Vectors derived from adeno-associated virus (AAV) are promising for human gene therapy, including treatment for retinal blindness. One major limitation of AAVs as vectors is that AAV cargo capacity has been considered to be restricted to 4.7 kb. Here we demonstrate that vectors with an AAV5 capsid (i.e., rAAV2/5) incorporated up to 8.9 kb of genome more efficiently than 6 other serotypes tested, independent of the efficiency of the rAAV2/5 production process. Efficient packaging of the large murine Abca4 and human MYO7A and CEP290 genes, which are mutated in common blinding diseases, was obtained, suggesting that this packaging efficiency is independent of the specific sequence packaged. Expression of proteins of the appropriate size and function was observed following transduction with rAAV2/5 carrying large genes. Intraocular administration of rAAV2/5 encoding ABCA4 resulted in protein localization to rod outer segments and significant and stable morphological and functional improvement of the retina in Abca4 -/-mice. This use of rAAV2/5 may be a promising therapeutic strategy for recessive Stargardt disease, the most common form of inherited macular degeneration. The possibility of packaging large genes in AAV greatly expands the therapeutic potential of this vector system.

MicroRNAs (miRNAs) are small noncoding RNAs that control gene expression by inducing RNA cleavage or translational inhibition. Most human miRNAs are intragenic and are transcribed as part of their hosting transcription units. We hypothesized that the expression profiles of miRNA host genes and of their targets are inversely correlated and devised a novel procedure, HOCTAR (host gene oppositely correlated targets), which ranks predicted miRNA target genes based on their anti-correlated expression behavior relative to their respective miRNA host genes. HOCTAR is the first tool for systematic miRNA target prediction that utilizes the same set of microarray experiments to monitor the expression of both miRNAs (through their host genes) and candidate targets. We applied the procedure to 178 human intragenic miRNAs and found that it performs better than currently available prediction softwares in pinpointing previously validated miRNA targets. The high-scoring HOCTAR predicted targets were enriched in Gene Ontology categories, which were consistent with previously published data, as in the case of miR-106b and miR-93. By means of overexpression and loss-of-function assays, we also demonstrated that HOCTAR is efficient in predicting novel miRNA targets and we identified, by microarray and qRT-PCR procedures, 34 and 28 novel targets for miR-26b and miR-98, respectively. Overall, we believe that the use of HOCTAR significantly reduces the number of candidate miRNA targets to be tested compared to the procedures based solely on target sequence recognition. Finally, our data further confirm that miRNAs have a significant impact on the mRNA levels of most of their targets.

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 methodspecific 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.

The web tool is freely available for academic use at http://mantra.tigem.it.