Cited by 218Cell Image Analysis TechniquesAI in cancer detectionMedical Image Segmentation Techniques

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

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Kiemen, Ashley L., Braxton, Alicia M., Grahn, Mia P., Han, Kyu Sang, Babu, Jaanvi Mahesh, Reichel, Rebecca, Jiang, Ann C., Kim, Bridgette, Hsu, Jocelyn, Amoa, Falone, Reddy, Sashank, Hong, Seung-Mo, Cornish, Toby C., Thompson, Elizabeth D., Huang, Peng, Wood, Laura D., Hruban, Ralph H., Wirtz, Denis, Wu, Pei-Hsun·2022·Nature Methods

DOI 10.1038/s41592-022-01650-9PMC PMC10500590

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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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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: Kiemen, Ashley L.
Braxton, Alicia M.
Grahn, Mia P.
Han, Kyu Sang
Reichel, Rebecca
Hsu, Jocelyn
Amoa, Falone
Reddy, Sashank
Hong, Seung-Mo
Cornish, Toby C.
Thompson, Elizabeth D.
Huang, Peng
Wood, Laura D.
Hruban, Ralph H.
Wirtz, Denis
Wu, Pei-Hsun
Venue: Nature Methods
Publisher: Springer Science and Business Media LLC
Published: 2022-10-24
Volume: 19
Issue: 11
Pages: 1490-1499
ISSN: 1548-7091 (ISSN-L)
1548-7105
DOI: 10.1038/s41592-022-01650-9
PMC: PMC10500590
OpenAlex: W4307286999
Cited by: 218
References: 70
Citations by year: 2026: 41 · 2025: 59 · 2024: 52 · 2023: 34 · 2022: 19
FWCI: 18.19
Type: journal article
Language: en
Open access: green
doi.org
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
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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