Advertisement

Optical Review

, Volume 25, Issue 3, pp 464–472 | Cite as

Optofluidic time-stretch microscopy: recent advances

  • Cheng Lei
  • Nao Nitta
  • Yasuyuki Ozeki
  • Keisuke Goda
Special Section: Regular Paper Optics Awards 2017 (OA 2017)
Part of the following topical collections:
  1. Optics Awards 2017 (OA2017)

Abstract

Flow cytometry is an indispensable method for valuable applications in numerous fields such as immunology, pathology, pharmacology, molecular biology, and marine biology. Optofluidic time-stretch microscopy is superior to conventional flow cytometry methods for its capability to acquire high-quality images of single cells at a high-throughput exceeding 10,000 cells per second. This makes it possible to extract copious information from cellular images for accurate cell detection and analysis with the assistance of machine learning. Optofluidic time-stretch microscopy has proven its effectivity in various applications, including microalga-based biofuel production, evaluation of thrombotic disorders, as well as drug screening and discovery. In this review, we discuss the principles and recent advances of optofluidic time-stretch microscopy.

Keywords

Optofluidic time-stretch microscopy Machine learning Microfluidics High-throughput cell analysis Single-cell analysis 

Notes

Acknowledgements

This work was primarily funded by the ImPACT Program of the CSTI (Cabinet Office, Government of Japan) and partly by Noguchi Shitagau Research Grant, New Technology Development Foundation, Konica Minolta Imaging Science Encouragement Award, JSPS KAKENHI Grant numbers 25702024 and 25560190, JGC-S Scholarship Foundation, Mitsubishi Foundation, TOBIRA Award, and Takeda Science Foundation. K. G. was partly supported by Burroughs Wellcome Foundation. The fabrication of the microfluidic device was conducted at the Center for Nano Lithography & Analysis, University of Tokyo, supported by the MEXT, Japan.

Compliance with ethical standards

Conflict of interest

The authors declare no competing financial interests.

