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)

Abstract

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.

References

  1. 1.

    Nitta, N. et al. Intelligent image-activated cell sorting. Cell 175, 266–276 (2018).

    CAS  PubMed  Google Scholar 

  2. 2.

    Mikami, H. et al. Ultrafast confocal fluorescence microscopy beyond the fluorescence lifetime limit. Optica 5, 117–126 (2018).

    CAS  Google Scholar 

  3. 3.

    Kanno, H., Mikami, H., Kaya, Y., Ozeki, Y. & Goda, K. Simple, stable, compact implementation of frequency-division-multiplexed microscopy by inline interferometry. Opt. Lett. 44, 467–470 (2019).

    PubMed  Google Scholar 

  4. 4.

    Shivhare, P. K., Bhadra, A., Sajeesh, P., Prabhakar, A. & Sen, A. K. Hydrodynamic focusing and interdistance control of particle-laden flow for microflow cytometry. Microfluid. Nanofluidics 20, 86 (2016).

    Google Scholar 

  5. 5.

    Park, J. W. et al. Acoustofluidic harvesting of microalgae on a single chip. Biomicrofluidics 10, 034119 (2016).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Grenvall, C., Antfolk, C., Bisgaard, C. Z. & Laurell, T. Two-dimensional acoustic particle focusing enables sheathless chip Coulter counter with planar electrode configuration. Lab Chip 14, 4629–4637 (2014).

    CAS  PubMed  Google Scholar 

  7. 7.

    Sakuma, S., Kasai, Y., Hayakawa, T. & Arai, F. On-chip cell sorting by high-speed local-flow control using dual membrane pumps. Lab Chip 17, 2760–2767 (2017).

    CAS  PubMed  Google Scholar 

  8. 8.

    Lecun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    CAS  PubMed  Google Scholar 

  9. 9.

    Goodfellow, I., Bengio, Y., Courville, A. & Bengio, Y. Deep Learning (MIT Press, Cambridge, 2016).

  10. 10.

    LeCun, Y. et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541–551 (1989).

    Google Scholar 

  11. 11.

    Krizhevesky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. In Proc. 25th International Conference on Neural Information Processing Systems (NIPS 2012) (eds Pereira, F., Burges, C. J. C., Bottou, L. & Weinberger, K. Q.) 1097–1105 (Curran Associates, 2012).

  12. 12.

    Herzenberg, L. A. et al. The history and future of the fluorescence activated cell sorter and flow cytometry: a view from Stanford. Clin. Chem. 48, 1819–1827 (2002).

    CAS  Google Scholar 

  13. 13.

    Tung, J. W. et al. Modern flow cytometry: a practical approach. Clin. Lab. Med. 27, 453–468 (2007).

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Liu, L., Cheung, T. H., Charville, G. W. & Rando, T. A. Isolation of skeletal muscle stem cells by fluorescence-activated cell sorting. Nat. Protoc. 10, 1612–1624 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Hayatsu, N. et al. Analyses of a mutant Foxp3 allele reveal BATF as a critical transcription factor in the differentiation and accumulation of tissue regulatory T cells. Immunity 47, 268–283 (2017).

    CAS  PubMed  Google Scholar 

  16. 16.

    de St Groth, B. F., Zhu, E. rhu., Asad, S. & Lee, L. Flow cytometric detection of human regulatory T cells. Methods Mol. Biol. 707, 263–279 (2011)

  17. 17.

    Shapiro, H. M. Practical Flow Cytometry (John Wiley & Sons, 2005).

  18. 18.

    Herzenberg, L. A., Gottlinger, C., Muller, W., Radbruch, A. & Recktenwald, D. Flow Cytometry and Cell Sorting (Springer, 1992).

  19. 19.

    Lindmo, T., Peters, D. C. & Sweet, R. G. Flow Cytometry and Sorting (Wiley-Liss, 1990).

  20. 20.

    Kawata, S., Hori, M., Kado, H. & Tamiya, E. Biological Imaging and Sensing (Springer, 2004).

  21. 21.

    Wang, P. & Wu, C. Micro/Nano Cell and Molecular Sensors (Springer, 2016).

  22. 22.

    Boutros, M., Heigwer, F. & Laufer, C. Microscopy-based high-content screening. Cell 163, 1314–1325 (2015).

