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CODA: quantitative 3D reconstruction of large tissues at cellular resolution.
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.
  • Kiemen AL; Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA.
  • Braxton AM; Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Grahn MP; Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Han KS; Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA.
  • Babu JM; Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA.
  • Reichel R; Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Jiang AC; Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Kim B; Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA.
  • Hsu J; Department of Mechanical Engineering, The Johns Hopkins University, Baltimore, MD, USA.
  • Amoa F; Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA.
  • Reddy S; Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Hong SM; Department of Plastic and Reconstructive Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Cornish TC; Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Thompson ED; Department of Pathology, University of Colorado School of Medicine, Aurora, CO, USA.
  • Huang P; Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Wood LD; Department of Biostatistics, The Johns Hopkins University, Baltimore, MD, USA.
  • Hruban RH; Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Wirtz D; Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Wu PH; Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Nat Methods ; 19(11): 1490-1499, 2022 11.
Article en En | MEDLINE | ID: mdl-36280719
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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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Neoplasias Pancreáticas / Imagenología Tridimensional Límite: Humans Idioma: En Año: 2022 Tipo del documento: Article
Texto completo
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PubMed Links
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Neoplasias Pancreáticas / Imagenología Tridimensional Límite: Humans Idioma: En Año: 2022 Tipo del documento: Article