Project description:50,000 cells were injected orthotopically into the inguinal fat pad of a Nod-Scid-Gamma (NSG) immuno-compromised mouse. Injected cells were 80% unlabelled 4T1 cells (parental population), and 20% ZsGreen-labelled 4T1-T cells (clone isolated in Wagenblast et Al, Nature, 2015). Tumour were allowed to develop for 20 days, and then collected during necropsy. Disaggegated cells were processed through the 10X genomics Single Cell 3' gene expression pipeline. This data is intended as an example dataset for a novel virtual reality viewer for single-cell data described in Bressan et Al, Nat. Cancer, 2021 (submitted)
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.