# Microsurgery Core

> **NIH NIH P01** · WASHINGTON UNIVERSITY · 2024 · $236,250

## Abstract

PROJECT SUMMARY/ABSTRACT
Microsurgical models of murine organ transplantation have played a critical role in advancing our 
understanding of immunological pathways that regulate graft rejection and tolerance. Such 
models are important tools to develop therapies for transplant patients. As substantial differences 
exist between immune responses to various grafts, insights gained in one organ cannot be 
translated to other organs. Our laboratories have developed clinically relevant models of lung 
transplantation and re-transplantation in the mouse as well as novel approaches to image immune 
cell interactions in the murine pulmonary graft in real time. We and others have used these 
approaches to investigate clinically relevant lung-specific alloimmune responses. A dedicated 
microsurgery core was established at the initiation of this program project in 2015. The 
microsurgery core has proven instrumental in facilitating the execution of the experiments. In aim 
1 we will continue to standardize the quality of microsurgical and imaging procedures for all three 
projects in the proposal. In aim 2 we will continue to standardize assessment of graft function and 
tissue harvest for all three projects. In aim 3 we will maintain a system that provides unfettered 
access to microsurgical transplants and coordinates intravital imaging procedures for Projects 1 
and 3.

## Key facts

- **NIH application ID:** 10869909
- **Project number:** 5P01AI116501-10
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Wenjun Li
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $236,250
- **Award type:** 5
- **Project period:** 2015-05-12 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10869909

## Citation

> US National Institutes of Health, RePORTER application 10869909, Microsurgery Core (5P01AI116501-10). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10869909. Licensed CC0.

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