# Scale up single-cell technologies to map pain-associated genes and cells across the lifespan

> **NIH NIH DP2** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $2,518,511

## Abstract

Pain is complex and difficult to manage, and current treatments, such as opioids, are not effective. The genes, circuits, and cells regulating pain remain a black box. The knowledge gap in understanding the underlying mechanisms of pain is mainly due to the lack of innovative technologies to map and decode the pain-associated genes, circuits, and cells at high resolution and scale. We will develop novel single-cell technologies that are highly modular to map the genes, circuits, and cells associated with pain. We will utilize a mouse model of postoperative pain to investigate the underlying molecular mechanisms. We will apply these single-cell technologies to systematically investigate genes and cells associated with postoperative pain at scale. With these innovative single-cell technologies, we will be able to generate highly valuable molecular information and identify novel candidate biomarkers and therapeutic targets for pain, which will lead to the development of novel diagnosis and treatment solutions. Meanwhile, these innovative single-cell technologies can be readily applied to investigate other multicellular complex tissues in health and diseases.

## Key facts

- **NIH application ID:** 10580155
- **Project number:** 1DP2TR004354-01
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Jian Shu
- **Activity code:** DP2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $2,518,511
- **Award type:** 1
- **Project period:** 2022-09-15 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10580155

## Citation

> US National Institutes of Health, RePORTER application 10580155, Scale up single-cell technologies to map pain-associated genes and cells across the lifespan (1DP2TR004354-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10580155. Licensed CC0.

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