CODA: quantitative 3D reconstruction of large tissues at cellular resolution
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Abstract
A central challenge in biology is obtaining high-content, high-resolution information while analyzing tissue samples at volumes relevant to disease progression. We address this here with CODA, a method to reconstruct exceptionally large (up to multicentimeter cubed) tissues at subcellular resolution using serially sectioned hematoxylin and eosin-stained tissue sections. Here we demonstrate CODA's ability to reconstruct three-dimensional (3D) distinct microanatomical structures in pancreas, skin, lung and liver tissues. CODA allows creation of readily quantifiable tissue volumes amenable to biological research. As a testbed, we assess the microanatomy of the human pancreas during tumorigenesis within the branching pancreatic ductal system, labeling ten distinct structures to examine heterogeneity and structural transformation during neoplastic progression. We show that pancreatic precancerous lesions develop into distinct 3D morphological phenotypes and that pancreatic cancer tends to spread far from the bulk tumor along collagen fibers that are highly aligned to the 3D curves of ductal, lobular, vascular and neural structures. Thus, CODA establishes a means to transform broadly the structural study of human diseases through exploration of exhaustively labeled 3D microarchitecture.
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3D image processing is adopted to construct microanatomical structures of skin.
“Three-dimensional image processing is also adopted to construct microanatomical structures of skin [59]”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [59]
In PDAC patient tissue, multiplexed myeloid, lymphoid, and functional antibody panels mapped the spatial relationship between myeloid and T cell populations.
“Illustrating the utility of this approach, multiplexed myeloid, lymphoid, and functional antibody panels were able to spatially define the relationship between myeloid and T cell populations in PDAC patient tissue275.”
From Pancreatic cancer: Advances and challenges (2023) · cites this paper as [275]
Although immune infiltration and behavior varied among patients, spatial analysis revealed extensive heterogeneity within individual patients.
“Of note, while many differences in immune infiltration and behavior were found between different patients, this ability to spatially assess these data allowed the observation of widespread heterogeneity in individual patients.”
From Pancreatic cancer: Advances and challenges (2023) · cites this paper as [275]
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Aspects and outcomes of surveillance for individuals at high-risk of pancreatic cancer
Bogdanski, A. M., van Hooft, J. E., Boekestijn, B., Bonsing, B. A., Wasser, M. N. J. M., Klatte, D. C. F., & van Leerdam, M. E. (2024) · Familial Cancer · doi:10.1007/s10689-024-00368-1 · Cited by 12 · see citing works
Burlingame, E. A., Ternes, L., Lin, J., Chen, Y.-A., Kim, E. N., Gray, J. W., Santagata, S., Sorger, P. K., & Chang, Y. H. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1275402 · Cited by 10 · see citing works
Karageorgos, G. M., Cho, S., McDonough, E. M., Chadwick, C. M., Ghose, S., Owens, J. R., Jung, K. J., Machiraju, R., West, R. B., Brooks, J. D., et al. (2024) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1296667 · Cited by 10 · see citing works
- Authors
- Kiemen, A. L., Braxton, A. M., Grahn, M. P., Han, K. S., Babu, J. M., Reichel, R., Jiang, A. C., Kim, B., Hsu, J., Amoa, F., Reddy, S., Hong, S.-M., Cornish, T. C., Thompson, E. D., Huang, P., Wood, L. D., Hruban, R. H., Wirtz, D., & Wu, P.-H.
- Affiliations
- Johns Hopkins University (US)
- Asan Medical Center (KR)
- University of Colorado Anschutz (US)
- Corresponding author
- Wirtz, Denis
- ORCID
- Venue
- Nature Methods
- Published
- 2022-10-24
- Volume
- 19
- Issue
- 11
- Pages
- 1490-1499
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC10500590
- OpenAlex
- W4307286999
- Cited by
- 218
- References
- 70
- Citations by year
- 2026: 41 · 2025: 59 · 2024: 52 · 2023: 34 · 2022: 19 · 2021: 12 · 2020: 1
- FWCI
- 18.19
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Foundation for the National Institutes of Health (U54CA268083, 5T32CA153952, U52CA210173, U01AG060903)
- The Sol Goldman Pancreatic Cancer Research Center
- ARCS Foundation, Inc, Susan Wojcicki and Dennis Troper, The Sol Goldman Pancreatic Cancer Research Center
- The Sol Goldman Pancreatic Cancer Research Center, Susan Wojcicki and Dennis Troper, The Rolfe Foundation for Pancreatic Cancer Research, Allegheny Health Network – Johns Hopkins Cancer Research Fund
- The Sol Goldman Pancreatic Cancer Research Center, Susan Wojcicki and Dennis Troper
- The Sol Goldman Pancreatic Cancer Research Center, NVIDIA GPU Grant
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- coda, 3D tissue reconstruction, subcellular resolution, serial sectioning, H&E staining, pancreatic cancer, pancreatic ductal system, pancreatic precancerous lesions, carcinogenesis, tissue microstructure, collagen fiber alignment, tissue labeling, high-content imaging
- MeSH terms
- Humans, Pancreas, Pancreatic Neoplasms, Imaging, Three-Dimensional
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