A practical guide to intelligent image-activated cell sorting

• Published in: Nature protocols Vol. 14; no. 8; pp. 2370 - 2415
• Main Authors: Isozaki, Akihiro, Mikami, Hideharu, Hiramatsu, Kotaro, Sakuma, Shinya, Kasai, Yusuke, Iino, Takanori, Yamano, Takashi, Yasumoto, Atsushi, Oguchi, Yusuke, Suzuki, Nobutake, Shirasaki, Yoshitaka, Endo, Taichiro, Ito, Takuro, Hiraki, Kei, Yamada, Makoto, Matsusaka, Satoshi, Hayakawa, Takeshi, Fukuzawa, Hideya, Yatomi, Yutaka, Arai, Fumihito, Di Carlo, Dino, Nakagawa, Atsuhiro, Hoshino, Yu, Hosokawa, Yoichiroh, Uemura, Sotaro, Sugimura, Takeaki, Ozeki, Yasuyuki, Nitta, Nao, Goda, Keisuke
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.08.2019; Nature Publishing Group
• Subjects: 631/114/1305; 631/1647/1407/1492; 631/1647/2163; 631/1647/245/2225; 631/1647/277; Actuation; Analytical Chemistry; Biological Techniques; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computer applications; Data acquisition; Data management; Data processing; Decision making; Design parameters; Digital signal processing; Flow cytometry; Fluorescence; Gene sequencing; Image quality; Integration; Life Sciences; Machine learning; Mechanical components; Mechanical properties; Mechatronics; Microarrays; Microfluidics; Microscopy; Optics; Organic Chemistry; Parameter sensitivity; Protocol; Real time operation; Signal processing
• ISSN: 1754-2189; 1750-2799; 1750-2799
• DOI: 10.1038/s41596-019-0183-1

Intelligent image-activated cell sorting (iIACS) is a machine-intelligence technology that performs real-time intelligent image-based sorting of single cells with high throughput. iIACS extends beyond the capabilities of fluorescence-activated cell sorting (FACS) from fluorescence intensity profiles of cells to multidimensional images, thereby enabling high-content sorting of cells or cell clusters with unique spatial chemical and morphological traits. Therefore, iIACS serves as an integral part of holistic single-cell analysis by enabling direct links between population-level analysis (flow cytometry), cell-level analysis (microscopy), and gene-level analysis (sequencing). Specifically, iIACS is based on a seamless integration of high-throughput cell microscopy (e.g., multicolor fluorescence imaging, bright-field imaging), cell focusing, cell sorting, and deep learning on a hybrid software–hardware data management infrastructure, enabling real-time automated operation for data acquisition, data processing, intelligent decision making, and actuation. Here, we provide a practical guide to iIACS that describes how to design, build, characterize, and use an iIACS machine. The guide includes the consideration of several important design parameters, such as throughput, sensitivity, dynamic range, image quality, sort purity, and sort yield; the development and integration of optical, microfluidic, electrical, computational, and mechanical components; and the characterization and practical usage of the integrated system. Assuming that all components are readily available, a team of several researchers experienced in optics, electronics, digital signal processing, microfluidics, mechatronics, and flow cytometry can complete this protocol in ~3 months. A protocol for the design, construction, and operation of an intelligent image-activated cell sorting (iIACS) machine that performs real-time image-based sorting of single cells from heterogeneous populations with high throughput and intelligence.