Spatial Representation of Single Cell RNA-sequencing Data in Embryonic
Thomas Carroll   (Dallas, TX)
Single cell RNA sequencing datasets have become ubiquitous. Although these datasets contain an enormous amount of data that can be used for hypothesis generation, the data is frequently provided in the form of tSNE plots or Excel datasheets that have limited utility. Neither of these methods provide significant spatial information. We have adapted a technique that allows us to present single cell data in a manner that includes spatial information. This technique requires in situ hybridization of a relatively small number of cell type specific genes to function as landmarks. However, these landmark genes must be hybridized to the same or adjacent tissue sections to allow image registration. This cannot be accomplished using current techniques. Here we propose to establish a protocol for multiplexing mRNA expression (so-called in situ single cell RNA-Seq) allowing us to visualize up to 1024 genes simultaneously on a single tissue section. By carefully selecting a small number of genes that represent a single cell sequencing cluster, we can the map the entire transcriptome from one or more single cell datasets onto a reference image of a kidney thus giving spatial resolution for all expressed genes. Ultimately, this data will be provided as a searchable website that will allow users to make multiple queries including simple gene expression data (virtual in situ hybridization), spatial readout of signaling pathway or transcription factor activity and spatial relationship of ligands and receptors. We feel development of this technique will increase access to single cell RNA-seq data and the facilitation of hypothesis generation and thus accelerate discovery and potential cures for kidney disease.
Data for this report has not yet been released.