The ability to rapidly assay morphological and intracellular molecular variations within large heterogeneous populations of cells is essential for understanding and exploiting cellular heterogeneity. Optofluidic time-stretch microscopy is a powerful method for meeting this goal, as it enables high-throughput imaging flow cytometry for large-scale single-cell analysis of various cell types ranging from human blood to algae, enabling a unique class of biological, medical, pharmaceutical, and green energy applications. Here, we describe how to perform high-throughput imaging flow cytometry by optofluidic time-stretch microscopy. Specifically, this protocol provides step-by-step instructions on how to build an optical time-stretch microscope and a cell-focusing microfluidic device for optofluidic time-stretch microscopy, use it for high-throughput single-cell image acquisition with sub-micrometer resolution at >10,000 cells per s, conduct image construction and enhancement, perform image analysis for large-scale single-cell analysis, and use computational tools such as compressive sensing and machine learning for handling the cellular ‘big data’. Assuming all components are readily available, a research team of three to four members with an intermediate level of experience with optics, electronics, microfluidics, digital signal processing, and sample preparation can complete this protocol in a time frame of 1 month.

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1. Goda, K., Tsia, K. K. & Jalali, B. Serial time-encoded amplified imaging for real-time observation of fast dynamic phenomena. Nature 458, 1145–1149 (2009). https://doi.org/10.1038/nature07980

2. Goda, K. et al. High-throughput single-microparticle imaging flow analyzer. Proc. Natl. Acad. Sci. USA 109, 11630–11635 (2012). https://doi.org/10.1073/pnas.1204718109

3. Lei, C., Guo, B., Cheng, Z. & Goda, K. Optical time-stretch imaging: principles and applications. Appl. Phys. Rev. 3, 011102 (2016). https://doi.org/10.1063/1.4941050

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  1. 1.

    Goda, K., Tsia, K. K. & Jalali, B. Serial time-encoded amplified imaging for real-time observation of fast dynamic phenomena. Nature 458, 1145–1149 (2009).

  2. 2.

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

  3. 3.

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

  4. 4.

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

  5. 5.

    Jiang, Y. et al. Label-free detection of aggregated platelets in blood by machine-learning-aided optofluidic time-stretch microscopy. Lab Chip 17, 2337–2530 (2017).

  6. 6.

    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).

  7. 7.

    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 (2007).

  8. 8.

    Basiji, D. & O’Gorman, M. R. G. Imaging flow cytometry. J. Immunol. Methods 423, 1–2 (2015).

  9. 9.

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

  10. 10.

    Shapiro, H. M. Practical Flow Cytometry 4th edn (John Wiley & Sons, Hoboken, NJ, USA, 2003).

  11. 11.

    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).

  12. 12.

    Wu, J. L. et al. Ultrafast laser-scanning time-stretch imaging at visible wavelengths. Light Sci. Appl. 6, e16196 (2016).

  13. 13.

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

  14. 14.

    Ugawa, M. et al. High-throughput optofluidic particle profiling with morphological and chemical specificity. Opt. Lett. 40, 4803–4806 (2015).

  15. 15.

    Lei, C. et al. High-throughput label-free image cytometry and image-based classification of live Euglena gracilis. Biomed. Opt. Express 7, 2703–2708 (2016).

  16. 16.

    Chen, C. L. et al. Deep learning in label-free cell classification. Sci. Rep. 6, 21471 (2016).

  17. 17.

    Chan, A. C. et al. All-passive pixel super-resolution of time-stretch imaging. Sci. Rep. 7, 44608 (2017).

  18. 18.

    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).

  19. 19.

    Guo, B. et al. High-throughput accurate single-cell screening of Euglena gracilis with fluorescence-assisted optofluidic time-stretch microscopy. PLoS ONE 11, e0166214 (2016).

  20. 20.

    Mahjoubfar, A., Chen, C., Niazi, K. R., Rabizadeh, S. & Jalali, B. Label-free high-throughput cell screening in flow. Biomed. Opt. Express 4, 1618–1625 (2013).

  21. 21.

