# Tech Core 1

> **NIH NIH U54** · JOHNS HOPKINS UNIVERSITY · 2022 · $364,786

## Abstract

Project Summary – Tech 1
The identification of key molecular and cellular events that drive the progression of metastatic breast and
pancreatic cancer could lead to improved mechanistic understanding and diagnostic tools and treatments.
However, the micro-anatomy of human pancreatic/breast tumors – their spatial organization and associated
content in cellular and non-cellular components – is poorly understood as it is intrinsically three-dimensional,
non-symmetric, and highly heterogeneous. Three-dimensional imaging tools that combine structural and spatial
omic information in large volumes are required to locate these metastatic driver events in cancerous tissue.
Specifically, we aim to investigate venous invasion, which is a common route of metastasis of breast and
pancreatic cancer cells. In Tech1, we propose to develop a new 3D multiscale imaging method, CODA, which
will allow us to probe the phenotypic heterogeneity of tumors from the multi-cm to the micron scale via
multiplexing serial imaging and registration/deep-learning algorithms. Our proposed repertoire of CODA imaging
methods addresses deficiencies of state-of-art tissue clearing and 3D imaging methods, including inconsistent
clearing and staining, lack of validation, poor antibody penetration and limited multiplexing capabilities. CODA
bypasses the limitations of optical microscopy, allowing for large volumetric imaging while achieving micron
resolution regardless of the volume of the imaged tissue. Another key advantage of CODA, which is based on
serially cut H&E sections, is that it can readily incorporate other imaging modalities to extract high
cellular/molecular content from the 3D samples. These include immunocytochemistry (CODA+IHC),
immunofluorescence (CODA+IF), and imaging mass cytometry (CODA+IMC). This is particularly important as
many potential molecular and cellular contributors of intravasation/extravasation and cancer cell invasion into
veins, including immune cells and cancer-associated fibroblasts, require specific labels that cannot be detected
in H&E slides. These proposed expanded versions of CODA offer a unique opportunity to produce new 3D
multiplex maps of human PDAC and breast tumors near and far from blood vessels. CODA and its integrated
versions CODA+X will be tested in the RTB units of the Center.

## Key facts

- **NIH application ID:** 10375192
- **Project number:** 1U54CA268083-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Pei-Hsun wu
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $364,786
- **Award type:** 1
- **Project period:** 2021-12-01 → 2026-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10375192

## Citation

> US National Institutes of Health, RePORTER application 10375192, Tech Core 1 (1U54CA268083-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10375192. Licensed CC0.

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