JOURNAL ARTICLE

Histology Atlas of the Developing Mouse Digestive System With Emphasis From Prenatal Day 7.5 Through Early Postnatal Development.

  • Published In: Toxicologic Pathology, 2026, v. 54, n. 1. P. 4 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Lubeck, Beth A.; Elmore, Susan A.; Mahler, Beth; Parslow, Vanessa; Sabio, David; Stamper, Greg; Bolon, Brad 3 of 3

Abstract

This article focuses on the development of the digestive system in mice from embryonic day 7.5 through early postnatal stages, providing a detailed histological atlas to support research on normal and abnormal gastrointestinal (GI) development. It describes the formation and maturation of the upper and lower digestive tracts—including the oral cavity, esophagus, stomach, intestines, pancreas, and salivary glands—highlighting key morphological milestones and cellular differentiation events. The atlas emphasizes the utility of the common outbred CD-1 mouse strain as a model due to its developmental similarity to humans and includes high-resolution, annotated images of hematoxylin and eosin (H&E) and immunohistochemically stained sections. Additionally, the article reviews common congenital GI defects in mice, such as atresias, fistulas, and organ hypoplasia, linking these phenotypes to underlying developmental processes and genetic factors, thereby providing a resource for identifying and characterizing digestive system abnormalities in biomedical research.

Additional Information

  • Source:Toxicologic Pathology. 2026/01, Vol. 54, Issue 1, p4
  • Document Type:Article
  • Subject Area:Geography and Cartography
  • Publication Date:2026
  • ISSN:0192-6233
  • DOI:10.1177/01926233251365601
  • Accession Number:190644885
  • Copyright Statement:Copyright of Toxicologic Pathology is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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