# High-resolution spatial transcriptomics through light patterning

> **NIH NIH R21** · UNIVERSITY OF WASHINGTON · 2022 · $178,107

## Abstract

PROJECT SUMMARY
The cellular composition of a tumor as well as the spatial arrangement of cells within the tumor are major
determinants of the response to therapy and the emergence of resistance. To improve our understanding of
tumor heterogeneity, accelerate the discovery of new drug targets or enable better patient stratification it is thus
necessary to develop tools that can resolve molecularly defined cell types within a tumor and capture their spatial
relationships. Driven by progress in single-cell RNA sequencing (scRNA-seq) technologies, a complete census
of molecularly defined cell types within a tumor is now within reach. However, because scRNA-seq requires
dissociated cells and cannot preserve information about the spatial arrangement of cells in their original context,
it gives an incomplete picture of the relationship between gene expression, cell type identity and tumor
architecture. The need for technologies that measure gene expression in single cells while retaining position
information has long been recognized, but existing solutions have insufficient cellular throughput, spatial
resolution, or gene detection sensitivity. We propose to develop Combinatorial Light-Activated Spatial
Sequencing (CLASSeq), a transformative approach to spatial transcriptomics that overcomes these limitations.
CLASSeq uses patterned light illumination to attach DNA barcodes encoding location information to all cells of
interest within a tissue section, with spatial resolution limited only by the wavelength of light. Spatial barcodes
are sequenced together with cellular transcriptomes after dissociating the tissue into individual cells or nuclei,
and tissue-wide gene expression patterns are computationally recreated. Because sequencing is performed after
dissociation, any established scRNA-seq workflow can be used, enabling high sensitivity and cell throughput. To
achieve high throughput and reproducibility, and facilitate wide adoption, we will work toward automating the
labeling workflow by constructing a prototype instrument that integrates fluidics for barcode delivery with
patterned illumination. To validate our approach and demonstrate its utility to cancer research, CLASSeq will be
used to characterize cellular diversity and organization in Hodgkin lymphoma, a mature B-cell lymphoma in which
the tumor microenvironment niche is critical to the tumor's success for host immune evasion and thus governs
the response or lack thereof to clinical immune modulatory therapies.

## Key facts

- **NIH application ID:** 10341212
- **Project number:** 5R21CA246358-03
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Georg Seelig
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $178,107
- **Award type:** 5
- **Project period:** 2020-03-01 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10341212

## Citation

> US National Institutes of Health, RePORTER application 10341212, High-resolution spatial transcriptomics through light patterning (5R21CA246358-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10341212. Licensed CC0.

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