Unmet Need / Invention Novelty: Three-dimensional (3D) tissue reconstruction permits analyses of the relationships between cellular factors, tissue structure, and diseasepathology.Current methods used to visualize and quantify tissue structures in 3D space are limited to small volumes and have various constraints relating to consistency, information content, resolution, and associated time and cost. There is an unmet need to develop a novel, high-content and high-resolution method for reconstructing 3D tissues in large volumetric landscapes to both further disease understanding and serve as a basis for biomarker and drug development.
Technical Details: Researchers at Johns Hopkins have developed a computational method for the 3D reconstruction of tissue from imaging data. The method, called CODA, utilizes nonlinear image registration, color deconvolution, and deep learning semantic segmentation to create a digital tissue volume from serially sectioned hematoxylin and eosin (H&E) histological samples and identify and label single cells and tissue subtypes. The result is a multi-labelled digital 3D map of cm-scale volumes of tissue at micrometer and single cell resolution. The CODA platform can be paired with additional single-cell analysis tools such as immunofluorescence and next-gen sequencing to provide a detailed single-cell tissue landscape in disease. Proof-of-concept studies demonstrated the ability of CODA to reconstruct and label pancreatic tissue and provide subsequent insights into pancreatic ductal adenocarcinoma tumorigenesis.
Value Proposition:
- 3D reconstruction of cm-scale, large volumetric tissue
- High-content and high-resolution reconstruction at the micrometer and single cell scales
- Highly precise recall and labeling of tissue subtypes without the need for additional molecular probes
- Applicable to a variety of tissue types
Looking for Partners to: Develop and commercialize as a novel three-dimensional tissue reconstruction method
Stage of Development: Pre-clinical
Data Availability: Proof-of-concept data with normal, precancerous, and invasive pancreatic cancer tissue samples
Publication(s)/Associated technologies:
Kiemen et al. 2020. In situ characterization of the 3D microanatomy of the pancreas and pancreatic cancer at single cell resolution. bioRxiv preprint. doi: 10.1101/2020.12.08.416909
