Leveraging Low-Dimensional Structure to Enable Spatial Transcriptomics

Abstract

Information about biological phenotype can be gleaned from a variety of sources. We’ve made rapid progress in the last decade in more and more accurate measurements of one, central source: gene expression levels inside of cells. We’re now able to rapidly and cheaply sequence the content and abundance of RNA transcripts down to single-cell resolution. However, in the process we lose information regarding the spatial context of the cell: where in the tissue it originated from. Techniques have been developed in the last few years to remedy this problem, by incorporating spatial information into gene expression measurements. However, these techniques tend to be restricted to the lab of origin due to their high degree of technical complexity. We aim to alleviate this problem by using low-dimensional structure in gene expression profiles to use low-dimensional experimental measurements that are widely accessible to impute the full, high-dimensional spatial transcriptome.