    CAS  PubMed  Google Scholar 

  23. 23.

    Caicedo, J. C. et al. Data-analysis strategies for image-based cell profiling. Nat. Methods 14, 849–863 (2017).

    CAS  PubMed  Google Scholar 

  24. 24.

    Boutros, M. & Ahringer, J. The art and design of genetic screens: RNA interference. Nat. Rev. Genet. 9, 554–566 (2008).

    CAS  PubMed  Google Scholar 

  25. 25.

    Carpenter, A. E. Image-based chemical screening. Nat. Chem. Biol. 3, 461–465 (2007).

    CAS  PubMed  Google Scholar 

  26. 26.

    Boutros, M. et al. Genome-wide RNAi analysis of growth and viability in Drosophila cells. Science 303, 832–835 (2004).

    CAS  PubMed  Google Scholar 

  27. 27.

    Lum, L. et al. Identification of Hedgehog pathway components by RNAi in Drosophila cultured cells. Science 299, 2039–2045 (2003).

    CAS  PubMed  Google Scholar 

  28. 28.

    Kiger, A. et al. A functional genomic analysis of cell morphology using RNA interference. J. Biol. 2, 27 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Liu, T., Sims, D. & Baum, B. Parallel RNAi screens across different cell lines identify generic and cell type-specific regulators of actin organization and cell morphology. Genome Biol. 10, R26 (2009).

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Arpali, S. A., Arpali, C., Coskun, A. F., Chiang, H. H. & Ozcan, A. High-throughput screening of large volumes of whole blood using structured illumination and fluorescent on-chip imaging. Lab Chip 12, 4968–4971 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Zhang, Y. et al. High-throughput screening of encapsulated islets using wide-field lens-free on-chip imaging. ACS Photonics 5, 2081–2086 (2018).

    CAS  Google Scholar 

  32. 32.

    Lei, C., Guo, B., Cheng, Z. & Goda, K. Optical time-stretch imaging: principles and applications. Appl. Phys. Rev. 3, 011102 (2016).

    Google Scholar 

  33. 33.

    Mikami, H. et al. High-speed imaging meets single-cell analysis. Chem 4, 2278–2300 (2018).

    CAS  Google Scholar 

  34. 34.

    Mikami, H., Gao, L. & Goda, K. Ultrafast optical imaging technology: principles and applications of emerging methods. Nanophotonics 5, 497–509 (2016).

    Google Scholar 

  35. 35.

    Porichis, F. et al. High-throughput detection of miRNAs and gene-specific mRNA at the single-cell level by flow cytometry. Nat. Commun. 5, 5641 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Wu, J. L. et al. Ultrafast laser-scanning time-stretch imaging at visible wavelengths. Light Sci. Appl. 6, e16196 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Mahjoubfar, A. et al. Time stretch and its applications. Nat. Photonics 11, 341–351 (2017).

    CAS  Google Scholar 

  38. 38.

    Lai, Q. T. K. et al. High-throughput time-stretch imaging flow cytometry for multi-class classification of phytoplankton. Opt. Express 24, 28170–28184 (2016).

    PubMed  Google Scholar 

  39. 39.

    Han, Y. & Lo, Y. Imaging cells in flow cytometer using spatial-temporal transformation. Sci. Rep. 5, 13267 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Han, Y., Gu, Y., Zhang, A. C. & Lo, Y. H. Review: imaging technologies for flow cytometry. Lab Chip 16, 4639–4647 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Rane, A. S., Rutkauskaite, J., DeMello, A. & Stavrakis, S. High-throughput multi-parametric imaging flow cytometry. Chem 3, 588–602 (2017).

    CAS  Google Scholar 

  42. 42.

    Miura, T. et al. On-chip light-sheet fluorescence imaging flow cytometry at a high flow speed of 1 m/s. Biomed. Opt. Express 9, 3424–3433 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Jiang, Y. et al. Label-free detection of aggregated platelets in blood by machine-learning-aided optofluidic time-stretch microscopy. Lab Chip 17, 2426–2434 (2017).

    CAS  PubMed  Google Scholar 

  44. 44.

    George, T. C. et al. Distinguishing modes of cell death using the ImageStream® multispectcal imaging flow cytometer. Cytometry A 59A, 237–245 (2004).

    Google Scholar 

  45. 45.

