# #2

> **NIH NIH U54** · GENERAL ELECTRIC GLOBAL RESEARCH CTR · 2022 · $499,881

## Abstract

PROJECT SUMMARY/ABSTRACT (DAC)
Skin diseases (including cancer) affect 84.5 million patients in the US alone and cost $75M in medical care costs.
The 2017 report on the National Burden of skin disease highlighted the need for “...prevention and early detection
methods to reduce morbidity and mortality from preventable diseases such as skin cancer and occupational
diseases such as dermatitis and improved diagnostic tools and treatment options for common and rare skin
diseases”. The goal of our Data Analysis Core (DAC) is to address the needs for a multi-marker/multimodal
reference atlas of skin which can ultimately faciliate the development of new prevention and treatment
approaches. Spatial information for various biomolecules in tissue sections will be acquired using multiplexed
fluorescent imaging (MxIF, Cell DIVE), MALDI-IMS for lipidomics and targeted spatial transcriptomics
(NanoString GeoMx). Harmonizing the data derived from these parallel assays and mapping them into a
common 3-dimensional (3D) space can be a major challenge. We have begun to address this challenge under our
current response to intervention (RTI) funding (Award Number UH3CA246594) using C++ and Python-based
solutions. We propose to scale this workflow to a much larger cohort (n=96 patients) using our automated
segmentation and registration pipeline developed under the RTI. We will export our data analysis pipeline to the
GE Research (GE) high-performance computing resource to run our automatic segmentation and 3D-
reconstruction pipeline. Our Data Analysis Core (DAC) will translate the image files from our OSP into 3D
cellular maps and co-register the resulting multi-modal data. Within these maps, we will identify key organ
architecture, cell types and states, and generate density maps for cell types and estimation of cell populations in
UV exposed/not, young vs. aged skin, and by Fitzpatrick scale. We will share these maps and the resulting data
with the HuBMAP Integration, Visualization, and Engagement (HIVE) team using common, interoperable data
formats to coordinate in the creation of an atlas of the skin in context of the human body as whole. In addition,
we will make all to-date and future data and code available on HubMAP GitHub such that 3D-mapping of cells
in other tissues/organs will systematically increase the coverage of the human reference atlas at large.

## Key facts

- **NIH application ID:** 10530828
- **Project number:** 1U54AR081775-01
- **Recipient organization:** GENERAL ELECTRIC GLOBAL RESEARCH CTR
- **Principal Investigator:** Fiona Ginty
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $499,881
- **Award type:** 1
- **Project period:** 2022-09-22 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10530828

## Citation

> US National Institutes of Health, RePORTER application 10530828, #2 (1U54AR081775-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10530828. Licensed CC0.

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