Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview

Thanks to innovative sample-preparation and sequencing technologies, gene expression in individual cells can now be measured for thousands of cells in a single experiment. Since its introduction, single-cell RNA sequencing (scRNA-seq) approaches have revolutionized the genomics field as they created unprecedented opportunities for resolving cell heterogeneity by exploring gene expression profiles at a single-cell resolution. However, the rapidly evolving field of scRNA-seq invoked the emergence of various analytics approaches aimed to maximize the full potential of this novel strategy. Unlike population-based RNA sequencing approaches, scRNA seq necessitates comprehensive computational tools to address high data complexity and keep up with the emerging single-cell associated challenges. Despite the vast number of analytical methods, a universal standardization is lacking. While this reflects the fields’ immaturity, it may also encumber a newcomer to blend in. In this review, we aim to bridge over the abovementioned hurdle and propose four ready-to-use pipelines for scRNA-seq analysis easily accessible by a newcomer, that could fit various biological data types. Here we provide an overview of the currently available single-cell technologies for cell isolation and library preparation and a step by step guide that covers the entire canonical analytic workflow to analyse scRNA-seq data including read mapping, quality controls, gene expression quantification, normalization, feature selection, dimensionality reduction, and cell clustering useful for trajectory inference and differential expression. Such workflow guidelines will escort novices as well as expert users in the analysis of complex scRNA-seq datasets, thus further expanding the research potential of single-cell approaches in basic science, and envisaging its future implementation as best practice in the field

The impact of microRNAs on transcriptional heterogeneity and gene co-expression across single embryonic stem cells

MicroRNAs act posttranscriptionally to suppress multiple target genes within a cell population. To what extent this multi-target suppression occurs in individual cells and how it impacts transcriptional heterogeneity and gene co-expression remains unknown. Here we used single-cell sequencing combined with introduction of individual microRNAs. miR-294 and let-7c were introduced into otherwise microRNA-deficient Dgcr8 knockout mouse embryonic stem cells. Both microRNAs induce suppression and correlated expression of their respective gene targets. The two microRNAs had opposing effects on transcriptional heterogeneity within the cell population, with let-7c increasing and miR-294 decreasing the heterogeneity between cells. Furthermore, let-7c promotes, whereas miR-294 suppresses, the phasing of cell cycle genes. These results show at the individual cell level how a microRNA simultaneously has impacts on its many targets and how that in turn can influence a population of cells. The findings have important implications in the understanding of how microRNAs influence the co-expression of genes and pathways, and thus ultimately cell fate

THE MUST MODEL EVALUATION EXERCISE: PATTERNS IN MODEL PERFORMANCE

As part of the COST 732 action more than a dozen different research groups have modelled the MUST experiment, as simulated in a wind tunnel. The model evaluation guidance developed within COST 732 recommends \u27exploratory data analysis\u27 as one of the elements in model validation. Experience has shown that such exploratory analysis is crucial to reveal shortcomings of models that might otherwise pass unnoticed. Conditions are best for detecting common patterns and anomalies if you have a situation where several models are put into a common framework – like the case at hand. The available material provides a unique opportunity to identify and explore patterns within model performance

THE MUST MODEL EVALUATION EXERCISE: STATISTICAL ANALYSIS OF MODELLING RESULTS

The first validation exercise of the COST action 732 lead to a substantial number of simulation results for comparison with the MUST wind tunnel experiments. Validation metrics for selected simulation results of the flow field and the concentrations are presented and compared to the state of the art. In addition mean metrics and corresponding scatter limits are computed from the individual results

Blood transcriptomics of drug-na\uefve sporadic Parkinson's disease patients

BACKGROUND: Parkinson's disease (PD) is a chronic progressive neurodegenerative disorder that is clinically defined in terms of motor symptoms. These are preceded by prodromal non-motor manifestations that prove the systemic nature of the disease. Identifying genes and pathways altered in living patients provide new information on the diagnosis and pathogenesis of sporadic PD. METHODS: Changes in gene expression in the blood of 40 sporadic PD patients and 20 healthy controls ("Discovery set") were analyzed by taking advantage of the Affymetrix platform. Patients were at the onset of motor symptoms and before initiating any pharmacological treatment. Data analysis was performed by applying Ranking-Principal Component Analysis, PUMA and Significance Analysis of Microarrays. Functional annotations were assigned using GO, DAVID, GSEA to unveil significant enriched biological processes in the differentially expressed genes. The expressions of selected genes were validated using RT-qPCR and samples from an independent cohort of 12 patients and controls ("Validation set"). RESULTS: Gene expression profiling of blood samples discriminates PD patients from healthy controls and identifies differentially expressed genes in blood. The majority of these are also present in dopaminergic neurons of the Substantia Nigra, the key site of neurodegeneration. Together with neuronal apoptosis, lymphocyte activation and mitochondrial dysfunction, already found in previous analysis of PD blood and post-mortem brains, we unveiled transcriptome changes enriched in biological terms related to epigenetic modifications including chromatin remodeling and methylation. Candidate transcripts as CBX5, TCF3, MAN1C1 and DOCK10 were validated by RT-qPCR. CONCLUSIONS: Our data support the use of blood transcriptomics to study neurodegenerative diseases. It identifies changes in crucial components of chromatin remodeling and methylation machineries as early events in sporadic PD suggesting epigenetics as target for therapeutic intervention

Identification and Characterization of Two Novel RNA Viruses from Anopheles gambiae Species Complex Mosquitoes

Mosquitoes of the Anopheles gambiae complex display strong preference for human blood-meals and are major malaria vectors in Africa. However, their interaction with viruses or role in arbovirus transmission during epidemics has been little examined, with the exception of O'nyong-nyong virus, closely related to Chikungunya virus. Deep-sequencing has revealed different RNA viruses in natural insect viromes, but none have been previously described in the Anopheles gambiae species complex. Here, we describe two novel insect RNA viruses, a Dicistrovirus and a Cypovirus, found in laboratory colonies of An. gambiae taxa using small-RNA deep sequencing. Sequence analysis was done with Metavisitor, an open-source bioinformatic pipeline for virus discovery and de novo genome assembly. Wild-collected Anopheles from Senegal and Cambodia were positive for the Dicistrovirus and Cypovirus, displaying high sequence identity to the laboratory-derived virus. Thus, the Dicistrovirus (Anopheles C virus, AnCV) and Cypovirus (Anopheles Cypovirus, AnCPV) are components of the natural virome of at least some anopheline species. Their possible influence on mosquito immunity or transmission of other pathogens is unknown. These natural viruses could be developed as models for the study of Anopheles-RNA virus interactions in low security laboratory settings, in an analogous manner to the use of rodent malaria parasites for studies of mosquito anti-parasite immunity

Application of the σ-IASI radiative transfer model to IASI

The paper illustrates the new features of the line-by-line radiative transfer model σ -IASI. The new features are mainly based on the computational optimization of the code and its parallelization. The paper also presents and discusses retrieval for temperature, water vapor and ozone profiles from spectra measured by the Infrared Atmospheric Sounding Interferometer in the tropical belt. Comparison with the ECMWF analysis is also provided, which shows the good quality and accuracy of IASI retrieval products