# Stanford Tissue Mapping Center

> **NIH NIH U54** · STANFORD UNIVERSITY · 2021 · $1,098,710

## Abstract

ABSTRACT: Organ Specific Project
Beyond nutrient absorption, the gastrointestinal tract has wide-ranging effects on the normal
and diseased physiologies of other organ systems, including metabolism, neural function, and
the immune system. A high-resolution map of the bowel would be an invaluable resource to
understand normal bowel function and the perturbations that lead to disease. We propose to
create this map along the length of the small bowel and colon, both of which have nuanced
geographic specializations of function. Using single cell RNA-seq and single cell ATAC-seq, we
will profile the gene expression and regulatory programs that define the complex cell
populations that drive bowel function. We will use CODEX, a highly-multiplexed, antibody-based
mapping method, to define the spatial relationships of these cell populations. These
investigations will be performed on tissues that are preserved and procured in a manner
suitable for human bowel transplantation. Are target milestone is to characterize the small bowel
and colon tissues from a total of 22 individuals over the course of this four-year effort. Our
protocol to obtain tissues from human organ donors whose families have provided broad, open
access consent is in place, and we are actively collecting tissues for other studies.

## Key facts

- **NIH application ID:** 10213803
- **Project number:** 5U54HG010426-04
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** William James Greenleaf
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,098,710
- **Award type:** 5
- **Project period:** 2018-09-19 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10213803

## Citation

> US National Institutes of Health, RePORTER application 10213803, Stanford Tissue Mapping Center (5U54HG010426-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10213803. Licensed CC0.

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