cell-types. However, the growing volume of transcriptional data available and the recent introduction of data-driven algorithmic approaches that predict factors for transdifferentiation holds great promise for accelerating this field. Here we review those computational methods whose in-silico predictions have been experimentally validated, highlighting differences and similarities. Our analysis reveals that the factors predicted by each method tend to be different due to varying source cells used, gene expression quantification and algorithmic steps. We show these differences have an impact on the regulatory influences downstream, with some methods favoring transcription factors regulating developmental progression and others favoring factors regulating mature cell processes. These computational approaches offer a starting point to predict and test novel factors for transdifferentiation. We argue that collecting high-quality gene expression data from single-cells or pure cell-populations across a broader set of cell-types would be necessary to improve the quality and consistency of the in-silico predictions.

Extended Evaluation of Virological, Immunological and Pharmacokinetic Endpoints of CELADEN: A Randomized, Placebo-Controlled Trial of Celgosivir in Dengue Fever Patients.

UNLABELLED: CELADEN was a randomized placebo-controlled trial of 50 patients with confirmed dengue fever to evaluate the efficacy and safety of celgosivir (A study registered at ClinicalTrials.gov, number NCT01619969). Celgosivir was given as a 400 mg loading dose and 200 mg bid (twice a day) over 5 days. Replication competent virus was measured by plaque assay and compared to reverse transcription quantitative PCR (qPCR) of viral RNA. Pharmacokinetics (PK) correlations with viremia, immunological profiling, next generation sequence (NGS) analysis and hematological data were evaluated as exploratory endpoints here to identify possible signals of pharmacological activity. Viremia by plaque assay strongly correlated with qPCR during the first four days. Immunological profiling demonstrated a qualitative shift in T helper cell profile during the course of infection. NGS analysis did not reveal any prominent signature that could be associated with drug treatment; however the phylogenetic spread of patients' isolates underlines the importance of strain variability that may potentially confound interpretation of dengue drug trials conducted during different outbreaks and in different countries. Celgosivir rapidly converted to castanospermine (Cast) with mean peak and trough concentrations of 5727 ng/mL (30.2 µM) and 430 ng/mL (2.3 µM), respectively and cleared with a half-life of 2.5 (± 0.6) hr. Mean viral log reduction between day 2 and 4 (VLR2-4) was significantly greater in secondary dengue than primary dengue (p = 0.002). VLR2-4 did not correlate with drug AUC but showed a trend of greater response with increasing Cmin. PK modeling identified dosing regimens predicted to achieve 2.4 to 4.5 times higher Cmin. than in the CELADEN trial for only 13% to 33% increase in overall dose. A small, non-statistical trend towards better outcome on platelet nadir and difference between maximum and minimum hematocrit was observed in celgosivir-treated patients with secondary dengue infection. Optimization of the dosing regimen and patient stratification may enhance the ability of a clinical trial to demonstrate celgosivir activity in treating dengue fever based on hematological endpoints. A new clinical trial with a revised dosing regimen is slated to start in 2016 (NCT02569827). Furthermore celgosivir's potential value for treatment of other flaviruses such as Zika virus should be investigated urgently. TRIAL REGISTRATION: ClinicalTrials.gov NCT01619969.