    Kobayashi, H. et al. Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning. Sci. Rep. 7, 12454 (2017).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Muñoz, H. E. et al. Single-cell analysis of morphological and metabolic heterogeneity in Euglena gracilis by fluorescence-imaging flow cytometry. Anal. Chem. 90, 11280–11289 (2018).

    PubMed  Google Scholar 

  47. 47.

    Guo, B. et al. High-throughput, label-free, single-cell, microalgal lipid screening by machine-learning-equipped optofluidic time-stretch quantitative phase microscopy. Cytometry A 91A, 494–502 (2017).

    Google Scholar 

  48. 48.

    George, T. C. et al. Quantitative measurement of nuclear translocation events using similarity analysis of multispectral cellular images obtained in flow. J. Immunol. Methods 311, 117–129 (2006).

    CAS  PubMed  Google Scholar 

  49. 49.

    Basiji, D. A., Ortyn, W. E., Liang, L., Venkatachalam, V. & Morrissey, P. Cellular image analysis and imaging by flow cytometry. Clin. Lab. Med. 27, 653–670 (2007).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Lee, D., Mehta, N., Shearer, A. & Kastner, R. A hardware accelerated system for high throughput cellular image analysis. J. Parallel Distrib. Comput. 113, 167–178 (2018).

    Google Scholar 

  51. 51.

    Goda, K. & Jalali, B. Dispersive Fourier transformation for fast continuous single-shot measurements. Nat. Photonics 7, 102–112 (2013).

    CAS  Google Scholar 

  52. 52.

    Wong, T. T. W. et al. Asymmetric-detection time-stretch optical microscopy (ATOM) for ultrafast high-contrast cellular imaging in flow. Sci. Rep. 4, 3656 (2014).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Lau, A. K. S., Shum, H. C., Wong, K. K. Y. & Tsia, K. K. Optofluidic time-stretch imaging-an emerging tool for high-throughput imaging flow cytometry. Lab Chip 16, 1743–1756 (2016).

    CAS  PubMed  Google Scholar 

  54. 54.

    Lei, C. et al. High-throughput imaging flow cytometry by optofluidic time-stretch microscopy. Nat. Protoc. 13, 1603–1631 (2018).

    CAS  PubMed  Google Scholar 

  55. 55.

    Guo, B. et al. Optofluidic time-stretch quantitative phase microscopy. Methods 136, 116–125 (2018).

    CAS  PubMed  Google Scholar 

  56. 56.

    Goda, K. et al. High-throughput single-microparticle imaging flow analyzer. Proc. Natl. Acad. Sci. USA 109, 11630–11635 (2012).

    CAS  PubMed  Google Scholar 

  57. 57.

    Lei, C., Nitta, N., Ozeki, Y. & Goda, K. Optofluidic time-stretch microscopy: recent advances. Opt. Rev. 25, 464–472 (2018).

    CAS  Google Scholar 

  58. 58.

    Lei, C. et al. GHz optical time-stretch microscopy by compressive sensing. IEEE Photonics J. 9, 1–8 (2017).

    Google Scholar 

  59. 59.

    Hiraki, K. et al. All-IP-Ethernet architecture for real-time sensor-fusion processing. In Proc. SPIE BiOS 9720 97200D (2016). https://doi.org/10.1117/12.2212016

  60. 60.

    Inaba, M. & Hiraki, K. Network processing hardware. In Proc. Second Asian International Conference on Technologies for Advanced Heterogeneous Network (eds Cho, K. & Jacquet, P.) 103–112 (Springer, 2006).

  61. 61.

    Okada, K. et al. Protocol design for all-IP computer architecture. In Proc. International Conference on Information Networking 2008 (ICOIN2008) (eds Kaiser, B., Madden, S. & Suri, S.) 1–5 (IEEE, 2008).

  62. 62.

    Hao, N., Budnik, Ba & Gunawardena, J. Tunable signal processing through modular control of transcription factor translocation. Science 339, 460–464 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Von Erlach, T. C. et al. Cell-geometry-dependent changes in plasma membrane order direct stem cell signalling and fate. Nat. Mater. 17, 237–242 (2018).

    Google Scholar 

  64. 64.

