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High-throughput imaging flow cytometry by optofluidic time-stretch microscopy

Nature Protocols volume 13, pages 1603–1631 (2018)Cite this article

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Abstract

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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Fig. 1: Overview of the protocol for optofluidic time-stretch microscopy.
Fig. 2: Optical time-stretch microscope.
Fig. 3: Performance of optical time-stretch microscopy.
Fig. 4: Schematic illustration and working principles of cell focusing in microfluidic devices.
Fig. 5: Fabrication procedures.
Fig. 6: Procedure for fabricating a microfluidic device for hydrodynamic focusing or inertial focusing.
Fig. 7: Digital image processing.
Fig. 8: Graphical illustration of the MMD.
Fig. 9: Imaging flow cytometry and analysis of E. gracilis cells.
Fig. 10: Imaging flow cytometry and analysis of blood cells.
Fig. 11: Imaging flow cytometry and analysis of MCF-7 cells.

References

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Book  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

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

    Google Scholar 

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

    Google Scholar 

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

    Article  CAS  Google Scholar 

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

  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).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Book  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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. 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).

    Google Scholar 

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Acknowledgements

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.

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Authors and Affiliations

  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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  1. Cheng Lei
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Contributions

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.

Corresponding authors

Correspondence to Cheng Lei or Keisuke Goda.

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The authors declare no competing interests.

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Related links

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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Integrated supplementary information

Supplementary Figure 1 Design of microfluidic devices.

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

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.

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.

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.

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

Combined Supplementary Information

Supplementary Figures 1–5 and Supplementary Methods

Reporting Summary

Supplementary Data 1

MATLAB scripts for image construction

Supplementary Data 2

AutoCAD design files for hydrodynamic and inertial focusing microfluidic devices

Supplementary Data 3

MATLAB scripts for image segmentation

Supplementary Video 1

Procedures for microfluidic device fabrication

Supplementary Video 2

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

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Lei, C., Kobayashi, H., Wu, Y. et al. High-throughput imaging flow cytometry by optofluidic time-stretch microscopy. Nat Protoc 13, 1603–1631 (2018). https://doi.org/10.1038/s41596-018-0008-7

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