References

  1. 1.
    Goda, K., Ayazi, A., Gossett, D.R., Sadasivam, J., Lonappan, C.K., Sollier, E., Fard, A.M., Hur, S.C., Adam, J., Murray, C., Wang, C., Brackbill, N., Di Carlo, D., Jalali, B.: High-throughput single-microparticle imaging flow analyzer. Proc. Natl. Acad. Sci. 109(29), 11630–11635 (2012)ADSCrossRefGoogle Scholar
  2. 2.
    Malo, N., Hanley, J.A., Cerquozzi, S., Pelletier, J., Nadon, R.: Statistical practice in high-throughput screening data analysis. Nat. Biotechnol. 24(2), 167–175 (2006)CrossRefGoogle Scholar
  3. 3.
    Corash, L.: Measurement of platelet activation by fluorescence-activated flow cytometry. Blood Cells 16(1), 97–108 (1990)Google Scholar
  4. 4.
    Usaj, M.M., Styles, E.B., Verster, A.J., Friesen, H., Boone, C., Andrews, B.J.: High-content screening for quantitative cell biology. Trends Cell Biol. 26(8), 598–611 (2016)CrossRefGoogle Scholar
  5. 5.
    Porichis, F., Hart, M.G., Griesbeck, M., Everett, H.L., Hassan, M., Baxter, A.E., Lindqvist, M., Miller, S.M., Soghoian, D.Z., Kavanagh, D.G., Reynolds, S., Norris, B., Mordecai, S.K., Quan, N., Lai, C., Kaufmann, D.E.: High-throughput detection of miRNAs and gene-specific mRNA at the single-cell level by flow cytometry. Nat. Commun. 5, 5641 (2014)ADSCrossRefGoogle Scholar
  6. 6.
    Goda, K., Tsia, K.K., Jalali, B.: Serial time-encoded amplified imaging for real-time observation of fast dynamic phenomena. Nature 458(7242), 1145–1149 (2009)ADSCrossRefGoogle Scholar
  7. 7.
    Lei, C., Guo, B., Cheng, Z., Goda, K.: Optical time-stretch imaging: principles and applications. Appl. Phys. Rev. 3(1), 011102 (2016)ADSCrossRefGoogle Scholar
  8. 8.
    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(10), 1743–1756 (2016)CrossRefGoogle Scholar
  9. 9.
    Ugawa, M., Lei, C., Nozawa, T., Ideguchi, T., Di Carlo, D., Ota, S., Ozeki, Y., Goda, K.: High-throughput optofluidic particle profiling with morphological and chemical specificity. Opt. Lett. 40(20), 4803–4806 (2015)ADSCrossRefGoogle Scholar
  10. 10.
    Lei, C., Ito, T., Ugawa, M., Nozawa, T., Iwata, O., Maki, M., Okada, G., Kobayashi, H., Sun, X., Tiamsak, P., Tsumura, N., Suzuki, K., Di Carlo, D., Ozeki, Y., Goda, K.: High-throughput label-free image cytometry and image-based classification of live Euglena gracilis. Biomed. Opt. Express 7(7), 2703–2708 (2016)CrossRefGoogle Scholar
  11. 11.
    Lai, Q.T.K., Lee, K.C.M., Tang, A.H.L., Wong, K.K.Y., So, H.K.H., Tsia, K.K.: High-throughput time-stretch imaging flow cytometry for multi-class classification of phytoplankton. Opt. Express 24(25), 28170–28184 (2016)ADSCrossRefGoogle Scholar
  12. 12.
    Jiang, Y., Lei, C., Yasumoto, A., Kobayashi, H., Aisaka, Y., Ito, T., Guo, B., Nitta, N., Kutsuna, N., Ozeki, Y., Nakagawa, A., Yatomi, Y., Goda, K.: Label-free detection of aggregated platelets in blood by machine-learning-aided optofluidic time-stretch microscopy. Lab Chip 17(14), 2337–2530 (2017)CrossRefGoogle Scholar
  13. 13.
    Kobayashi, H., Lei, C., Wu, Y., Mao, A., Jiang, Y., Guo, B., Ozeki, Y., Goda, K.: Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning. Sci. Rep. 7(1), 12454 (2017)ADSCrossRefGoogle Scholar
  14. 14.
    Golden, J.P., Justin, G.A., Nasir, M., Ligler, F.S.: Hydrodynamic focusing-a versatile tool. Anal. Bioanal. Chem. 402(1), 325–335 (2012)CrossRefGoogle Scholar
  15. 15.
    Di Carlo, D.: Inertial microfluidics. Lab Chip 9(21), 3038–3046 (2009)CrossRefGoogle Scholar
  16. 16.
    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(24), 4629–4637 (2014)CrossRefGoogle Scholar
  17. 17.
    Goda, K., Jalali, B.: Dispersive Fourier transformation for fast continuous single-shot measurements. Nat. Photonics 7(2), 102–112 (2013)ADSCrossRefGoogle Scholar
  18. 18.
    Fargione, J., Hill, J., Tilman, D., Polasky, S., Hawthorne, P.: Land clearing and the biofuel carbon debt. Science 319(5867), 1235–1238 (2008)ADSCrossRefGoogle Scholar
