Histopathological images dataset of virus-induced pulmonary changes in HCoV-229E-infected BALB/c mice

Human coronavirus 229E (HCoV-229E) is an alphacoronavirus that typically causes mild respiratory illness but can be severe in vulnerable individuals. Due to the absence of the human aminopeptidase-N (hAPN) receptor, BALB/c mice are not naturally permissive to productive 229E replication. This study assesses the immunogenicity of HCoV-229E in BALB/c mice and investigates how different routes of infection influence antibody responses. Mice were inoculated intranasally, intraperitoneally, or intramuscularly, with mock animals as controls. Here, we report a histopathological dataset that includes lung tissue sections from BALB/c mice stained with Haematoxylin and Eosin (H&E) and Masson’s Trichrome (MT) to assess microscopic changes following HCoV-229E infection. H&E staining was used to evaluate cellular infiltration, tissue architecture, and inflammation, which was evident in infected mice compared to mock. While MT staining was utilised to examine collagen deposition and fibrosis-related changes, which are commonly observed in severe coronavirus infections such as SARS-CoV-2. Although collagen deposition was observed in infected animals, no significant difference was detected.
The dataset is organized into a total of seven folders to facilitate easy navigation and review, organized under a primary folder named 229E, which serves as the main directory. This folder contains two subfolders corresponding to the staining methods: H&E and Masson’s Trichrome (MT). The H&E-stained images are grouped into two subfolders: one for infected samples and one for mock controls. The infected H&E subfolder includes 50 images, while the mock H&E subfolder contains 43 images. Similarly, Masson’s Trichrome (MT)-stained images are organized, which include two subfolders corresponding to infected and mock samples. The MT-infected subfolder comprises 33 images, and the MT-mock subfolder contains 34 images. This structured organization allows reviewers to readily identify and access images based on staining method.