Rapid Technology Implementation

Multi-Scale 3D Image Analytics for High Dimensional Spatial Mapping of Normal Tissues


The overall goal of this project is to develop open-source software and algorithms for 3-D reconstruction and multi-scale mapping of normal tissues. Another significant goal is to evaluate effects of aging and environmental factors on molecular and structural architecture of skin. The team leverages their mature (TRL8) technology for multiplexed 2-D imaging (Cell DIVE™), and their vast experience in 2-D image analytics and machine learning. High-resolution (subcellular) mapping of biomolecules is implemented using 2-D multiplexed images that are used to reconstruct the 3-D tissue and linked to a lower resolution 3-D optical coherence tomography (OCT) image of the normal tissue. Other cell-level omic data (e.g., RNA FISH) is mapped in the same way. The low-resolution image is mapped back to a higher-level landmark (e.g., organ) as defined by the HuBMAP common coordinate framework (CCF). As outlined, the technologies utilized include several key features that are significant and complementary to existing HuBMAP consortium projects and help to advance the state of the art in 3-D tissue analysis.

The proposed algorithms have several key innovations that will advance the state of the art in 3-D multiplexed tissue image analysis. First, given the large volumes to be analyzed, high throughput isa key requirement of each image analysis algorithm. Second, the proposed algorithms segment the images at multiple scales. The third area of innovation focuses on efficient multi-channel analysis. The project includes the creation of an easy-to-use software tool for assembling and visualizing multiscale tissue data called Tissue Atlas Navigation Graphical Overview (TANGO).

Fast Facts
Project title:Multi-Scale 3D Image Analytics for High Dimensional Spatial Mapping of Normal Tissues
PI:Fiona Ginty
Project Manager:Anup Sood
Grant number:1UH3CA246594-01
Learn more: Visit the lab website.