CODA: quantitative 3D reconstruction of large tissues at cellular resolution

Area of Science:

• Biology
• Pathology
• Biomedical Engineering

Background:

• Analyzing large tissue volumes at high resolution is crucial for understanding disease progression.
• Current methods face limitations in integrating high-content data with relevant tissue scales.

Purpose of the Study:

• To introduce CODA (Combinatorial Object Detection and Analysis), a novel method for reconstructing large-scale tissues at subcellular resolution.
• To demonstrate CODA's utility in analyzing complex 3D microanatomy and its application in studying human diseases, specifically pancreatic cancer.

Main Methods:

• CODA reconstructs exceptionally large (multicentimeter cubed) tissues using serially sectioned hematoxylin and eosin-stained slides.
• The method achieves subcellular resolution, enabling detailed 3D microanatomical reconstruction.
• Applied to pancreas, skin, lung, and liver tissues, with a focus on human pancreatic tumorigenesis.

Main Results:

• Successfully reconstructed 3D microanatomical structures in multiple human organs.
• Identified distinct 3D morphological phenotypes of precancerous pancreatic lesions.
• Revealed that pancreatic cancer spreads along aligned collagen fibers within the ductal, vascular, and neural architecture.

Conclusions:

• CODA enables the creation of quantifiable, high-resolution 3D tissue volumes for biological research.
• The method transforms the study of human diseases by allowing exhaustive exploration of 3D microarchitecture.
• Provides new insights into pancreatic cancer progression, heterogeneity, and spread patterns.