# Microsurgery Core

> **NIH NIH P01** · WASHINGTON UNIVERSITY · 2021 · $235,636

## 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:** 10197015
- **Project number:** 5P01AI116501-07
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Wenjun Li
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $235,636
- **Award type:** 5
- **Project period:** 2015-05-12 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10197015

## Citation

> US National Institutes of Health, RePORTER application 10197015, Microsurgery Core (5P01AI116501-07). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10197015. Licensed CC0.

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