More Like this

1. Zelinski Lab: Japanese Macaque Ovary

   https://doi.org/10.48349/ASU/KM2QZQ  

   Multispecies_Ovary_Tissue_Histology_Electronic_Repository_MOTHER (January 2022, ASU Library Research Data Repository)

   Dietrich, Suzanne W; Zelinski, Mary B; Sluka, James P; Watanabe, Karen H (Ed.)

   Dataset for histology images from the ovaries of Japanese macaque (Macaca fuscata). These images are associated with the Multispecies Ovary Tissue Histology Electronic Repository (MOTHER), an online repository (https://mother-db.org) of ovary tissue histology digital images funded by NSF (DBI-2054061). Sharing these histology images will facilitate comparative studies of female reproductive strategies, enable the development of computational models to test hypotheses related to ovarian development and female reproduction, and serve as an educational resource thereby reducing the use of animals in research. See the README file (https://dataverse.asu.edu/file.xhtml?fileId=10645) for an overview of the dataset, including file naming conventions. 

   more » « less
2. Zelinski Lab: Rhesus Macaque Ovary

   https://doi.org/10.48349/ASU/46JWEX  

   Multispecies_Ovary_Tissue_Histology_Electronic_Repository_MOTHER (January 2022, ASU Library Research Data Repository)

   Dietrich, Suzanne W; Zelinksi, Mary B; Sluka, James P; Watanabe, Karen H (Ed.)

   Dataset for histology images from the ovaries of Rhesus macaque (Macaca mulatta). These images are associated with the Multispecies Ovary Tissue Histology Electronic Repository (MOTHER), an online repository (https://mother-db.org) of ovary tissue histology digital images, funded by NSF (DBI-2054061). Sharing these histology images will facilitate comparative studies of female reproductive strategies, enable the development of computational models to test hypotheses related to ovarian development and female reproduction, and serve as an educational resource thereby reducing the use of animals in research. See the README file (https://dataverse.asu.edu/file.xhtml?fileId=32181) for an overview of the dataset, including naming conventions. 

   more » « less
3. Overview of the Multispecies Ovary Tissue Histology Electronic Repository

   https://doi.org/10.1093/biolre/ioae101  

   Watanabe, Karen_H; Dietrich, Suzanne_W; Ding, Yian; Ma, Wenli; Sluka, James_P; Zelinski, Mary_B (June 2024, Biology of Reproduction)

   Abstract The Multispecies Ovary Tissue Histology Electronic Repository (MOTHER) is a publicly accessible repository of ovary histology images. MOTHER includes hundreds of images from nonhuman primates, as well as ovary histology images from an expanding range of other species. Along with an image, MOTHER provides metadata about the image, and for selected species, follicle identification annotations. Ongoing work includes assisting scientists with contributing their histology images, creation of manual and automated (via machine learning) processing pipelines to identify and count ovarian follicles in different stages of development, and the incorporation of that data into the MOTHER database (MOTHER-DB). MOTHER will be a critical data repository storing and disseminating high-value histology images that are essential for research into ovarian function, fertility, and intra-species variability. 

   more » « less
4. MOTHER-DB: A Database for Sharing Nonhuman Ovarian Histology Images

   https://doi.org/10.1109/TCBB.2024.3426999  

   Dietrich, Suzanne W; Ma, Wenli; Ding, Yian; Watanabe, Karen H; Zelinski, Mary B; Sluka, James P (November 2024, IEEE/ACM Transactions on Computational Biology and Bioinformatics)

   The goal of the Multispecies Ovary Tissue Histology Electronic Repository (MOTHER) project is to establish a collection of nonhuman ovary histology images for multiple species as a resource for researchers and educators. An important component of sharing scientific data is the inclusion of the contextual metadata that describes the data. MOTHER extends the Ecological Metadata Language (EML) for documenting research data, leveraging its data provenance and usage license with the inclusion of metadata for ovary histology images. The design of the MOTHER metadata includes information on the donor animal, including reproductive cycle status, the slide and its preparation. MOTHER also extends the ezEML tool, called ezEML+MOTHER, for the specification of the metadata. The design of the MOTHER database (MOTHER-DB) captures the metadata about the histology images, providing a searchable resource for discovering relevant images. MOTHER also defines a curation process for the ingestion of a collection of images and its metadata, verifying the validity of the metadata before its inclusion in the MOTHER collection. A Web search provides the ability to identify relevant images based on various characteristics in the metadata itself, such as genus and species, using filters. 

   more » « less
5. Volumetric Stimulated Raman Scattering Imaging of Cleared Tissues towards Three-dimensional Chemical Histopathology

   Junjie Li, Peng Lin (January 2019, Biomedical optics express)

   Abstract: Thin tissue slice based histology has been used as a gold standard for disease diagnosis since over a hundred years ago. However, histopathological evaluation on two-dimensional slides suffers from large variations due to limited sampling. To improve the diagnostic accuracy, three-dimensional (3D) histology is performed through serial sectioning, staining, imaging and reconstruction of individual slices, which is highly time-consuming and labor intensive. We developed a volumetric stimulated Raman scattering (SRS) imaging method, which provides histology-like information in 3D context without the need for staining with dyes. Using a small molecule clearing agent, formamide, we performed tissue clearing within 30 min and achieved an imaging depth up to 500 µm in highly scattered tissues, including brain, kidney, liver and lung. Through a two-color SRS imaging scheme, we obtained histology-like images in cleared brain tissue slices. Our method has the potential for 3D tissue histopathology to improve the accuracy of histopathological examination. https://doi.org/10.1364/BOE.10.004329 

   more » « less