    Lau, A. K. S. et al. Interferometric time-stretch microscopy for ultrafast quantitative cellular and tissue imaging at 1 μm. J. Biomed. Opt. 19, 076001 (2014).

  22. 22.

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

  23. 23.

    Tang, A. H. et al. Microfluidic imaging flow cytometry by asymmetric-detection time-stretch optical microscopy (ATOM). J. Vis. Exp. 124, e55840 (2017).

  24. 24.

    Tang, A. H. et al. Time-stretch microscopy on a DVD for high-throughput imaging cell-based assay. Biomed. Opt. Express 8, 640–652 (2017).

  25. 25.

    Wu, J. et al. Multi-MHz laser-scanning single-cell fluorescence microscopy by spatiotemporally encoded virtual source array. Biomed. Opt. Express 8, 4160–4171 (2017).

  26. 26.

    Goda, K. et al. High-throughput optical coherence tomography at 800 nm. Opt. Express 20, 19612–19617 (2012).

  27. 27.

    Li, M. et al. Inertial focusing of ellipsoidal Euglena gracilis cells in a stepped microchannel. Lab Chip 16, 4458–4465 (2016).

  28. 28.

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

  29. 29.

    Guo, B. et al. Optofluidic time-stretch quantitative phase microscopy. Methods 136, 116–125 (2017).

  30. 30.

    Lei, C. et al. GHz optical time-stretch microscopy by compressive sensing. Photonics J. 9, 3900308 (2017).

  31. 31.

    Yan, W., Wu, J., Wong, K. K. & Tsia, K. K. A high-throughput all-optical laser-scanning imaging flow cytometer with biomolecular specificity and subcellular resolution. J. Biophotonics 11, e201700178 (2017).

  32. 32.

    Guo, Q. et al. High-speed compressive microscopy of flowing cells using sinusoidal illumination patterns. Photonics J. 9, 1–11 (2017).

  33. 33.

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

  34. 34.

    Goda, K. et al. Hybrid dispersion laser scanner. Sci. Rep. 2, 445 (2012).

  35. 35.

    Lei, C. et al. Time-stretch high-speed microscopic imaging system based on temporally and spectrally shaped amplified spontaneous emission. Opt. Lett. 40, 946–949 (2015).

  36. 36.

    Chen, H. et al. Multiwavelength time-stretch imaging system. Opt. Lett. 39, 2202–2205 (2014).

  37. 37.

    Xing, F. et al. Serial wavelength division 1 GHz line-scan microscopic imaging. Photonics Res. 2, B31–B34 (2014).

  38. 38.

    Xing, F., Chen, H., Xie, S. & Yao, J. Ultrafast three-dimensional surface imaging based on short-time Fourier transform. IEEE Photonics Technol. Lett. 27, 2264–2267 (2015).

  39. 39.

    Guo, Q. et al. Fast time-lens-based line-scan single-pixel camera with multi-wavelength source. Biomed. Opt. Express 6, 3610–3617 (2015).

  40. 40.

    Xing, F., Chen, H., Xie, S. & Yao, J. Ultrafast surface imaging with an increased spatial resolution based on polarization-division multiplexing. J. Lightwave Technol. 33, 396–402 (2015).

  41. 41.

    Guo, Q. et al. Compressive sensing based high-speed time-stretch optical microscopy for two-dimensional image acquisition. Opt. Express 23, 29639–29646 (2015).

  42. 42.

    Chen, H. et al. Ultrafast web inspection with hybrid dispersion laser scanner. Appl. Opt. 52, 4072–4076 (2013).

  43. 43.

    Xing, F., Chen, H., Chen, M., Yang, S. & Xie, S. Simple approach for fast real-time line scan microscopic imaging. Appl. Opt. 52, 7049–7053 (2013).

  44. 44.

    Wang, G., Wang, C., Yan, Z. & Zhang, L. Highly efficient spectrally encoded imaging using a 45° tilted fiber grating. Opt. Lett. 41, 2398–2401 (2016).

  45. 45.

