Yichen Si, Joo Sang Lee, Goo Jun, Hyun Min Kang, Jun Hee Lee
{"title":"空间组学进入微观领域：机遇与挑战。","authors":"Yichen Si, Joo Sang Lee, Goo Jun, Hyun Min Kang, Jun Hee Lee","doi":"10.1016/j.tig.2025.05.002","DOIUrl":null,"url":null,"abstract":"<p><p>Spatial transcriptomics (ST) enables systematic profiling of whole-transcriptome gene expression in tissues while preserving spatial context. Recent advances in sequencing- and imaging-based ST technologies have ushered in the era of microscopic-resolution ST (μST), allowing transcriptome mapping at cellular and even subcellular scales with unprecedented precision. Despite these advances, μST faces substantial challenges, including sparse transcript discovery per submicron (or micron)-sized spatial units and data fragmentation across platforms, hindering integration and analysis. There is also a growing demand for scalable, segmentation-free, and universally applicable analysis methods, as well as strategies for 3D mapping, multi-omics integration, and artificial intelligence (AI)-driven spatial analysis. In this review, we highlight recent breakthroughs, outline key challenges, and discuss emerging experimental and computational solutions shaping the future of μST.</p>","PeriodicalId":54413,"journal":{"name":"Trends in Genetics","volume":" ","pages":""},"PeriodicalIF":16.3000,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatial omics enters the microscopic realm: opportunities and challenges.\",\"authors\":\"Yichen Si, Joo Sang Lee, Goo Jun, Hyun Min Kang, Jun Hee Lee\",\"doi\":\"10.1016/j.tig.2025.05.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Spatial transcriptomics (ST) enables systematic profiling of whole-transcriptome gene expression in tissues while preserving spatial context. Recent advances in sequencing- and imaging-based ST technologies have ushered in the era of microscopic-resolution ST (μST), allowing transcriptome mapping at cellular and even subcellular scales with unprecedented precision. Despite these advances, μST faces substantial challenges, including sparse transcript discovery per submicron (or micron)-sized spatial units and data fragmentation across platforms, hindering integration and analysis. There is also a growing demand for scalable, segmentation-free, and universally applicable analysis methods, as well as strategies for 3D mapping, multi-omics integration, and artificial intelligence (AI)-driven spatial analysis. In this review, we highlight recent breakthroughs, outline key challenges, and discuss emerging experimental and computational solutions shaping the future of μST.</p>\",\"PeriodicalId\":54413,\"journal\":{\"name\":\"Trends in Genetics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":16.3000,\"publicationDate\":\"2025-06-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Trends in Genetics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1016/j.tig.2025.05.002\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in Genetics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.tig.2025.05.002","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
Spatial omics enters the microscopic realm: opportunities and challenges.
Spatial transcriptomics (ST) enables systematic profiling of whole-transcriptome gene expression in tissues while preserving spatial context. Recent advances in sequencing- and imaging-based ST technologies have ushered in the era of microscopic-resolution ST (μST), allowing transcriptome mapping at cellular and even subcellular scales with unprecedented precision. Despite these advances, μST faces substantial challenges, including sparse transcript discovery per submicron (or micron)-sized spatial units and data fragmentation across platforms, hindering integration and analysis. There is also a growing demand for scalable, segmentation-free, and universally applicable analysis methods, as well as strategies for 3D mapping, multi-omics integration, and artificial intelligence (AI)-driven spatial analysis. In this review, we highlight recent breakthroughs, outline key challenges, and discuss emerging experimental and computational solutions shaping the future of μST.
期刊介绍：
Launched in 1985, Trends in Genetics swiftly established itself as a "must-read" for geneticists, offering concise, accessible articles covering a spectrum of topics from developmental biology to evolution. This reputation endures, making TiG a cherished resource in the genetic research community. While evolving with the field, the journal now embraces new areas like genomics, epigenetics, and computational genetics, alongside its continued coverage of traditional subjects such as transcriptional regulation, population genetics, and chromosome biology.
Despite expanding its scope, the core objective of TiG remains steadfast: to furnish researchers and students with high-quality, innovative reviews, commentaries, and discussions, fostering an appreciation for advances in genetic research. Each issue of TiG presents lively and up-to-date Reviews and Opinions, alongside shorter articles like Science & Society and Spotlight pieces. Invited from leading researchers, Reviews objectively chronicle recent developments, Opinions provide a forum for debate and hypothesis, and shorter articles explore the intersection of genetics with science and policy, as well as emerging ideas in the field. All articles undergo rigorous peer-review.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
