Image-Activated Cell Sorting (IACS) is a revolutionary technology that performs real-time, image-based sorting of single cells at a high rate of over 1000 events per second. Developed by a collaborative group led by Professor Keisuke Goda, IACS extends beyond the capabilities of traditional fluorescence-activated cell sorting (FACS) by analyzing multidimensional images of cells, enabling high-content sorting of cells or cell clusters with unique spatial, chemical, and morphological traits. This includes intracellular protein localization and cell-cell interactions, making IACS an essential part of holistic single-cell analysis by facilitating direct links between population-level analysis (flow cytometry), cell-level analysis (microscopy), and gene-level analysis (sequencing).
IACS operates through seamless integration of high-throughput cell microscopy, including multicolor fluorescence imaging and bright-field imaging, along with cell focusing and sorting on a hybrid software-hardware data management infrastructure. This enables real-time, automated operation for data acquisition, processing, decision-making, and actuation. The platform also utilizes deep learning techniques for more accurate cell analysis. IACS is a versatile technology that facilitates machine-based scientific discovery in biological, pharmaceutical, and medical sciences.
Our team is currently developing a novel range of biological, pharmaceutical, and medical applications using the IACS platform. These applications include but are not limited to cancer biology, immunology, food science, stem cell biology, environmental science, green energy, cell therapy, cancer and thrombosis diagnostics. We are seeking new team members who are interested in contributing to the optimization of the IACS technology and developing new applications within these fields.
- N. Nitta, T. Sugimura, A. Isozaki, H. Mikami, K. Hiraki, S. Sakuma, T. Iino, F. Arai, T. Endo, Y. Fujiwaki, H. Fukuzawa, M. Hase, T. Hayakawa, K. Hiramatsu, Y. Hoshino, M. Inaba, T. Ito, H. Karakawa, .., T. Yamano, A. Yasumoto, Y. Yatomi, M. Yazawa, D. Di Carlo, Y. Hosokawa, S. Uemura, Y. Ozeki, and K. Goda, "Intelligent image-activated cell sorting", Cell 175, 266 (2018)
- M. Herbig, A. Isozaki, D. Di Carlo, J. Guck, N. Nitta, H. Shintaku, A. Wu, I. Nikaido, S. Kamikawaji, E. Suyama, R. Damoiseaux, and K. Goda, "Best practices for reporting throughput in biomedical research", Nature Methods 19, 633 (2022)
- N. Nitta, T. Iino, A. Isozaki, M. Yamagishi, Y. Kitahama, S. Sakuma, Y. Suzuki, H. Tezuka, M. Oikawa, F. Arai, T. Asai, D. Deng, H. Fukuzawa, M. Hase, T. Hasunuma, T. Hayakawa, .., D. Di Carlo, Y. Hosokawa, S. Uemura, T. Sugimura, Y. Ozeki, and K. Goda, "Raman image-activated cell sorting", Nature Communications 11, 3452 (2020)
- A. Isozaki, H. Mikami, K. Hiramatsu, S. Sakuma, Y. Kasai, T. Iino, T. Yamano, A. Yasumoto, Y. Oguchi, .., M. Yamada, S. Matsusaka, T. Hayakawa, H. Fukuzawa, Y. Yatomi, F. Arai, D. Di Carlo, A. Nakagawa, Y. Hoshino, Y. Hosokawa, S. Uemura, T. Sugimura, Y. Ozeki, N. Nitta, and K. Goda, "A practical guide to intelligent image-activated cell sorting", Nature Protocols 14, 2370 (2019)
- A. Isozaki, H. Mikami, H. Tezuka, H. Matsumura, K. Huang, M. Akamine, K. Hiramatsu, T. Iino, T. Ito, H. Karakawa, Y. Kasai, Y. Li, Y. Nakagawa, .., T. Sugimura, N. Nitta, and K. Goda, "Intelligent image-activated cell sorting 2.0", Lab on a Chip 20, 2263 (2020)
- H. Mikami, M. Kawaguchi, C.-J. Huang, H. Matsumura, T. Sugimura, K. Huang, C. Lei, S. Ueno, T. Miura, T. Ito, K. Nagasawa, T. Maeno, H. Watarai, M. Yamagishi, S. Uemura, S. Ohnuki, Y. Ohya, H. Kurokawa, S. Matsusaka, C.-W. Sun, Y. Ozeki, and K. Goda, "Virtual-freezing fluorescence imaging flow cytometry", Nature Communications 11, 1162 (2020)
Image-Activated Cell Sorting
- Field leader: Tianben Ding
- Funding: JSPS Core-to-Core Program, JSPS Kiban S, JSPS Kiban A, White Rock Foundation, Super Yeast Crowdfunding Program
- Collaboration: Serendipity Lab