    Xu, Y., Ren, Z., Wong, K. K. Y. & Tsia, K. Overcoming the limitation of phase retrieval using Gerchberg-Saxton-like algorithm in optical fiber time-stretch systems. Opt. Lett. 40, 3595–3598 (2015).

  46. 46.

    Xu, J. et al. High-performance multi-megahertz optical coherence tomography based on amplified optical time-stretch. Biomed. Opt. Express 6, 1340–1350 (2015).

  47. 47.

    Zhang, C., Xu, Y., Wei, X., Tsia, K. K. & Wong, K. K. Y. Time-stretch microscopy based on time-wavelength sequence reconstruction from wideband incoherent source. Appl. Phys. Lett. 105, 041113 (2014).

  48. 48.

    Wei, X. et al. Breathing laser as an inertia-free swept source for high-quality ultrafast optical bioimaging. Opt. Lett. 39, 6593–6596 (2014).

  49. 49.

    Wei, X. et al. Broadband fiber-optical parametric amplification for ultrafast time-stretch imaging at 1.0 μm. Opt. Lett. 39, 5989–5992 (2014).

  50. 50.

    Wei, X. et al. Coherent laser source for high frame-rate optical time-stretch microscopy at 1.0 μm. IEEE J. Sel. Top. Quantum 20, 1100306 (2014).

  51. 51.

    Wong, T. T. W., Lau, A. K. S., Wong, K. K. Y. & Tsia, K. K. Optical time-stretch confocal microscopy at 1 μm. Opt. Lett. 37, 3330–3332 (2012).

  52. 52.

    Qiu, Y., Xu, J., Wong, K. K. Y. & Tsia, K. K. Exploiting few mode-fibers for optical time-stretch confocal microscopy in the short near-infrared window. Opt. Express 20, 24115–24123 (2012).

  53. 53.

    Zhang, C., Qiu, Y., Zhu, R., Wong, K. K. Y. & Tsia, K. K. Serial time-encoded amplified microscopy (STEAM) based on a stabilized picosecond supercontinuum source. Opt. Express 19, 15810–15816 (2011).

  54. 54.

    Tsia, K. K., Goda, K., Capewell, D. & Jalali, B. Performance of serial time-encoded amplified microscope. Opt. Express 18, 10016–10028 (2010).

  55. 55.

    Goda, K., Solli, D. R., Tsia, K. K. & Jalali, B. Theory of amplified dispersive Fourier transformation. Phys. Rev. A 80, 033827 (2009).

  56. 56.

    Tsia, K. K., Goda, K., Capewell, D. & Jalali, B. Simultaneous mechanical-scan-free confocal microscopy and laser microsurgery. Opt. Lett. 34, 2099–2101 (2009).

  57. 57.

    Goda, K., Tsia, K. K. & Jalali, B. Amplified dispersive Fourier-transform imaging for ultrafast displacement sensing and barcode reading. Appl. Phys. Lett. 93, 131109 (2008).

  58. 58.

    Mahjoubfar, A. et al. High-speed nanometer-resolved imaging vibrometer and velocimeter. Appl. Phys. Lett. 98, 101107 (2011).

  59. 59.

    Fard, A. M. et al. Nomarski serial time-encoded amplified microscopy for high-speed contrast-enhanced imaging of transparent media. Biomed. Opt. Express 2, 3387–3392 (2011).

  60. 60.

    Kim, S. H., Goda, K., Fard, A. & Jalali, B. Optical time-domain analog pattern correlator for high-speed real-time image recognition. Opt. Lett. 36, 220–222 (2011).

  61. 61.

    Goda, K. & Jalali, B. Noise figure of amplified dispersive Fourier transformation. Phys. Rev. A 82, 043821 (2010).

  62. 62.

    Goda, K., Mahjoubfar, A. & Jalali, B. Demonstration of Raman gain at 800 nm in single-mode fiber and its potential application to biological sensing and imaging. Appl. Phys. Lett. 95, 251101 (2009).

  63. 63.

    Goda, K., Solli, D. R. & Jalali, B. Real-time optical reflectometry enabled by amplified dispersive Fourier transformation. Appl. Phys. Lett. 93, 031106 (2008).

