# Orthocoding for Spatial Sequencing

> **NIH NIH R56** · STANFORD UNIVERSITY · 2020 · $394,250

## Abstract

Project Summary
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.

## Key facts

- **NIH application ID:** 10191664
- **Project number:** 1R56HG011231-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Polly Morrell Fordyce
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $394,250
- **Award type:** 1
- **Project period:** 2020-09-10 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10191664

## Citation

> US National Institutes of Health, RePORTER application 10191664, Orthocoding for Spatial Sequencing (1R56HG011231-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10191664. Licensed CC0.

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