A practical guide to intelligent image-activated cell sorting

An Author Correction to this article was published on 17 October 2019

This article has been updated (view changelog)


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.

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Fig. 1: Overview of the procedure.
Fig. 2: Schematic of the iIACS machine.
Fig. 3: The iIACS machine.
Fig. 4: Schematic of the liquid pump.
Fig. 5: Schematic of the microfluidic chip.
Fig. 6: Construction of the microfluidic chip.
Fig. 7: Schematic and characterization of the cell focuser.
Fig. 8: Schematic of the FDM microscope.
Fig. 9: Characterization of the FDM microscope.
Fig. 10: Schematic and characterization of the speed meter.
Fig. 11: Design of the image processor.
Fig. 12: Schematic and characterization of the cell sorter.
Fig. 13: Schematic of the optics–microfluidics integration unit.
Fig. 14: Throughput performance of the iIACS machine.
Fig. 15: Sorting performance of the iIACS machine.
Fig. 16: Images of various types of cells obtained by the iIACS machine.
Fig. 17: Flowchart of how to operate the iIACS machine.
Fig. 18: High-content sorting of rare Chlamydomonas reinhardtii mutants with the iIACS machine.
Fig. 19: High-content sorting of platelet aggregates in human blood with the iIACS machine.

Data and code availability

The data and code are available as Supplementary Data and upon reasonable request.

Change history

  • 17 October 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.


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This work was supported primarily by the ImPACT program of the Council for Science, Technology, and Innovation (Cabinet Office, Government of Japan) and partly by the JSPS Core-to-Core Program and White Rock Foundation. We thank M. Kanematsu, M. Urakawa, A. Komiya, and S. Aihara for assistance. N.N. is an ISAC Marylou Ingram Scholar.

Author information




K.G. conceived iIACS. A.I., N.N., T. Iino, and K.G. designed the protocol. A.I., H.M., K. Hiramatsu, S.S., Y.K., T. Iino, T.Y., A.Y., Y. Oguchi, N.S., Y.S., T. Ito, K. Hiraki, S.M., T.H., F.A., T.S., Y. Ozeki, and N.N. performed the experiments. H.F. and Y.Y. helped prepare the blood and microalgal samples. T.E., M.Y., and T.S. developed the digital image-processing algorithms. K. Hiraki developed the all-IP network. A.I., H.M., K. Hiramatsu, S.S., Y.K., T. Iino, T.Y., A.Y., Y. Ozeki, F.A., T.S., Y. Oguchi, N.N., and K.G. prepared the figures and tables. K.G. supervised the work with the help of T. Ito, Y. Hoshino, Y. Hosokawa, A.N., S.U., T.S., Y. Ozeki, and N.N. A.I., H.M., K. Hiramatsu, S.S., Y.S., M.Y., D.D., T.S., N.N., and K.G. mainly wrote the manuscript. All authors contributed to the writing of the manuscript.

Corresponding author

Correspondence to Keisuke Goda.

Ethics declarations

Competing interests

H.M. and K.G. are inventors on a patent covering the FDM microscope. S.S., F.A., and T.H. are inventors on a patent application covering the dual-membrane push–pull cell sorter. N.N., T.S., and K.G. are inventors on a patent covering the data analysis and display method. N.N. is the president of CYBO, Inc. N.N., T.S., and K.G. are shareholders of CYBO, Inc.

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Peer review information: Nature Protocols thanks Kenneth K. Y. Wong and other anonymous reviewer(s) for their contribution to the peer review of this work.

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Related links

Key references using this protocol

Nitta, N. et al. Cell 175, 266–276.e13 (2018): https://www.cell.com/cell/fulltext/S0092-8674(18)31044-4

Mikami, H. et al. Optica 5, 117–126 (2018): https://doi.org/10.1364/OPTICA.5.000117

Sakuma, S., Kasai, Y., Hayakawa, T. & Arai, F. Lab Chip 17, 2760–2767 (2017): https://pubs.rsc.org/en/content/articlelanding/2017/lc/c7lc00536a

Supplementary information

Supplementary Video 1

Operation of the iIACS machine. The iIACS machine is composed of optical, microfluidic, electrical, computational, and mechanical parts. An interdisciplinary team of trained operators is needed to run the iIACS machine. First, a sample of suspended cells is prepared before a sorting run. Second, a tube containing the sample is placed at the injection port for the sorting run. Third, the process of each subsystem is monitored on multiple computer panels during the sorting run. Fourth, when the sorting run is finished, collection and waste tubes containing sorted and unsorted cells, respectively, are removed from the iIACS machine. Fifth, cells in the tubes are inspected under an optical microscope to evaluate the results of the sorting run. Sixth, the microscope images are automatically analyzed and then manually verified. Finally, the operators discuss the outcomes and reach a conclusion.

Reporting Summary

Supplementary Data 1

AutoCAD design file for the microfluidic chip.

Supplementary Data 2

SolidWorks design file for the optics–microfluidic integration unit.

Supplementary Data 3

Source codes for the IA node.

Supplementary Data 4

Source codes for the TM node.

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Isozaki, A., Mikami, H., Hiramatsu, K. et al. A practical guide to intelligent image-activated cell sorting. Nat Protoc 14, 2370–2415 (2019). https://doi.org/10.1038/s41596-019-0183-1

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