A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples

Zoltán Gӧrӧcs; Miu Tamamitsu; Vittorio Bianco; Patrick Wolf; Shounak Roy; Koyoshi Shindo; Kyrollos Yanny; Yichen Wu; Hatice Ceylan Koydemir; Yair Rivenson; Aydogan Ozcan

doi:10.1038/s41377-018-0067-0

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A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples

Original Articles

- A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples
- Light: Science & Applications Vol. 7, Issue 5, Pages: 616-627(2018)
- Affiliations：
  
  1.Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
  2.Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
  3.California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
- Author bio：
  
  Aydogan Ozcan (ozcan@ucla.edu)
- Funds：
- DOI：10.1038/s41377-018-0067-0
  CLC：
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Gӧrӧcs, Z. et al. A deep learning-enabled portable imaging flow cytometer for cost-effective, highthroughput, and label-free analysis of natural water samples. Light: Science & Applications, 5, 616-627 (2018).
DOI：

Gӧrӧcs, Z. et al. A deep learning-enabled portable imaging flow cytometer for cost-effective, highthroughput, and label-free analysis of natural water samples. Light: Science & Applications, 5, 616-627 (2018). DOI： 10.1038/s41377-018-0067-0.

摘要

Abstract

We report a deep learning-enabled field-portable and cost-effective imaging flow cytometer that automatically captures phase-contrast color images of the contents of a continuously flowing water sample at a throughput of 100 mL/h. The device is based on partially coherent lens-free holographic microscopy and acquires the diffraction patterns of flowing micro-objects inside a microfluidic channel. These holographic diffraction patterns are reconstructed in real time using a deep learning-based phase-recovery and image-reconstruction method to produce a color image of each micro-object without the use of external labeling. Motion blur is eliminated by simultaneously illuminating the sample with red, green, and blue light-emitting diodes that are pulsed. Operated by a laptop computer, this portable device measures 15.5 cm × 15 cm × 12.5 cm, weighs 1 kg, and compared to standard imaging flow cytometers, it provides extreme reductions of cost, size and weight while also providing a high volumetric throughput over a large object size range. We demonstrated the capabilities of this device by measuring ocean samples at the Los Angeles coastline and obtaining images of its micro- and nanoplankton composition. Furthermore, we measured the concentration of a potentially toxic alga (,Pseudo-nitzschia,) in six public beaches in Los Angeles and achieved good agreement with measurements conducted by the California Department of Public Health. The cost-effectiveness, compactness, and simplicity of this computational platform might lead to the creation of a network of imaging flow cytometers for large-scale and continuous monitoring of the ocean microbiome, including its plankton composition.

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references

Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).

Behrenfeld, M. J. et al. Biospheric primary production during an ENSO transition. Science 291, 2594–2597 (2001).

Smetacek, V. & Cloern, J. E. On phytoplankton trends. Science 319, 1346–1348 (2008).

Cloern, J. E., Jassby, A. D., Thompson, J. K. & Hieb, K. A. A cold phase of the East Pacific triggers new phytoplankton blooms in San Francisco Bay. Proc. Natl Acad. Sci. USA 104, 18561–18565 (2007).

Benincà, E. et al. Chaos in a long-term experiment with a plankton community. Nature 451, 822–825 (2008).

Campisi, J. & Ahmed, S. A toxic algae bloom in Florida is slaughtering marine life by the masses. https://www.cnn.com/2018/08/02/us/sea-turtles-florida-red-tide-wxc-trnd/index.htmlhttps://www.cnn.com/2018/08/02/us/sea-turtles-florida-red-tide-wxc-trnd/index.html.

Qu, M. Z. et al. Algal blooms: proactive strategy. Science 346, 175–176 (2014).

Flewelling, L. J. et al. Brevetoxicosis: red tides and marine mammal mortalities. Nature 435, 755–756 (2005).

Gobler, C. J. et al. Ocean warming since 1982 has expanded the niche of toxic algal blooms in the North Atlantic and North Pacific oceans. Proc. Natl Acad. Sci. USA 114, 4975–4980 (2017).

Maldonado, M. T., Hughes, M. P., Rue, E. L. & Wells, M. L. The effect of Fe and Cu on growth and domoic acid production by Pseudo-nitzschia multiseries and Pseudo-nitzschia australis. Limnol. Oceanogr. 47, 515–526 (2002).

