{"title":"多路图像分析：我们取得了什么成就，我们将走向何方？","authors":"Yuval Bussi, Leeat Keren","doi":"10.1038/s41592-024-02539-5","DOIUrl":null,"url":null,"abstract":"Multiplexed tissue imaging has transformed tissue biology by revealing cellular diversity and interactions, but the analysis of its massive datasets remains a bottleneck. Here, we provide an overview of computational advancements, discuss current challenges and envision an AI-driven future in which integrated tools streamline analysis and visualization, unlocking the full potential of multiplexed imaging for breakthroughs in spatial biology.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2212-2215"},"PeriodicalIF":36.1000,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multiplexed image analysis: what have we achieved and where are we headed?\",\"authors\":\"Yuval Bussi, Leeat Keren\",\"doi\":\"10.1038/s41592-024-02539-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multiplexed tissue imaging has transformed tissue biology by revealing cellular diversity and interactions, but the analysis of its massive datasets remains a bottleneck. Here, we provide an overview of computational advancements, discuss current challenges and envision an AI-driven future in which integrated tools streamline analysis and visualization, unlocking the full potential of multiplexed imaging for breakthroughs in spatial biology.\",\"PeriodicalId\":18981,\"journal\":{\"name\":\"Nature Methods\",\"volume\":\"21 12\",\"pages\":\"2212-2215\"},\"PeriodicalIF\":36.1000,\"publicationDate\":\"2024-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Methods\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.nature.com/articles/s41592-024-02539-5\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Methods","FirstCategoryId":"99","ListUrlMain":"https://www.nature.com/articles/s41592-024-02539-5","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Multiplexed image analysis: what have we achieved and where are we headed?
Multiplexed tissue imaging has transformed tissue biology by revealing cellular diversity and interactions, but the analysis of its massive datasets remains a bottleneck. Here, we provide an overview of computational advancements, discuss current challenges and envision an AI-driven future in which integrated tools streamline analysis and visualization, unlocking the full potential of multiplexed imaging for breakthroughs in spatial biology.
期刊介绍：
Nature Methods is a monthly journal that focuses on publishing innovative methods and substantial enhancements to fundamental life sciences research techniques. Geared towards a diverse, interdisciplinary readership of researchers in academia and industry engaged in laboratory work, the journal offers new tools for research and emphasizes the immediate practical significance of the featured work. It publishes primary research papers and reviews recent technical and methodological advancements, with a particular interest in primary methods papers relevant to the biological and biomedical sciences. This includes methods rooted in chemistry with practical applications for studying biological problems.
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