The 3D spatial context of a cell determines which genes and RNA isoforms it expresses, enabling specialized cell functions fundamental to multicellular life. In typical single-cell RNA-seq (scRNA-seq), the first step of cell dissociation erases the spatial context of the cell. This flaw creates an urgent need for a technology that has the same throughput of scRNA-seq but also encodes the cells? spatial context. Although a new wave of spatial transcriptomic technologies based on sequencing has emerged recently, all suffer from severe limitations: low efficiency (~1-2% of the Drop-Seq efficiency), providing 2D resolution only, failure to discriminate cell boundaries and requiring specialized or expensive equipment. These limitations are intrinsic and result from their shared reliance on cDNA synthesis in situ by from a solid support. Imaging-based technologies have higher spatial resolution but require more equipment, time for protocol execution, have limited gene measurement throughput, and cannot profile RNA isoforms or other sequence variants. To overcome these limitations in state-of-the-art spatial transcriptomic methods, we propose to develop Orthocode, an innovative paradigm for statistically-driven spatial transcriptomics, grounded in proof-of-principle molecular experiments, and cutting-edge statistical theory. Orthocode achieves > 50x or higher sensitivity compared to current approaches by encoding and recovering spatial information from simple, inexpensive and efficient molecular biology protocols. The experimental Orthocode protocol has two steps: 1) a pool of two types of ?location-encoding oligos? (a) barcoded emitter oligos produce copies of themselves that diffuse locally and (b) ?receptors? record the barcodes of nearby emitters are coupled to cells; 2) cells coupled to location- encoding oligos that have together record the spatial position of the cell, are isolated and input into scRNA-seq workflows, eg. Drop-seq and sequenced. Orthocode then employs a rigorous statistical analysis of the barcode profiles of location encoding oligos to triangulate the location of each sequenced cell. This rigorously reasoned experimental design and prototype development builds Orthocode from the simplest test systems to prototypes that will allow unprecedented spatial transcriptomic resolution in tissues to address a critical unmet need in biomedicine. The Orthocode paradigm can be generalized beyond RNA profiling to spatial measurements of proteins, DNA and epigenetic modifications and is a potential breakthrough innovation in deep-sequencing based spatial ?omics.

Public Health Relevance

The spatially controlled expression of genes and RNA isoforms is essential for development and homeostasis in multicellular organisms and the ability to precisely measure their expression in 3D cells and tissues would have transformative impacts in biomedicine. Current technologies suffer from severe limitations: low efficiency ~ < 0.1% of all RNAs in a cell, providing 2D resolution only, failure to discriminate cell boundaries and requiring specialized or expensive equipment. We propose to develop Orthocode, an innovative paradigm for statistically-driven, simple and low-cost approach to measuring RNA in space at unprecedented scale and depth. Orthocode will allow researchers to address some of the most foundational questions in biomedicine from how genomes function to the mechanistic basis of human disease.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
High Priority, Short Term Project Award (R56)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Sen, Shurjo Kumar
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Stanford University
Schools of Medicine
United States
Zip Code