# Next-Generation Genomic Imaging Technology

> **NIH NIH UH3** · STANFORD UNIVERSITY · 2020 · $394,748

## Abstract

Single cell genomics has transformed biology by enabling unbiased molecular
identification and characterization of the full panoply of cell types that make up a
tissue. A 'marriage' between this molecular information, which includes an
inventory of all the ligands and receptors expressed by each cell class, and the
accompanying 3D spatial information about them will enable the comprehensive
reconstruction of the active signaling centers within a given tissue. Here, by
continuing a fruitful collaboration between a biologist and molecular biochemist,
we propose to deliver a transformative new technology for 3D multi-dimensional
molecular imaging in intact human tissue. A major strength of our approach is
that we entirely bypass the intrinsic limitations of fluorescent imaging, namely
endogenous autofluorescence, low signal to noise, and restriction to five or fewer
data channels, by using lanthanide resonant energy transfer. By so doing, we will
simultaneously overcome challenges in detection of individual signals and
massively increase throughput. Furthermore, the technology we deliver will be
both biologist 'friendly' and low cost, providing a widely available and practical
platform that will be rapidly and broadly adopted by scientists worldwide.

## Key facts

- **NIH application ID:** 10026446
- **Project number:** 4UH3CA255135-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Tushar Jasubhai DESAI
- **Activity code:** UH3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $394,748
- **Award type:** 4N
- **Project period:** 2018-09-15 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10026446

## Citation

> US National Institutes of Health, RePORTER application 10026446, Next-Generation Genomic Imaging Technology (4UH3CA255135-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10026446. Licensed CC0.

---

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