Lim, H. C. et al. Morphology and molecular characterization of Pseudo-nitzschia (Bacillariophyceae) from Malaysian Borneo, including the new species Pseudo-nitzschia circumpora Sp. Nov. J. Phycol. 48, 1232–1247 (2012).

Phytoplankton monitoring program. https://www.cdph.ca.gov/Programs/CEH/DFDCS/Pages/FDBPrograms/FoodSafetyProgram/PhytoplanktonMonitoringProgram.aspxhttps://www.cdph.ca.gov/Programs/CEH/DFDCS/Pages/FDBPrograms/FoodSafetyProgram/PhytoplanktonMonitoringProgram.aspx.

Trask, B. J. & van den Engh, G. J. Elgershuizen JHBW. Analysis of phytoplankton by flow cytometry. Cytometry 2, 258–264 (1982).

Olson, R. J., Shalapyonok, A. & Sosik, H. M. An automated submersible flow cytometer for analyzing pico- and nanophytoplankton: FlowCytobot. Deep Sea Res. Part Ⅰ Oceanogr. Res. Pap. 50, 301–315 (2003).

Pomati, F., Jokela, J., Simona, M., Veronesi, M. & Ibelings, B. W. An automated platform for phytoplankton ecology and aquatic ecosystem monitoring. Environ. Sci. Technol. 45, 9658–9665 (2011).

Gérikas Ribeiro, C., Marie, D., Lopes dos Santos, A., Pereira Brandini, F. & Vaulot, D. Estimating microbial populations by flow cytometry: comparison between instruments. Limnol. Oceanogr. Methods 14, 750–758 (2016).

Leroux, R. et al. Combining laser diffraction, flow cytometry and optical microscopy to characterize a nanophytoplankton bloom in the Northwestern Mediterranean. Prog. Oceanogr. 163, 248–259 (2018).

Olson, R. J. & Sosik, H. M. A submersible imaging-in-flow instrument to analyze nano-and microplankton: imaging FlowCytobot. Limnol. Oceanogr. Methods 5, 195–203 (2007).

Thyssen, M. et al. High-resolution analysis of a North Sea phytoplankton community structure based on in situ flow cytometry observations and potential implication for remote sensing.Biogeosciences 12, 4051–4066 (2015).

Dashkova, V., Malashenkov, D., Poulton, N., Vorobjev, I. & Barteneva, N. S. Imaging flow cytometry for phytoplankton analysis. Methods 112, 188–200 (2017).

Poulton, N. J. in Imaging Flow Cytometry (eds Barteneva, N. S. & Vorobjev, I. A.) 237–247 (Humana Press, New York, NY, 2016). https://doi.org/10.1007/978-1-4939-3302-0_17https://doi.org/10.1007/978-1-4939-3302-0_17

Olson, R. J., Shalapyonok, A., Kalb, D. J., Graves, S. W. & Sosik, H. M. Imaging FlowCytobot modified for high throughput by in-line acoustic focusing of sample particles. Limnol. Oceanogr. Methods 15, 867–874 (2017).

Taucher, J. et al. Influence of ocean acidification on plankton community structure during a winter-to-summer succession: an imaging approach indicates that copepods can benefit from elevated CO2 via indirect food web effects. PLoS ONE 12, e0169737 (2017).

Heflinger, L. O., Stewart, G. L. & Booth, C. R. Holographic motion pictures of microscopic plankton. Appl. Opt. 17, 951–954 (1978).

Xu, W. B., Jericho, M. H., Meinertzhagen, I. A. & Kreuzer, H. J. Digital in-line holography for biological applications. Proc. Natl Acad. Sci. USA 98, 11301–11305 (2001).

Göröcs, Z. & Ozcan, A. On-chip biomedical imaging. IEEE Rev. Biomed. Eng. 6, 29–46 (2013).

Pfitsch, D. W. et al. Development of a free-drifting submersible digital holographic imaging system. In Proc. of OCEANS 2005 MTS/IEEE 690–696 (IEEE, Washington, DC, USA, 2005), https://doi.org/10.1109/OCEANS.2005.1639833.https://doi.org/10.1109/OCEANS.2005.1639833.

Zetsche, E. M. et al. Imaging-in-flow: digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms. Limnol. Oceanogr. Methods 12, 757–775 (2014).