  64. 64.

    Chen, C. L., Mahjoubfar, A. & Jalali, B. Optical data compression in time stretch imaging. PLoS ONE 10, e0125106 (2015).

  65. 65.

    Asghari, M. H. & Jalali, B. Warped time lens in temporal imaging for optical real-time data compression. Chinese Sci. Bull. 59, 2649–2654 (2014).

  66. 66.

    Asghari, M. H. & Jalali, B. Discrete anamorphic transform for image compression. IEEE Signal Proc. Lett. 21, 829–833 (2014).

  67. 67.

    Bosworth, B. T. et al. High-speed flow microscopy using compressed sensing with ultrafast laser pulses. Opt. Express 23, 10521–10532 (2015).

  68. 68.

    Dai, B. et al. Data compression for time-stretch imaging based on differential detection and run-length encoding. J. Lightwave Technol. 35, 5098–5104 (2017).

  69. 69.

    Dai, B. et al. Ultrafast three-dimensional imaging system based on phase-shifting method and hybrid dispersion laser scanning. Photonics J. 7, 6900509 (2015).

  70. 70.

    Diebold, E. D., Buckley, B. W., Gossett, D. R. & Jalali, B. Digitally synthesized beat frequency multiplexing for sub-millisecond fluorescence microscopy. Nat. Photonics 7, 806–810 (2013).

  71. 71.

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

  72. 72.

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

  73. 73.

    Beebe, D. J., Mensing, G. A. & Walker, G. M. Physics and applications of microfluidics in biology. Annu. Rev. Biomed. Eng. 4, 261–286 (2002).

  74. 74.

    Duncombe, T. A., Tentori, A. M. & Herr, A. E. Microfluidics: reframing biological enquiry. Nat. Rev. Mol. Cell Biol. 16, 554–567 (2015).

  75. 75.

    Croslandtaylor, P. J. A device for counting small particles suspended in a fluid through a tube. Nature 171, 37–38 (1953).

  76. 76.

    Lee, G. B., Chang, C. C., Huang, S. B. & Yang, R. J. The hydrodynamic focusing effect inside rectangular microchannels. J. Micromech. Microeng. 16, 1024–1032 (2006).

  77. 77.

    Golden, J. P., Justin, G. A., Nasir, M. & Ligler, F. S. Hydrodynamic focusing—a versatile tool. Anal. Bioanal. Chem. 402, 325–335 (2012).

  78. 78.

    Park, H. Y. et al. Achieving uniform mixing in a microfluidic device: hydrodynamic focusing prior to mixing. Anal. Chem. 78, 4465–4473 (2006).

  79. 79.

    Di Carlo, D. Inertial microfluidics. Lab Chip 9, 3038–3046 (2009).

  80. 80.

    Di Carlo, D., Irimia, D., Tompkins, R. G. & Toner, M. Continuous inertial focusing, ordering, and separation of particles in microchannels. Proc. Natl. Acad. Sci. USA 104, 18892–18897 (2007).

  81. 81.

    Amini, H., Lee, W. & Di Carlo, D. Inertial microfluidic physics. Lab Chip 14, 2739–2761 (2014).

  82. 82.

    Chung, A. J., Gossett, D. R. & Di Carlo, D. Three dimensional, sheathless, and high-throughput microparticle inertial focusing through geometry-induced secondary flows. Small 9, 685–690 (2013).

  83. 83.

    Li, M., Munoz, H. E., Goda, K. & Di Carlo, D. Shape-based separation of microalga Euglena gracilis using inertial microfluidics. Sci. Rep. 7, 10802 (2017).

  84. 84.

    Hur, S. C., Henderson-MacLennan, N. K., McCabe, E. R. B. & Di Carlo, D. Deformability-based cell classification and enrichment using inertial microfluidics. Lab Chip 11, 912–920 (2011).

  85. 85.

    Bengio, Y. Learning deep architectures for AI. Found. Trends Mach. Learn. 2, 1–127 (2009).

  86. 86.

    Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017).

  87. 87.

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

  88. 88.

