Project description:Purpose: Establish a high-throughput method to transcriptionally define projection neurons, VECTORseq, that reimagines transgenes expressed by widely used retrogradely infecting viruses as multiplexed RNA barcodes that are detected in single-cell sequencing. Methods: mRNA profiles of adult mouse brains Conclusions: Retrograde viruses express mRNA at levels detectable in single-cell sequencing. Different transgenes can be multiplexed in a single sequencing run. VECTORseq identifies both cortical and subcortical projection neurons. VECTORseq defined new superior colliculus and zona incerta projection populations. Established a high-throughput method to transcriptionally define projection neurons, VECTORseq, that reimagines transgenes expressed by widely used retrogradely infecting viruses as multiplexed RNA barcodes that are detected in single-cell sequencing.
Project description:Virophages are small dsDNA viruses dependent on a nucleocytoplasmic large-DNA virus infection of a cellular host for replication. Putative virophages infecting algal hosts are classified together with Polinton-like viruses, transposable elements widely found in algal genomes, yet the lack of isolated strains raises questions about their existence as independent entities. We isolated and characterized a virophage (PgVV-14T) co-infecting Phaeocystis globosa with the Phaeocystis globosa virus-14T (PgV-14T).
Project description:The development of rapid and sensitive assays capable of detecting a wide range of infectious agents is critical for the effective diagnosis of diseases that have multiple etiologies. In recent years, many microarray-based diagnostics have been developed to identify viruses present in clinical specimens in a highly parallel fashion. Unfortunately, the rate of development of algorithms to interpret data generated from such platforms has not been commensurate. In particular, none of the existing interpretive algorithms is capable of utilizing empirical training data in a Bayesian framework. We have developed an interpretive algorithm, VIPR (Viral Identification using a PRobabilistic algorithm), to capitalize on our ability to generate positive control data for analysis of microbial diagnostic arrays. To illustrate this approach, we have focused on the analysis of viruses that cause hemorrhagic fever (HF). To assess the efficacy of VIPR, we hybridized 33 viruses to 100 microarrays and applied our algorithm to this dataset. A microarray composed of nearly 15,000 oligonucleotides was designed using a custom viral taxonomy-based strategy. The performance of VIPR was assessed by performing a leave-one-out cross validation. VIPR was able to identity the infecting virus with an accuracy of 94%. VIPR outperformed previously described algorithms for the set of HF viruses tested. Bayesian interpretative algorithms such as VIPR should be considered for diagnostic microarray applications. In this study, 33 viruses including virtually every known hemorrhagic fever virus and a selection of their close relatives were grown in culture and hybridized to 102 microarrays. In addition, 8 uninfected samples were hybridized (110 total hybridizations). These hybridizations were used to test a novel algorithm for diagnosing the infecting virus from a hybridization pattern.
Project description:Purpose: The goal of this study was to characterize the chromatin binding features and the transcriptional target genes of a new key player during corticogenesis, the transcription factor Tox Methods: DamID was performed using two cell lines, the Neuro-2a and the HEK-293T cells. Experiments were performed in duplicates for each cell line by infecting them with pLgwV5Eco-ToxDam lentiviruses and using the pLgxV5Dam viruses as control. DamID was performed as described in Vogel et al., 2006. Results: Our analysis identifyed ca. 13,000 Tox chromatin binding regions
2015-03-16 | GSE64240 | GEO
Project description:Variant analysis of several viruses infecting Ipomoeba batata and Nicotiana benthamiana