# Light-Seq: Spatially targeted profiling of transcriptomic states in cells and tissue

> **NIH NIH R01** · HARVARD UNIVERSITY · 2024 · $580,022

## Abstract

Summary
We will develop a new spatial-omics platform, Light-Seq, for spatial indexing of intact biological samples using
light-directed DNA barcoding in fixed cells and tissues followed by ex situ sequencing. Our light-directed
barcoding strategy will enable user-directed, in situ selection of rare, disjoint cell populations for full-
transcriptome sequencing based on morphology, location, or protein expression without dissociation. We will
develop Light-Seq as a spatial-omic DNA barcoding platform capable of extracting the transcriptomic information
from single-cells, scalable to uniquely address thousands of user-defined regions, and can be applied in both
fixed and FFPE clinical samples for direct applications in human health. We envision that the Light-Seq platform
will be a scalable, cost-effective, and flexible approach to spatial transcriptomics that allows the user to define
spatial regions in tissue for NGS sequencing. Light-seq can thus serve as a low barrier-to-entry platform for
spatial transcriptomics for many pathologists and researchers, and would be a key driver for a wider adoption of
spatial transcriptomic tools.

## Key facts

- **NIH application ID:** 10831979
- **Project number:** 5R01HG012926-02
- **Recipient organization:** HARVARD UNIVERSITY
- **Principal Investigator:** Peng Yin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $580,022
- **Award type:** 5
- **Project period:** 2023-05-01 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10831979

## Citation

> US National Institutes of Health, RePORTER application 10831979, Light-Seq: Spatially targeted profiling of transcriptomic states in cells and tissue (5R01HG012926-02). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10831979. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*