    Kamentsky, L. et al. Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software. Bioinformatics 27, 1179–1180 (2011).

  89. 89.

    Bray, M. A. et al. Cell painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat. Protoc. 11, 1757–1774 (2016).

  90. 90.

    Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv Preprint at https://arxiv.org/abs/1409.1556 (2014).

  91. 91.

    He, K. M., Zhang, X. Y., Ren, S. Q. & Sun, J. Deep residual learning for image recognition. in Proc. 29th IEEE Conference on Computer Vision and Pattern Recognition: CVPR 2016 (eds. Agapito, L. et al.) 770–778 (IEEE, New York, 2016).

  92. 92.

    Yosinski, J., Clune, J., Bengio, Y. & Lipson, H. How transferable are features in deep neural networks? Adv. Neural Inf. Process. Syst. 27, 3320–3328 (2014).

  93. 93.

    Villani, C. Optimal Transport: Old and New (Springer Science & Business Media, Berlin, Germany, 2009).

  94. 94.

    Battiti, R. Using mutual information for selecting features in supervised neural net learning. IEEE Trans. Neural Network 5, 537–550 (1994).

  95. 95.

    Gretton, A., Borgwardt, K. M., Rasch, M., Schölkopf, B. & Smola, A. J. A kernel method for the two-sample-problem. Adv. Neural Inf. Process. Syst. 19, 513–520 (2006).

  96. 96.

    Schölkopf, B. & Smola, A. J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (The MIT Press, Cambridge, MA, USA, 2002).

  97. 97.

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

  98. 98.

    Candes, E. J. & Tao, T. Near-optimal signal recovery from random projections: universal encoding strategies? IEEE. Trans. Inform. Theory 52, 5406–5425 (2006).

  99. 99.

    Litjens, G. et al. A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017).

  100. 100.

    Xie, J., Xu, L. & Chen, E. Image denoising and inpainting with deep neural networks. Adv. Neural Inf. Process. Syst. 2012, 341–349 (2012).

  101. 101.

    Chang, J., Li, C. L., Póczos, B., Kumar, B. & Sankaranarayanan, A. C. One network to solve them all—solving linear inverse problems using deep projection models. arXiv Preprint at https://arxiv.org/abs/1703.09912 (2017).

  102. 102.

    Schuler, C. J., Hirsch, M., Harmeling, S. & Scholkopf, B. Learning to deblur. IEEE. T. Pattern Anal. 38, 1439–1451 (2016).

  103. 103.

    Rivenson, Y. et al. Deep learning microscopy. Optica 4, 1437–1443 (2017).

  104. 104.

    Yamada, M., Umezu, Y., Fukumizu, K. & Takeuchi, I. Post selection inference with kernels. arXiv Preprint at https://arxiv.org/abs/1610.03725 (2016).

  105. 105.

    Canny, J. F. A computational approach to edge detection. in Readings in Computer Vision: Issues, Problems, Principles, and Paradigms (eds. Fischler, M. A. & Firschein, O.) 184–203 (Morgan Kaufmann, San Francisco, 1987).

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This work was supported by the ImPACT Program of the Council for Science, Technology and Innovation (Cabinet Office, Government of Japan). The fabrication of the microfluidic devices was conducted at the University of Tokyo’s Center for Nano Lithography.

Author information


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

    • Cheng Lei
    • , Hirofumi Kobayashi
    • , Yi Wu
    • , Akihiro Isozaki
    • , Hideharu Mikami
    •  & Keisuke Goda
  2. Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA

    • Yi Wu
  3. Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, CA, USA

    • Ming Li
    •  & Keisuke Goda
  4. Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA

    • Ming Li
    •  & Dino Di Carlo
  5. Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    • Atsushi Yasumoto
    •  & Yutaka Yatomi
  6. Japan Science and Technology Agency, Kawaguchi, Japan

    • Takuro Ito
    • , Nao Nitta
    • , Takeaki Sugimura
    •  & Keisuke Goda
  7. Centre for Advanced Intelligence Project, RIKEN, Tokyo, Japan

