Tianben Ding, Kelvin C. M. Lee, Kevin K. Tsia, T. Nicolai Siegel, Dino Di Carlo, Keisuke Goda
{"title":"图像激活细胞分选","authors":"Tianben Ding, Kelvin C. M. Lee, Kevin K. Tsia, T. Nicolai Siegel, Dino Di Carlo, Keisuke Goda","doi":"10.1038/s44222-025-00334-1","DOIUrl":null,"url":null,"abstract":"The field of genomic research has undergone a remarkable transformation with the rapid expansion of high-quality genome databases, the discovery of epigenetic factors and the advent of CRISPR–Cas technology. However, a major challenge remains in linking these genetic and epigenetic perturbations to spatially resolved cellular phenotypes at scale. Image-activated cell sorting (IACS) offers a way to address this challenge by enabling real-time image-based sorting of suspended objects (for example, single live cells, cell clusters or cells adhered to carriers) at high rates of over 1,000 events per second. Unlike fluorescence-activated cell sorting (FACS), which relies on one-dimensional fluorescence intensity profiles of cells, IACS leverages multi-dimensional optical imaging to capture the full complexity of cellular characteristics, enabling high-content sorting based on both visual and functional attributes. A distinctive feature of IACS is its ability to integrate artificial intelligence for real-time image analysis, enabling sophisticated and precision decision-making in the sorting process. In this Review, we explore the principles, components and key performance indicators of IACS. We also highlight its unique applications across fields such as microbiology, immunology, cancer biology, food science and sustainability science, while addressing the challenges and future opportunities that lie ahead for IACS. Our goal is for this Review to serve as a comprehensive guide to IACS for beginners and experienced users alike, fostering its adoption and driving discoveries across biology and medicine. Genomic research has been transformed by genome databases, epigenetic discoveries and precise editing, yet linking genetic and epigenetic changes to cellular phenotypes remains challenging. In this Review, we explore the principles, components and applications of image-activated cell sorting (IACS) and discuss how it can address these limitations by enabling high-rate, real-time image-based sorting.","PeriodicalId":74248,"journal":{"name":"Nature reviews bioengineering","volume":"3 10","pages":"890-907"},"PeriodicalIF":37.6000,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Image-activated cell sorting\",\"authors\":\"Tianben Ding, Kelvin C. M. Lee, Kevin K. Tsia, T. Nicolai Siegel, Dino Di Carlo, Keisuke Goda\",\"doi\":\"10.1038/s44222-025-00334-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The field of genomic research has undergone a remarkable transformation with the rapid expansion of high-quality genome databases, the discovery of epigenetic factors and the advent of CRISPR–Cas technology. However, a major challenge remains in linking these genetic and epigenetic perturbations to spatially resolved cellular phenotypes at scale. Image-activated cell sorting (IACS) offers a way to address this challenge by enabling real-time image-based sorting of suspended objects (for example, single live cells, cell clusters or cells adhered to carriers) at high rates of over 1,000 events per second. Unlike fluorescence-activated cell sorting (FACS), which relies on one-dimensional fluorescence intensity profiles of cells, IACS leverages multi-dimensional optical imaging to capture the full complexity of cellular characteristics, enabling high-content sorting based on both visual and functional attributes. A distinctive feature of IACS is its ability to integrate artificial intelligence for real-time image analysis, enabling sophisticated and precision decision-making in the sorting process. In this Review, we explore the principles, components and key performance indicators of IACS. We also highlight its unique applications across fields such as microbiology, immunology, cancer biology, food science and sustainability science, while addressing the challenges and future opportunities that lie ahead for IACS. Our goal is for this Review to serve as a comprehensive guide to IACS for beginners and experienced users alike, fostering its adoption and driving discoveries across biology and medicine. Genomic research has been transformed by genome databases, epigenetic discoveries and precise editing, yet linking genetic and epigenetic changes to cellular phenotypes remains challenging. In this Review, we explore the principles, components and applications of image-activated cell sorting (IACS) and discuss how it can address these limitations by enabling high-rate, real-time image-based sorting.\",\"PeriodicalId\":74248,\"journal\":{\"name\":\"Nature reviews bioengineering\",\"volume\":\"3 10\",\"pages\":\"890-907\"},\"PeriodicalIF\":37.6000,\"publicationDate\":\"2025-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature reviews bioengineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.nature.com/articles/s44222-025-00334-1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature reviews bioengineering","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s44222-025-00334-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The field of genomic research has undergone a remarkable transformation with the rapid expansion of high-quality genome databases, the discovery of epigenetic factors and the advent of CRISPR–Cas technology. However, a major challenge remains in linking these genetic and epigenetic perturbations to spatially resolved cellular phenotypes at scale. Image-activated cell sorting (IACS) offers a way to address this challenge by enabling real-time image-based sorting of suspended objects (for example, single live cells, cell clusters or cells adhered to carriers) at high rates of over 1,000 events per second. Unlike fluorescence-activated cell sorting (FACS), which relies on one-dimensional fluorescence intensity profiles of cells, IACS leverages multi-dimensional optical imaging to capture the full complexity of cellular characteristics, enabling high-content sorting based on both visual and functional attributes. A distinctive feature of IACS is its ability to integrate artificial intelligence for real-time image analysis, enabling sophisticated and precision decision-making in the sorting process. In this Review, we explore the principles, components and key performance indicators of IACS. We also highlight its unique applications across fields such as microbiology, immunology, cancer biology, food science and sustainability science, while addressing the challenges and future opportunities that lie ahead for IACS. Our goal is for this Review to serve as a comprehensive guide to IACS for beginners and experienced users alike, fostering its adoption and driving discoveries across biology and medicine. Genomic research has been transformed by genome databases, epigenetic discoveries and precise editing, yet linking genetic and epigenetic changes to cellular phenotypes remains challenging. In this Review, we explore the principles, components and applications of image-activated cell sorting (IACS) and discuss how it can address these limitations by enabling high-rate, real-time image-based sorting.