# Mapping the cell-type-specific molecular and genetic basis of lupus using single cell multiomics

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $807,498

## Abstract

Abstract
Systemic lupus erythematosus (SLE) is an autoimmune rheumatic disease with elevated prevalence in women
and individuals of Asian, African, and Hispanic ancestries. SLE patients present a broad range of symptoms
across multiple organ systems and differentially respond to treatments. Our central hypothesis is twofold: 1)
genetic drivers of SLE affect gene regulatory mechanisms in specific cell types and activation states, and 2)
cellular drivers responsible for disease initiation and exacerbation may exhibit transcriptional regulatory states
(e.g., epigenomic states) poised to respond to environmental disease triggers. To test this hypothesis, we will
use highly innovative multiplexed multimodal single-cell sequencing to map cell-type-specific epigenomic,
transcriptomic, and surface protein features that stratify patients. When integrated with SLE GWAS data, we will
further fine-map SLE-associated variants and annotate the cellular contexts by which associated variants act
through. Finally, utilizing a novel strategy to sequence capillary blood, we will characterize circulating immune
cells in SLE patients during flare, resolution, and response to discrete treatments.
3

## Key facts

- **NIH application ID:** 10882903
- **Project number:** 1R01AI171184-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Chun Jimmie Ye
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $807,498
- **Award type:** 1
- **Project period:** 2024-02-01 → 2028-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10882903

## Citation

> US National Institutes of Health, RePORTER application 10882903, Mapping the cell-type-specific molecular and genetic basis of lupus using single cell multiomics (1R01AI171184-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10882903. Licensed CC0.

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