Kiss, M. Z. et al. Special multicolor illumination and numerical tilt correction in volumetric digital holographic microscopy. Opt. Express 22, 7559–7573 (2014).

Bochdansky, A. B., Jericho, M. H. & Herndl, G. J. Development and deployment of a point-source digital inline holographic microscope for the study of plankton and particles to a depth of 6000 m. Limnol. Oceanogr. Methods 11, 28–40 (2013).

Yourassowsky, C. & Dubois, F. High throughput holographic imaging-in-flow for the analysis of a wide plankton size range. Opt. Express 22, 6661–6673 (2014).

Kamykowski, D. & Zentara, S. J. The diurnal vertical migration of motile phytoplankton through temperature gradients1. Limnol. Oceanogr. 22, 148–151 (1977).

Villarino, M. L. et al. Evidence of in situ diel vertical migration of a red-tide microplankton species in Ría de Vigo (NW Spain). Mar. Biol. 123, 607–617 (1995).

Suárez, L. V. et al. Thin layers of Pseudo-nitzschia spp. and the fate of Dinophysis acuminata during an upwelling-downwelling cycle in a Galician RÃa. Limnol. Oceanogr. 53, 1816–1834 (2008).

Sullivan, J. M., Donaghay, P. L. & Rines, J. E. B. Coastal thin layer dynamics: consequences to biology and optics. Cont. Shelf Res 30, 50–65 (2010).

Bishara, W., Su, T. W., Coskun, A. F. & Ozcan, A. Lensfree on-chip microscopy over a wide field-of-view using pixel super-resolution. Opt. Express 18, 11181–11191 (2010).

Bishara, W., Zhu, H. Y. & Ozcan, A. Holographic opto-fluidic microscopy. Opt. Express 18, 27499–27510 (2010).

Wu, Y. C. et al. Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery. ArXiv: 1803.08138, 2018.

OpenROV underwater drones, an open source submersible platform. https://www.openrov.com/products/openrov28/https://www.openrov.com/products/openrov28/.

Isikman, S. O., Greenbaum, A., Luo, W., Coskun, A. F. & Ozcan, A. Giga-pixel lensfree holographic microscopy and tomography using color image sensors. PLoS ONE 7, e45044 (2012).

Zhang, Y. B., Wang, H. D., Wu, Y. C., Tamamitsu, M. & Ozcan, A. Edge sparsity criterion for robust holographic autofocusing. Opt. Lett. 42, 3824–3827 (2017).

Tamamitsu, M., Zhang, Y. B., Wang, H. D., Wu, Y. C. & Ozcan, A. Comparison of Gini index and Tamura coefficient for holographic autofocusing based on the edge sparsity of the complex optical wavefront. ArXiv: 1708.08055, 2017.

Luo, W., Zhang, Y. B., Göröcs, Z., Feizi, A. & Ozcan, A. Propagation phasor approach for holographic image reconstruction. Sci. Rep. 6, 22738 (2016).

Rivenson, Y., Zhang, Y. B., Günaydın, H., Teng, D. & Ozcan, A. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light Sci. Appl. 7, 17141 (2018).

Luo, W., Zhang, Y. B., Feizi, A., Göröcs, Z. & Ozcan, A. Pixel super-resolution using wavelength scanning. Light Sci. Appl. 5, e16060 (2016).

Luo, W., Greenbaum, A., Zhang, Y. B. & Ozcan, A. Synthetic aperture-based on-chip microscopy. Light Sci. Appl. 4, e261 (2015).

Greenbaum, A. & Ozcan, A. Maskless imaging of dense samples using pixel super-resolution based multi-height lensfree on-chip microscopy. Opt. Express 20, 3129–3143 (2012).

Faulkner, H. M. L. & Rodenburg, J. M. Movable aperture lensless transmission microscopy: a novel phase retrieval algorithm. Phys. Rev. Lett. 93, 023903 (2004).

Bao, P., Zhang, F. C., Pedrini, G. & Osten, W. Phase retrieval using multiple illumination wavelengths. Opt. Lett. 33, 309–311 (2008).

Fienup, J. R. & Wackerman, C. C. Phase-retrieval stagnation problems and solutions. J. Opt. Soc. Am. A 3, 1897–1907 (1986).

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