Three-dimensional assessments are necessary to determine the true, spatially resolved composition of tissues

André Forjaz; Eduarda Vaz; Valentina Matos Romero; Saurabh Joshi; Vasco Queiroga; Alicia M Braxton; Ann C Jiang; Kohei Fujikura; Toby Cornish; Seung-Mo Hong; Ralph H Hruban; Pei-Hsun Wu; Laura D Wood; Ashley L Kiemen; Denis Wirtz

doi:10.1016/j.crmeth.2025.101075

Three-dimensional assessments are necessary to determine the true, spatially resolved composition of tissues

Cell Rep Methods. 2025 Jun 16;5(6):101075. doi: 10.1016/j.crmeth.2025.101075. Epub 2025 Jun 9.

Authors

Affiliations

¹ Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
² Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC, USA.
³ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
⁴ Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada.
⁵ Department of Pathology, Data Science Institute, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
⁶ Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
⁷ Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD, USA; Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
⁸ Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA.
⁹ Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD, USA; Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA.
¹⁰ Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD, USA; Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA; Department of Functional Anatomy & Evolution, Johns Hopkins School of Medicine, Baltimore, MD, USA. Electronic address: kiemen@jhmi.edu.
¹¹ Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD, USA; Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA. Electronic address: wirtz@jhu.edu.

PMID: 40494356
DOI: 10.1016/j.crmeth.2025.101075

Abstract

Methods for spatially resolved cellular profiling of tissue sections enable in-depth study of inter- and intra-sample heterogeneity but often profile small regions, requiring evaluation of many samples to compensate for limited assessment. Recent advances in three-dimensional (3D) tissue mapping offer deeper insights; however, attempts to quantify the information gained in transitioning to 3D remains limited. Here, to compare inter- and intra-sample tissue heterogeneity, we analyze >100 pancreas samples as cores, whole-slide images (WSIs), and cm³-sized 3D samples. We show that tens of WSIs and hundreds of tissue microarrays are needed to approximate the compositional tissue heterogeneity of tumors. Additionally, spatial correlations of pancreatic structures decay significantly within microns, demonstrating that isolated two-dimensional (2D) sections poorly represent their surroundings. Through 3D simulations, we determined the number of slides necessary to accurately measure tumor burden. These results quantify the power of 3D mapping and establish sampling methods for biological studies prioritizing composition or incidence.

Keywords: 3D digital pathology; 3D modeling; CP: imaging; PDAC; pancreatic cancer; tissue composition; tissue heterogeneity; tissue sampling; tumor heterogeneity; tumor microenvironment.

MeSH terms

Humans
Imaging, Three-Dimensional* / methods
Pancreas* / pathology
Pancreatic Neoplasms* / pathology
Tissue Array Analysis / methods