Project description:To understand differences of gene expression profiles between Francisella strains RNA profiles of Francisella strains were generated by deep sequencing, in triplicate, using NovaSeq6000. qRT–PCR validation was performed using SYBR Green assays. Our study represents the first detailed differential transcriptomic analysis of Francisella strains , with biologic replicates, generated by RNA-seq technology.
Project description:Perivascular adipose (PVAT and subcutaneous adipose tissue (SubQ) from donors undergoing mitral valve repair/replacement (VR) and coronary artery bypass graft (CABG) surgeries were analyzed. There are 3 SWATH data sets. The first contains CABP PVAT, CABG SubQ, VR PVAT, and VR SubQ. The second includes only CABG PVAT from donors with and without diabetes. The third set includes CABG PVAT and SubQ and VR PVAT. Additionally, there are multiple reaction monitoring data sets comparing CABG PVAT with and without diabetes, CABG SubQ with and without diabetes, CABG PVAT to CABG SubQ both without diabetes, CABG PVAT to CABG SubQ both with diabetes, and CABG PVAT without diabetes to VR PVAT without diabetes.
Project description:Spatially resolved transcriptomics (SRT) produces complex, multi-dimensional gene expression data sets at up to subcellular spatial resolution. While SRT provides powerful datasets to probe biological processes, well-designed computational tools provide the key to extracting value from SRT technology. Currently, no single piece of software facilitates the combined automated analysis, visualisation, and subsequent interaction of single or multi-section SRT data as a desktop application or in an immersive environment. Here we present VR-Omics, a freely available, SRT platform agnostic, stand-alone programme that incorporates an in-built, automated workflow to pre-process and spatially mine SRT data within a user-friendly graphical interface. Benchmarking demonstrates VR-Omics has superior capabilities for seamless end-to-end analyses of SRT data, hence making SRT data processing and mining accessible to users regardless of computational and data handling skills. Importantly, VR-Omics supports comparison between datasets generated using different spatial technologies alongside processing and analysis of multiple 2D or 3D SRT datasets provides a unique environment for biological discovery. Finally, we utilise VR-Omics to uncover the molecular mechanisms that drive the growth of rare paediatric cardiac rhabdomyomas.
Project description:Comparison of expression profiles of strains of Francisella to identify virulence factors We used custom microarrays to detail the global gene expression of four strains of Francisella (Schu4, LVS, OR960246, U112) and identified distinct classes of differentially expressed genes during this process.
Project description:Differential expression in human peripheral blood monocytes between F. novicida-infected and uninfected, and between Francisella tularensis tularensis isolate Schu S4 and uninfected. The goal was to examine genomewide transcriptional reponses to these two strains, and identify differentially-regulated genes that may help explain the virulence of Schu S4. Keywords: Immune Response, Human Monocytes, Bacteria, Francisella
