# Data Analysis Core

> **NIH NIH U54** · UNIVERSITY OF FLORIDA · 2020 · $48,664

## Abstract

Three-dimensional (3D) representations of tissues can be readily understood by the human brain and are the
most informative and accurate way to quantitatively and comprehensively study cellular state and its
relationships in health and disease. To generate 3D tissue representations, molecular measurements at single
cell resolution are needed. These measurements can be performed directly from intact tissue, or alternatively,
serial sections of a tissue can be generated, measured and assembled into a 3D object. Equally important to
data generation are powerful computational tools that enable first, integration of various data types with
different resolutions into multiscale 3D tissue volumes; second, to identify single cells; and third, to derive
meta-features from such 3D single cell tissue models. The computational tools employed and developed by the
Data Analysis Core will not only enable such analyses of the lymphatic tissues, but will also be generally
applicable to a wide range of molecular data types and tissues. Specifically, the Data Analysis Core will
provide the infrastructure and computational tools to store the data and metadata, to integrate the different
measurement modalities into multi-scale images, to generate 3D voxel representations of tissues, to identify
the single cells in 3D representation, and to determine cell types, their neighborhood and other features. All of
these analyses will be built on an open source computational pipeline (histoCAT), which was developed in the
lab of Dr. Bodenmiller and is emerging as a standard analysis pipeline for highly multiplexed 2D and 3D tissue
data of various types. The Data Analysis Core will use OME-tiff as a standard format for all data and
metadata. Data will be stored in a flexible database (openBIS) that enables straightforward exchange of raw
data, data at any step of processing, and the processing pipeline itself to the HIVE. The structure of data and
metadata storage can be readily harmonized to the needs of the HIVE. Given that all molecular measurements
in our proposal provide single cell resolved information, we will use the single cell as a “bucket” to integrate
different imaging modalities. The images generated by the optical microscopy methods and by imaging mass
cytometry will be segmented, and using cell labels and cellular and tissue features, the different data
modalities will be integrated to generate multiscale, multiparamter images. The multiscale, serial 2D tissue
maps will be registered to build the 3D voxel tissue models. A single cell resolved model will be generated
using 3D segmentation approaches. Many algorithms will then be employed to derive meta-features, such as
cell shapes, patterns of cellular neighborhoods, distances to morphological features, and tissue motifs. These
meta-features can be visualized on the 3D model to support the study of biological phenomena. The proposed
computational pipeline, together with the unprecedented datasets generated of th...

## Key facts

- **NIH application ID:** 9970193
- **Project number:** 5U54AI142766-03
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Bernd Bodenmiller
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $48,664
- **Award type:** 5
- **Project period:** 2018-09-14 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9970193

## Citation

> US National Institutes of Health, RePORTER application 9970193, Data Analysis Core (5U54AI142766-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9970193. Licensed CC0.

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