    Sarioglu, A. F. et al. A microfluidic device for label-free, physical capture of circulating tumor cell clusters. Nat. Methods 12, 685–691 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Moor, A. E. et al. Global mRNA polarization regulates translation efficiency in the intestinal epithelium. Science 357, 1299–1303 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Zenker, J. et al. A microtubule-organizing center directing intracellular transport in the early mouse embryo. Science 357, 925–928 (2017).

    CAS  PubMed  Google Scholar 

  67. 67.

    Pernas, L., Bean, C., Boothroyd, J. C. & Scorrano, L. Mitochondria restrict growth of the intracellular parasite Toxoplasma gondii by limiting its uptake of fatty acids. Cell Metab. 27, 886–897 (2018).

    CAS  PubMed  Google Scholar 

  68. 68.

    Cho, E. H. et al. Characterization of circulating tumor cell aggregates identified in patients with epithelial tumors. Phys. Biol. 9, 016001 (2012).

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Aceto, N. et al. Circulating tumor cell clusters are oligoclonal precursors of breast cancer metastasis. Cell 158, 1110–1122 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Molnar, B., Ladanyi, A., Tanko, L., Sréter, L. & Tulassay, Z. Circulating tumor cell clusters in the peripheral blood of colorectal cancer patients. Clin. Cancer Res. 7, 4080–4085 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Wang, L. et al. Chloroplast-mediated regulation of CO2-concentrating mechanism by Ca2+-binding protein CAS in the green alga Chlamydomonas reinhardtii. Proc. Natl. Acad. Sci. USA 113, 12586–12591 (2016).

    CAS  PubMed  Google Scholar 

  72. 72.

    Mackinder, L. C. M. et al. A spatial interactome reveals the protein organization of the algal CO2-concentrating mechanism. Cell 171, 133–147 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Ohnuki, S. & Ohya, Y. High-dimensional single-cell phenotyping reveals extensive haploinsufficiency. PLoS Biol. 16, 1–23 (2018).

    Google Scholar 

  74. 74.

    Suzuki, G. et al. Global study of holistic morphological effectors in the budding yeast Saccharomyces cerevisiae. BMC Genomics 19, 149 (2018).

    PubMed  PubMed Central  Google Scholar 

  75. 75.

    Iwaki, A., Ohnuki, S., Suga, Y., Izawa, S. & Ohya, Y. Vanillin inhibits translation and induces messenger ribonucleoprotein (mRNP) granule formation in Saccharomyces cerevisiae: application and validation of high-content, image-based profiling. PLoS ONE 8, e61748 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Treiser, M. D. et al. Cytoskeleton-based forecasting of stem cell lineage fates. Proc. Natl. Acad. Sci. USA 107, 610–615 (2010).

    CAS  PubMed  Google Scholar 

  77. 77.

    Thery, M. et al. Anisotropy of cell adhesive microenvironment governs cell internal organization and orientation of polarity. Proc. Natl. Acad. Sci. USA 103, 19771–19776 (2006).

    CAS  PubMed  Google Scholar 

  78. 78.

    Wu, C. Y. et al. Shaped 3D microcarriers for adherent cell culture and analysis. Microsyst. Nanoeng. 4, 21 (2018).

    PubMed  PubMed Central  Google Scholar 

  79. 79.

    Lancaster, M. A. & Knoblich, J. A. Organogenesis in a dish: modeling development and disease using organoid technologies. Science 345, 1247125 (2014).

    PubMed  Google Scholar 

  80. 80.

    Orange, J. S. Formation and function of the lytic NK-cell immunological synapse. Nat. Rev. Immunol. 8, 713–725 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Dustin, M. L., Chakraborty, A. K. & Shaw, A. S. Understanding the structure and function of the immunological synapse. Cold Spring Harb. Perspect. Biol. 2, a002311 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Ingham, P. W. The molecular genetics of embryonic pattern formation in Drosophila. Nature 335, 25–34 (1988).

    CAS  PubMed  Google Scholar 

  83. 83.

    Mullins, M. C., Hammerschmidt, M., Haffter, P. & Nüsslein-Volhard, C. Large-scale mutagenesis in the zebrafish: in search of genes controlling development in a vertebrate. Curr. Biol. 4, 189–202 (1994).

    CAS  PubMed  Google Scholar 

  84. 84.

    Fabritius, A. et al. Imaging-based screening platform assists protein engineering. Cell Chem. Biol. 25, 1554–1561 (2018).

