Project description:Data from the VLA lyssavirus genotyping microarray. The array platform for this data is GEO accession GPL8066, and consists of 624 oligos representing two viral families. The data set itself consists of 14 arrays, 7 hybridised with RNA from mice brains infected with 7 genotypes of lyssaviruses, 1 hybridised with RNA from normal mouse brain, and 6 hybridised with RNA from coded samples consisting of infected mouse brains or control mouse brains. Keywords: Lyssavirus genotyping microarray Data from the VLA lyssavirus genotyping microarray. The array platform for this data is GEO accession GPL8066, and consists of 624 oligos representing two viral families. The data set itself consists of 14 arrays, 7 hybridised with RNA from mice brains infected with 7 genotypes of lyssaviruses, 1 hybridised with RNA from normal mouse brain, and 6 hybridised with RNA from coded samples consisting of infected mouse brains or control mouse brains. Statistical analysis of the data was done with DetectiV software (Watson et al., 2007). The median and array methods of normalization were used in the statistical analysis of the results. In the median method, DetectiV software calculates the mean fluorescence for each set of probes and normalised against background fluorescence of all probes, assuming that most probes are not hybridized. The array method utilizes an entire control array, e.g. RNA from a known uninfected animal, as the negative control and all probe values are divided by their respective elements from the control array.

2010-05-18 | E-GEOD-14705 | biostudies-arrayexpress

Nanostring of 3D hiPSC-derived neurons infected with rabies virus-wild type-thai strain (WT-TH) and rabies virus-challenge virus standard 11 (CVS-11) against uninfected controls at 8 and 24 hours post-infection

Project description:This experiment is part of the project that primarily aims to utilize 3D hydrogel-based hiPSC-derived neuronal model to study rabies virus infection in the central nervous system. Having established the optimal 3D neuronal model, we then investigated the growth kinetics of two strains of rabies virus (TH and CVS-11) and comparatively analyzed the 2D and 3D culture models. We performed a gene expression analysis using NanoString to determine whether changes in gene expression could explain the differences in virus growth kinetics of two strains of rabies virus observed between the 2D and 3D neuronal culture models. Gene expression analysis of the neuropathological pathway observed during rabies virus infection demonstrated a vast number of differentially expressed genes in the 3D model as compared to the 2D model.

2023-08-23 | GSE231664 | GEO

Morus alba Raw protein sequence reads

Project description:Morus alba L. Raw protein sequence reads and sequence

2024-02-08 | PXD049451 | iProX

Rabies-Infected Neurons Can Be Transcriptomically Characterized Despite Changes in Gene Expression

Project description:Cortical circuit tracing using modified rabies virus can identify input neurons making direct monosynaptic connections onto neurons of interest. However, challenges remain in our ability to establish the cell type identity of rabies-labeled input neurons. While transcriptomics may offer an avenue to characterize inputs, the extent of rabies-induced transcriptional changes in distinct neuronal cell types remains unclear and whether these changes preclude characterization of rabies-infected neurons according to established transcriptomic cell types is unknown. We used single-nucleus RNA sequencing to survey the gene expression profiles of rabies-infected neurons and assessed their correspondence with established transcriptomic cell types. We demonstrated that when using transcriptome-wide RNA profiles, rabies-infected cortical neurons can be transcriptomically characterized despite global and cell-type-specific rabies-induced transcriptional changes. Notably, we found differential modulation of neuronal marker gene expression, suggesting that caution should be taken when attempting to characterize rabies-infected cells with single genes or small gene sets.

2022-05-25 | GSE196771 | GEO