In recent years, the introduction of massively parallel sequencing platforms for Next Generation Sequencing (NGS) protocols, able to simultaneously sequence hundred thousand DNA fragments, dramatically changed the landscape of the genetics studies. RNA-Seq for transcriptome studies, Chip-Seq for DNA-proteins interaction, CNV-Seq for large genome nucleotide variations are only some of the intriguing new applications supported by these innovative platforms. Among them RNA-Seq is perhaps the most complex NGS application. Expression levels of specific genes, differential splicing, allele-specific expression of transcripts can be accurately determined by RNA-Seq experiments to address many biological-related issues. All these attributes are not readily achievable from previously widespread hybridization-based or tag sequence-based approaches. However, the unprecedented level of sensitivity and the large amount of available data produced by NGS platforms provide clear advantages as well as new challenges and issues. This technology brings the great power to make several new biological observations and discoveries, it also requires a considerable effort in the development of new bioinformatics tools to deal with these massive data files. The paper aims to give a survey of the RNA-Seq methodology, particularly focusing on the challenges that this application presents both from a biological and a bioinformatics point of view.

Metarhizium anisopliae and Beauveria bassiana are ubiquitous insect pathogens and possible plant symbionts, as some strains are endophytic or colonize the rhizosphere. We evaluated 11 strains of M. anisopliae and B. bassiana, and two soil saprophytes (the non-rhizospheric Aspergillus niger and the rhizosphere-competent Trichoderma harzianum) for their ability to germinate in bean root exudates (REs). Our results showed that some generalist strains of M. anisopliae were as good at germinating in RE as T. harzianum, although germination rates of the specialized acridid pathogen Metarhizium acridum and the B. bassiana strains were significantly lower. At RE concentrations of <1 mg ml⁻¹, M. anisopliae strain ARSEF 2575 showed higher germination rates than T. harzianum. Microarrays showed that strain 2575 upregulated 29 genes over a 12 h period in RE. A similar number of genes (21) were downregulated. Upregulated genes were involved in carbohydrate metabolism, lipid metabolism, cofactors and vitamins, energy metabolism, proteolysis, extracellular matrix/cell wall proteins, transport proteins, DNA synthesis, the sexual cycle and stress response. However, 41.3 % of the upregulated genes were hypothetical or orphan sequences, indicating that many previously uncharacterized genes have functions related to saprophytic survival. Genes upregulated in response to RE included the subtilisin Pr1A, which is also involved in pathogenicity to insects. However, the upregulated Mad2 adhesin specifically mediates adhesion to plant surfaces, demonstrating that M. anisopliae has genes for rhizosphere competence that are induced by RE.

Metabolites are emerging as key mediators of crosstalk between metabolic flux, cellular signaling, and epigenetic regulation of cell fate. We found that the nonessential amino acid L-proline (L-Pro) acts as a signaling molecule that promotes the conversion of embryonic stem cells into mesenchymal-like, spindle-shaped, highly motile, invasive pluripotent stem cells. This embryonic-stem-cell-to-mesenchymal-like transition (esMT) is accompanied by a genome-wide remodeling of the H3K9 and H3K36 methylation status. Consistently, L-Pro-induced esMT is fully reversible either after L-Pro withdrawal or by addition of ascorbic acid (vitamin C), which in turn reduces H3K9 and H3K36 methylation, promoting a mesenchymal-like-to-embryonic-stem-cell transition (MesT). These findings suggest that L-Pro, which is produced by proteolytic remodeling of the extracellular matrix, may act as a microenvironmental cue to control stem cell behavior.

ZFP57 is necessary for maintaining repressive epigenetic modifications at Imprinting control regions (ICRs). In mouse embryonic stem cells (ESCs), ZFP57 binds ICRs (ICR_BS) and many other loci (non-ICR_BS). To address the role of ZFP57 on all its target sites, we performed high-throughput and multi-locus analyses of inbred and hybrid mouse ESC lines carrying different gene knockouts. By using an allele-specific RNA-seq approach, we demonstrate that ZFP57 loss results in derepression of the imprinted allele of multiple genes in the imprinted clusters. We also find marked epigenetic differences between ICR_BS and non-ICR_BS suggesting that different cis-acting regulatory functions are repressed by ZFP57 at these two classes of target loci. Overall, these data demonstrate that ZFP57 is pivotal to maintain the allele-specific epigenetic modifications of ICRs that in turn are necessary for maintaining the imprinted expression over long distances. At non-ICR_BS, ZFP57 inactivation results in acquisition of epigenetic features that are characteristic of poised enhancers, suggesting that another function of ZFP57 in early embryogenesis is to repress cis-acting regulatory elements whose activity is not yet required.

The human retina is a specialized tissue involved in light stimulus transduction. Despite its unique biology, an accurate reference transcriptome is still missing. Here, we performed gene expression analysis (RNA-seq) of 50 retinal samples from non-visually impaired post-mortem donors. We identified novel transcripts with high confidence (Observed Transcriptome (ObsT)) and quantified the expression level of known transcripts (Reference Transcriptome (RefT)). The ObsT included 77 623 transcripts (23 960 genes) covering 137 Mb (35 Mb new transcribed genome). Most of the transcripts (92%) were multi-exonic: 81% with known isoforms, 16% with new isoforms and 3% belonging to new genes. The RefT included 13 792 genes across 94 521 known transcripts. Mitochondrial genes were among the most highly expressed, accounting for about 10% of the reads. Of all the protein-coding genes in Gencode, 65% are expressed in the retina. We exploited inter-individual variability in gene expression to infer a gene co-expression network and to identify genes specifically expressed in photoreceptor cells. We experimentally validated the photoreceptors localization of three genes in human retina that had not been previously reported. RNA-seq data and the gene co-expression network are available online (http://retina.tigem.it).

Single-cell RNA-seq (scRNAseq) is a powerful tool to study heterogeneity of cells. Recently, several clustering based methods have been proposed to identify distinct cell populations. These methods are based on different statistical models and usually require to perform several additional steps, such as preprocessing or dimension reduction, before applying the clustering algorithm. Individual steps are often controlled by method-specific parameters, permitting the method to be used in different modes on the same datasets, depending on the user choices. The large number of possibilities that these methods provide can intimidate non-expert users, since the available choices are not always clearly documented. In addition, to date, no large studies have invistigated the role and the impact that these choices can have in different experimental contexts. This work aims to provide new insights into the advantages and drawbacks of scRNAseq clustering methods and describe the ranges of possibilities that are offered to users. In particular, we provide an extensive evaluation of several methods with respect to different modes of usage and parameter settings by applying them to real and simulated datasets that vary in terms of dimensionality, number of cell populations or levels of noise. Remarkably, the results presented here show that great variability in the performance of the models is strongly attributed to the choice of the user-specific parameter settings. We describe several tendencies in the performance attributed to their modes of usage and different types of datasets, and identify which methods are strongly affected by data dimensionality in terms of computational time. Finally, we highlight some open challenges in scRNAseq data clustering, such as those related to the identification of the number of clusters.