    CAS  PubMed  Google Scholar 

  85. 85.

    Környei, Z. et al. Cell sorting in a Petri dish controlled by computer vision. Sci. Rep. 3, 1–10 (2013).

    Google Scholar 

  86. 86.

    Das, A. et al. Adaptive from innate: human IFN-γ+ CD4+ T cells can arise directly from CXCL8-producing recent thymic emigrants in babies and adults. J. Immunol. 199, 1696–1705 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Jin, A. et al. A rapid and efficient single-cell manipulation method for screening antigen-specific antibody-secreting cells from human peripheral blood. Nat. Med. 15, 1088–1092 (2009).

    CAS  PubMed  Google Scholar 

  88. 88.

    Yoshimoto, N. et al. An automated system for high-throughput single cell-based breeding. Sci. Rep. 3, 1191 (2013).

    PubMed  PubMed Central  Google Scholar 

  89. 89.

    Dura, B. et al. Longitudinal multiparameter assay of lymphocyte interactions from onset by microfluidic cell pairing and culture. Proc. Natl. Acad. Sci. USA 113, E3599–E3608 (2016).

    CAS  PubMed  Google Scholar 

  90. 90.

    Ogunniyi, A. O., Story, C. M., Papa, E., Guillen, E. & Love, J. C. Screening individual hybridomas by microengraving to discover monoclonal antibodies. Nat. Protoc. 4, 767–782 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Yao, X. et al. Tumor cells are dislodged into the pulmonary vein during lobectomy. J. Thorac. Cardiovasc. Surg. 148, 3224–3231 (2014).

    PubMed  PubMed Central  Google Scholar 

  92. 92.

    Piatkevich, K. D. et al. A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters. Nat. Chem. Biol. 14, 352–360 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. 93.

    Brasko, C. et al. Intelligent image-based in situ single-cell isolation. Nat. Commun. 9, 1–7 (2018).

    CAS  Google Scholar 

  94. 94.

    Grys, B. T. et al. Machine learning and computer vision approaches for phenotypic profiling. J. Cell Biol. 216, 65–71 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Hennig, H. et al. An open-source solution for advanced imaging flow cytometry data analysis using machine learning. Methods 112, 201–210 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Abadi, M. et al. Tensorflow: a system for large-scale machine learning. In Proc. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16) 265–283 (USENIX, 2016).

  97. 97.

    Chollet, F. Keras: the Python deep learning library. https://keras.io (2015).

  98. 98.

    Kasai, Y., Sakuma, S. & Arai, F. On-chip multi-sorting using high-speed and high-accuracy flow control. In Proc. 22nd International Conference on Miniaturized Systems for Chemistry and Life Sciences (MicroTAS2018) (eds Tseng, F.-G. & Lee, G.-B.) 1237–1238 (Chemical and Biological Microsystems Society, 2018).

  99. 99.

    Paszke, A. et al. Automatic differentiation in PyTorch. In Proc. 31st Conference on Neural Information Processing Systems (NIPS 2017) (eds Guyon, I. et al.)1–4 (Curran Associates, 2017).

  100. 100.

    Tokui, S., Oono, K., Hido, S. & Clayton, J. Chainer: a next-generation open source framework for deep learning. In Proc. Conference on Neural Information Processing Systems (NIPS 2015) (eds Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M. & Garnett, R.) 1–4 (Curran Associates, 2015).

  101. 101.

    Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100.1–R100.11 (2006).

    Google Scholar 

  102. 102.

    Abrams, C. S. et al. Direct detection of activated platelets and platelet-derived microparticles in humans. Blood 75, 128–138 (1990).

    CAS  PubMed  Google Scholar 

  103. 103.

    Shalek, A. K. et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363–369 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104.

    Kalisky, T. & Quake, S. R. Single-cell genomics. Nat. Methods 8, 311–314 (2011).

    CAS  PubMed  Google Scholar 

  105. 105.

    Rotem, A. et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Yamano, T. et al. Light and low-CO2-dependent LCIB-LCIC complex localization in the chloroplast supports the carbon-concentrating mechanism in Chlamydomonas reinhardtii. Plant Cell Physiol. 51, 1453–1468 (2010).

    CAS  PubMed  Google Scholar 

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Acknowledgements

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.

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Authors

Contributions

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.

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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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