# TECH Core

> **NIH NIH U54** · UNIVERSITY OF MINNESOTA · 2024 · $420,957

## Abstract

This TECH core of the U54 Center for Multiparametric Imaging of Tumor Immune Microenvironments (C-MITIE)
will develop an integrated toolkit of advanced imaging and data analysis to power quantitative, mechanistic
investigations of immune-microenvironment dynamics in poor prognosis solid tumors. There is great need for
improved imaging methods that can advance understanding of the physical and molecular mechanisms
governing immune infiltration, distribution, and function in native tumor microenvironments. We propose a
number of multiparametric imaging and computational methods for the two research test beds that seek to define
the physical and molecular barriers to effective anti-tumor immunity and immunotherapies. A major theme of
the TECH approach is to use label-free imaging approaches that can characterize and quantitate the interactions
between immune cells and the tumor microenvironment. These label free methods are largely built on the
platform method of multiphoton microscopy and can be used on intact cell and tissue models with minimal
perturbation. T-cell identity and activation will be tracked by metabolic profiling using new fluorescence lifetime
(FLIM) and hyperdimensional imaging (HDIM) approaches. These metabolically sensitive methods will be
complemented by Full-Field Optical Coherence Tomography (FFOCT) to reveal new insight into metabolically
relevant architecture. FLIM based FRET can be used to yield new insights into signaling molecular interactions
relevant to immune-microenvironment dynamics The collagen rich extracellular matrix (ECM) will be queried
with Second Harmonic Generation (SHG) imaging for collagen fiber topology measurement and collagen cross-
linking measurements with Enhanced Backscattering Spectroscopy (EBS). Multiphoton Excitation (MPE)
photochemistry fabrication can be used to create in vitro cell ready models of collagen fiber organization that are
directly based on human data blueprints. Advanced computational analysis methods including algorithmic and
machine learning approaches will be used to examine all multiparametric signals and make correlation between
immune and microenvironment interactions. All imaging and computational methods will be shared not only
widely within the UW and UMN research teams but importantly with the general cancer imaging community using
established hardware and open source software dissemination protocols.

## Key facts

- **NIH application ID:** 10782987
- **Project number:** 5U54CA268069-03
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Kevin William Eliceiri
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $420,957
- **Award type:** 5
- **Project period:** 2021-12-09 → 2026-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10782987

## Citation

> US National Institutes of Health, RePORTER application 10782987, TECH Core (5U54CA268069-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10782987. Licensed CC0.

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