# Cross-Disease Multi-Modality Mapping of the Human Lung

> **NIH NIH U01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2024 · $747,861

## Abstract

PROJECT SUMMARY
 The tremendous diversity across human lung diseases presents significant clinical challenges, but also
offers remarkable opportunities for capturing high-resolution disease mechanisms to more effectively treat
these conditions. Adhering to the mission of LungMAP phase II, our Research Center has generated, curated,
and made publicly available a rich array of high-quality single nucleus RNAseq and single nucleus ATACseq
datasets, focusing on normal developing lung and pediatric lung diseases. In LungMAP phase III, with the
extension into adult lung diseases, we will leverage our expertise on lung biology and single cell technologies
to embark on direct cross-disease comparisons, a recognized bottleneck in the next-stage disease mechanism
discoveries. We have assembled an interdisciplinary team with strong expertise in lung biology, pulmonology,
surgery, pathology, single cell technology and computational biology, with a track record of working together
within and beyond the LungMAP consortium. Guided by the scientific premise that different lung diseases can
be distinguished by a finite set of signatures, we will test the hypothesis that these diseases differ in cell
type/cell state composition, transcriptomic and epigenomic profiles, signaling and extracellular matrix
dynamics, among other dimensions. Capturing cutting-edge technologies, we present robust preliminary data
demonstrating the feasibility of using single nucleus Multiome (RNAseq and ATACseq from the same nucleus)
and spatial transcriptomic MERFISH (Multiplexed Error-Robust Fluorescence in situ Hybridization)
technologies on tissues procured using an ultra-low ischemia time protocol. Critical to achieving precise cross-
disease comparisons, we show preliminary data demonstrating effective addition of multiplexing to these
technologies, which eliminated batch effect which was the major roadblock in previous comparison efforts. We
expect that the datasets that we will generate and make publicly available in LungMAP phase III will facilitate
paradigm-shifting studies by the collective lung research community.

## Key facts

- **NIH application ID:** 10975746
- **Project number:** 1U01HL175452-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Xin Sun
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $747,861
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10975746

## Citation

> US National Institutes of Health, RePORTER application 10975746, Cross-Disease Multi-Modality Mapping of the Human Lung (1U01HL175452-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10975746. Licensed CC0.

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