  19. 19.
    Giometto, A., Altermatt, F., Maritan, A., Stocker, R., Rinaldo, A.: Generalized receptor law governs phototaxis in the phytoplankton Euglena gracilis. Proc. Natl. Acad. Sci. 112(22), 7045–7050 (2015)ADSCrossRefGoogle Scholar
  20. 20.
    Rezic, T., Filipovic, J., Santek, B.: Photo-mixotrophic cultivation of algae Euglena gracilis for lipid production. Agric. Conspec. Sci. 78(1), 65–69 (2013)Google Scholar
  21. 21.
    Wilson, R.M., Michel, P., Olsen, S., Gibberd, R.W., Vincent, C., El-Assady, R., Rasslan, O., Qsous, S., Macharia, W.M., Sahel, A., Whittaker, S., Abdo-Ali, M., Letaief, M., Ahmed, N.A., Abdellatif, A., Larizgoitia, I., Worki, W.H.: O.P.S.E.A.: patient safety in developing countries: retrospective estimation of scale and nature of harm to patients in hospital. Br. Med. J. 344, e832 (2012)CrossRefGoogle Scholar
  22. 22.
    Raskob, G.E., Angchaisuksiri, P., Blanco, A.N., Buller, H., Gallus, A., Hunt, B.J., Hylek, E.M., Kakkar, A., Konstantinides, S.V., McCumber, M., Ozaki, Y., Wendelboe, A., Weitz, J.I., World, I.S.C.: Thrombosis: a major contributor to the global disease burden. J. Thromb. Haemost. 12(10), 1580–1590 (2014)CrossRefGoogle Scholar
  23. 23.
    Jackson, S.P.: The growing complexity of platelet aggregation. Blood 109(12), 5087–5095 (2007)CrossRefGoogle Scholar
  24. 24.
    Fabre, J.E., Nguyen, M.T., Latour, A., Keifer, J.A., Audoly, L.P., Coffman, T.M., Koller, B.H.: Decreased platelet aggregation, increased bleeding time and resistance to thromboembolism in P2Y1-deficient mice. Nat. Med. 5(10), 1199–1202 (1999)CrossRefGoogle Scholar
  25. 25.
    Bull, B.S., Schneiderman, M.A., Brecher, G.: Platelet counts with the Coulter counter. Am. J. Clin. Pathol. 44(6), 678–688 (1965)CrossRefGoogle Scholar
  26. 26.
    Satoh, K., Yatomi, Y., Kubota, F., Ozaki, Y.: Small aggregates of platelets can be detected sensitively by a flow cytometer equipped with an imaging device: mechanisms of epinephrine-induced aggregation and antiplatelet effects of beraprost. Cytometry 48(4), 194–201 (2002)CrossRefGoogle Scholar
  27. 27.
    Guo, B., Lei, C., Ito, T., Jiang, Y., Ozeki, Y., Goda, K.: High-throughput accurate single-cell screening of Euglena gracilis with fluorescence-assisted optofluidic time-stretch microscopy. PLoS One 11(11), e0166214 (2016)CrossRefGoogle Scholar
  28. 28.
    Futamura, Y., Kawatani, M., Kazami, S., Tanaka, K., Muroi, M., Shimizu, T., Tomita, K., Watanabe, N., Osada, H.: Morphobase, an encyclopedic cell morphology database, and its use for drug target identification. Chem Biol 19(12), 1620–1630 (2012)CrossRefGoogle Scholar
  29. 29.
    Heynen-Genel, S., Pache, L., Chanda, S.K., Rosen, J.: Functional genomic and high-content screening for target discovery and deconvolution. Expert Opin. Drug Dis. 7(10), 955–968 (2012)CrossRefGoogle Scholar
  30. 30.
    Wojcik, K., Dobrucki, J.W.: Interaction of a DNA intercalator DRAQ5, and a minor groove binder SYTO17, with chromatin in live cells-influence on chromatin organization and histone-DNA interactions. Cytom. A 73A(6), 555–562 (2008)CrossRefGoogle Scholar
  31. 31.
    Blasi, T., Hennig, H., Summers, H.D., Theis, F.J., Cerveira, J., Patterson, J.O., Davies, D., Filby, A., Carpenter, A.E., Rees, P.: Label-free cell cycle analysis for high-throughput imaging flow cytometry. Nat. Commun. 7, 10256 (2016)ADSCrossRefGoogle Scholar

Copyright information

© The Optical Society of Japan 2018

Authors and Affiliations

  1. 1.Department of ChemistryUniversity of TokyoTokyoJapan
  2. 2.Japan Science and Technology AgencyKawaguchiJapan
  3. 3.Department of Electrical Engineering and Information SystemsUniversity of TokyoTokyoJapan
  4. 4.Department of Electrical EngineeringUniversity of CaliforniaLos AngelesUSA

Personalised recommendations