Efficient and accessible platform for spatial transcriptomics

Abstract Spatial transcriptomics promises to deliver gene expression at single-cell resolution or lower within the spatial context of the tissue analyzed. It has been enabled by different technological solutions, including array capture, sequential in-situ hybridization, or in-tissue sequencing. Recent advances have focused on increasing spatial resolution at the expense of data quality and ease of use. No method enables accessible and efficient unbiased spatial transcriptomics at single-cell resolution with a high read count per spatial barcode. This limits the ability to delve into meaningful biological questions using a single platform. As a result, investigators have either used complex platforms or combined different methods. This creates a costly barrier of entry to most biomedical researchers. Array capture, e.g. Slide-Seq, is appealing because it is unbiased, leverages sequencer capabilities, and does not require specialized equipment and skills; however, 1) it captures a small amount of nucleic acid molecules per capture bead, and 2) requires advanced techniques to decipher the barcode spatial pattern. We propose to address those limitations by creating a novel capture array that increases the sequencing depth per barcode, and 2) encoding the barcode pattern into sequencing data. Our strategy hinges on two key aspects: 1) increase accessibility to the technology by using a simple format (filter array) and lowering the complexity of the spatial decoding, and 2) provide more sequencing data per capture bead to drive biomedical discoveries. Our platform will contribute to the adoption of spatial genomics by the wider biomedical community.