bioRxiv preprint doi: https://doi.org/10.1101/2020.12.08.416909; this version posted December 9, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. In situ characterization of the 3D microanatomy of the pancreas and pancreatic cancer at single cell resolution Ashley Kiemen1, Alicia M. Braxton2, Mia P. Grahn1, Kyu Sang Han1, Jaanvi Mahesh Babu2, Rebecca Reichel2, Falone Amoa2, Seung-Mo Hong3, Toby C. Cornish4, Elizabeth D. Thompson2, Laura D. Wood2, Ralph H. Hruban2, Pei-Hsun Wu1*, Denis Wirtz1,2,6,7* 1 Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland 21218, USA 2 Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA 3 Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea 4 Department of Pathology, University of Colorado School of Medicine, Aurora, Colorado 80045, USA 5 Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA 6 Department of Materials Science and Engineering, The Johns Hopkins University, Baltimore, Maryland 21218, USA 7 Johns Hopkins Physical Sciences - Oncology Center, The Johns Hopkins University, Baltimore, Maryland 21218, USA *Corresponding authors: Denis Wirtz (
[email protected]) and Pei-Hsun Wu (
[email protected]) Keywords: digital pathology, deep learning, pancreatic cancer, cancer invasion bioRxiv preprint doi: https://doi.org/10.1101/2020.12.08.416909; this version posted December 9, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.