{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T14:17:58Z","timestamp":1785161878482,"version":"3.55.0"},"reference-count":161,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Geisel School of Medicine at Dartmouth\u2019s Center for Quantitative Biology"},{"DOI":"10.13039\/100000057","name":"the National Institute of General Medical Sciences","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000057","name":"NIGMS","doi-asserted-by":"publisher","award":["P20GM130454"],"award-info":[{"award-number":["P20GM130454"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In brief: This review synthesizes the technical landscape of current spatial transcriptomic platforms, addresses computational challenges unique to ovarian tissue, and surveys biological discoveries across ovarian development, aging, follicle dynamics, and cancer, providing a practical framework to guide platform selection and analytical strategy in reproductive biology.<\/jats:p>\n                  <jats:p>Abstract: The ovary is a structurally complex organ whose function depends on precisely coordinated interactions among multiple cell types. Spatially resolved transcriptomics (ST) has emerged as a powerful complement to single-cell RNA sequencing (scRNA-seq), enabling gene expression profiling within intact tissue and preserving the spatial context that dissociation-based methods inherently lack. This review provides a comprehensive overview of the major ST platforms, including sequencing-based technologies (Visium, Visium HD, Stereo-seq, and GeoMx) and imaging-based technologies (Xenium, MERSCOPE, and CosMx), with a focus on their distinct technical features, resolution trade-offs, and suitability for ovarian research. We survey 40 published studies applying ST to ovarian biology, spanning atlases, ovarian aging, follicle development and ovulation, and ovarian cancer. We also discuss typical computational analyses as well as their challenges specific to ovary, including cell segmentation of morphologically diverse cell populations, deconvolution of mixed-cell capture spots in sequencing-based platforms, quality control, batch correction, and spatially aware downstream analyses encompassing trajectory inference, cell\u2013cell interaction modeling, gene regulatory network reconstruction, and more. Across these biological contexts, multimodal integration, pairing ST with scRNA-seq, spatial proteomics, or chromatin accessibility profiling, has proven increasingly valuable for resolving the full molecular complexity of ovarian biology. Nevertheless, some challenges persist, and no single platform is universally optimal for all research questions. Thoughtful alignment between biological objectives, tissue scale, and platform capability will be critical for advancing ST from descriptive mapping toward mechanistic and clinically translatable discovery.<\/jats:p>","DOI":"10.1093\/reprod\/xaag079","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T16:18:43Z","timestamp":1782922723000},"source":"Crossref","is-referenced-by-count":0,"title":["Spatial transcriptomics in ovarian biology technologies: computational challenges, and biological insights"],"prefix":"10.1093","volume":"172","author":[{"given":"Ruixu","family":"Huang","sequence":"first","affiliation":[{"name":"Thayer School of Engineering, Dartmouth College , Hanover, NH,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5962-9570","authenticated-orcid":false,"given":"Brittany A","family":"Goods","sequence":"additional","affiliation":[{"name":"Faculty of Science, School of BioSciences, The University of Melbourne , Melbourne, Victoria,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"2026072709442835900_xaag079-B1","author":"10X Genomics"},{"key":"2026072709442835900_xaag079-B2","author":"10x Genomics"},{"key":"2026072709442835900_xaag079-B3","author":"10x Genomics"},{"key":"2026072709442835900_xaag079-B5","author":"10X Visium HD Product"},{"key":"2026072709442835900_xaag079-B6","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1038\/nmeth.4463","article-title":"SCENIC: Single-cell regulatory network inference and clustering","volume":"14","author":"Aibar","year":"2017","journal-title":"Nature Methods"},{"key":"2026072709442835900_xaag079-B7","doi-asserted-by":"publisher","first-page":"R106","DOI":"10.1186\/gb-2010-11-10-r106","article-title":"Differential expression analysis for sequence count 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