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

    • Dino Di Carlo
  9. Jonsson Comprehensive Cancer Centre, University of California, Los Angeles, Los Angeles, CA, USA

    • Dino Di Carlo
  10. Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, Japan

    • Yasuyuki Ozeki


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C.L. and K.G. designed the protocol. C.L., H.K., and Y.W. performed the experiments. M.L. and A.I. helped fabricate the microfluidic devices. A.Y. and T.I. helped prepare the blood and microalga samples. H.M., N.N., and T.S. helped prepare the figures and tables. M.Y., Y.Y., D.D.C., Y.O., and K.G. supervised the project. All authors contributed to the writing of the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Cheng Lei or Keisuke Goda.

Integrated supplementary information

  1. Supplementary Figure 1 Design of microfluidic devices.

    Design of (a) hydrodynamic focusing and (b) inertial focusing microfluidic devices. Scale bar = 100 µm.

  2. Supplementary Figure 2 Photographs of microfluidic devices.

    Photographs of (a) hydrodynamic-focusing and (b) inertial-focusing microfluidic devices used in the experiments for cell focusing.

  3. Supplementary Figure 3 Procedure for preparing an E. gracilis sample.

    (I) Stock culture of E. gracilis NIES-48 in the plant growth chamber. (II) Inoculate the stock cells of E. gracilis to fresh AF-6. (III) To prepare fresh E. gracilis cells, inoculate 3 × 104 cells to fresh AF-6. (IV) To prepare lipid-accumulated E. gracilis cells, collect cells using a centrifuge, and then replace the culture medium with AF-6‒N (nitrogen nutrient omitted from AF-6) for rinsing. After rinsing, collect and resuspend cells in fresh AF-6‒N. All the cultures are incubated in the same conditions except for the culture media and cell density.

  4. Supplementary Figure 4 Procedure for preparing a human blood sample.

    (I) Prepare a 21-guage butterfly needle and a 4.5-mL vacuum plasma separator tube with 0.5-mL of 3.2% sodium citrate, Venoject II. (II) Mix the blood sample and the anti-coagulant thoroughly and slowly by gentle inversion. (III) Transfer 100 µL of the blood sample to a 1.5-mL tube immediately after mixing it. (IV) Add 500 µL of OptiLyse C Lysing Solution to the blood sample and mix it gently by tapping. (V) Incubate it for 10 min at room temperature (18–25 °C). (VI) Add 500 µL of DPBS to the blood sample and mix it gently by tapping. (VII) Leave the blood sample for 10 min at room temperature. This study was approved by the Institutional Ethics Committee of the Faculty of Medicine, the University of Tokyo (#11049-[5]). Written informed consents were obtained from the blood donors.

  5. Supplementary Figure 5 Procedure for preparing a breast cancer cell sample.

    (I) Incubate MCF-7 cells seeded in a 12-well plate for 24 h with multiple concentrations of paclitaxel. (II) Aspirate the cell medium in the 12-well plate, wash the cells with 1 mL of DPBS, and aspirate it again. (III) Add 100 µL of trypsin to the sample and incubate it for ~5 min at 37 °C. (IV) Add 1 mL of complete medium to the sample to dilute the cell suspension and mix it by pipetting up and down a few times with a micropipette to break up any clumps of cells. (V) Place a 40-µm cell strainer on top of a 2-mL conical tube. Pass cells through the cell strainer to remove clumps and debris. (VI) Put the single-cell suspension into a 1-mL syringe and load the syringe on a syringe pump.

Supplementary information

  1. Combined Supplementary Information

    Supplementary Figures 1–5 and Supplementary Methods

  2. Reporting Summary

  3. Supplementary Data 1

    MATLAB scripts for image construction

  4. Supplementary Data 2

    AutoCAD design files for hydrodynamic and inertial focusing microfluidic devices

  5. Supplementary Data 3

    MATLAB scripts for image segmentation

  6. Supplementary Video 1

    Procedures for microfluidic device fabrication

  7. Supplementary Video 2

    Procedures for high-throughput imaging flow cytometry by optofluidic time-stretch microscopy

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