NetTracer3D Enables User-Friendly Analysis of Diverse Microscopic and Medical 3D Datasets

Area of Science:

• Spatial biology
• Bioinformatics
• Computational pathology

Background:

• High-resolution imaging and spatial transcriptomics enable 3D tissue map reconstruction.
• Standardization challenges impede integration, sharing, and analysis of 3D tissue datasets.
• A need exists for accessible, reproducible tools for 3D spatial biology analysis.

Purpose of the Study:

• To develop NetTracer3D, an integrated tool for simplifying 3D tissue map analysis.
• To provide standardized data formats and interactive exploration capabilities for diverse 3D datasets.
• To enable network analysis of tissue architecture, cellular organization, and physiological relationships.

Main Methods:

• NetTracer3D offers three network analysis modalities: Connectivity, Branch Adjacency/Branchpoint, and Proximity networks.
• The tool processes multidimensional data from various imaging modalities (e.g., CODEX, Raman spectroscopy).
• Analysis encompasses subcellular to anatomical scales and includes spatial statistics and visual analytics.

Main Results:

• NetTracer3D characterized neural relationships in human/mouse kidneys and mouse bronchi.
• Branchpoint networks identified vascular defects in human brain angiograms and lymph node innervation.
• Proximity networks analyzed tumor microenvironments and detected cellular neighborhoods in multiplexed 2D CODEX datasets.

Conclusions:

• NetTracer3D provides accessible and reproducible analytical tools for 3D spatial biology.
• Standardized formats and workflows facilitate data integration and sharing across research communities.
• The tool supports new discoveries linking tissue structure to physiological functions.