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.

Grant Number: 1R21GM159205-01
NIH Institute/Center: NIH
Principal Investigator: Eric Brouzes