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  • Review Article
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Image-activated cell sorting

Nature Reviews Bioengineering volume 3, pages 890–907 (2025)Cite this article

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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.

Key points

  • The field of genomic research has been revolutionized by the rapid growth of high-quality genome databases, the identification of epigenetic factors and the introduction of CRISPR–Cas technology.

  • A key challenge remains in linking genetic changes to spatially resolved cellular phenotypes, given that spatial organization is vital to understanding cellular function and behaviour.

  • Image-activated cell sorting (IACS) offers a way to address this challenge by providing real-time, high-content sorting of suspended objects such as single live cells, cell clusters or cells adhered to carriers, based on their visual and functional attributes using multi-dimensional imaging.

  • The integration of artificial intelligence for real-time image analysis enables sophisticated and precise decision-making based on nuanced features during the sorting process.

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Fig. 1: Principles of IACS.
Fig. 2: Components of IACS.
Fig. 3: Applications of IACS.
Fig. 4: Challenges and future opportunities.

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Acknowledgements

This work was supported by the JSPS Core-to-Core Program (JPJSCCA20190007 to K.G.), the Research Grants Council (14125924 and RFS2021-7S06 to K.K.T.), the Innovation and Technology Commission (InnoHK initiative and ITS/318/22FP to K.K.T.), the Chan Zuckerberg Initiative Donor Advised Fund (2023-332386 to D.D.C.), the Humboldt Foundation’s Philipp Franz von Siebold Award (to K.G.), the Humboldt Association of Japan (to K.G.), the Mohammed bin Salman Center for Future Science and Technology for Saudi-Japan Vision 2030 (to K.G.), and the Cell Aging Control Endowed Chair at the University of Tokyo. We gratefully acknowledge the Serendipity Lab, the MSCA ITN Cell2Cell, and the LMU Munich and the University of Tokyo strategic partnership fund for facilitating collaboration opportunities.

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Authors and Affiliations

  1. Department of Chemistry, The University of Tokyo, Tokyo, Japan

    Tianben Ding & Keisuke Goda

  2. Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China

    Kelvin C. M. Lee & Kevin K. Tsia

  3. Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin New Town, Hong Kong, China

    Kelvin C. M. Lee & Kevin K. Tsia

  4. Division of Experimental Parasitology, Faculty of Veterinary Medicine, Ludwig-Maximilians-Universität München, Munich, Germany

    T. Nicolai Siegel

  5. Biomedical Center Munich, Division of Physiological Chemistry, Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany

    T. Nicolai Siegel

  6. Department of Bioengineering, University of California, Los Angeles, CA, USA

    Dino Di Carlo & Keisuke Goda

  7. Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, CA, USA

    Dino Di Carlo

  8. California NanoSystems Institute, University of California, Los Angeles, CA, USA

    Dino Di Carlo

  9. Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA

    Dino Di Carlo

  10. International Center for Synchrotron Radiation Innovation Smart (SRIS), Tohoku University, Sendai, Miyagi, Japan

    Keisuke Goda

  11. Institute of Technological Sciences, Wuhan University, Wuhan, Hubei, China

    Keisuke Goda

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Contributions

All authors contributed to the writing of the manuscript. K.G. edited the manuscript and led the manuscript development team.

Corresponding author

Correspondence to Keisuke Goda.

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Competing interests

K.G. is a shareholder of CYBO, LucasLand and FlyWorks. K.K.T. is a shareholder of Conzeb. D.D.C. and the Regents of the University of California are shareholders of Partillion Bioscience Corporation, which sells Nanovials.

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Ding, T., Lee, K.C.M., Tsia, K.K. et al. Image-activated cell sorting. Nat Rev Bioeng 3, 890–907 (2025). https://doi.org/10.1038/s44222-025-00334-1

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