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
- A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples
- 光：科学与应用 2018年7卷第5期页码：616-627
- 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
  中图分类号：
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Zoltán Gӧrӧcs, Miu Tamamitsu, Vittorio Bianco, 等. A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples[J]. 光：科学与应用, 2018,7(5):616-627.

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).
Zoltán Gӧrӧcs, Miu Tamamitsu, Vittorio Bianco, 等. A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples[J]. 光：科学与应用, 2018,7(5):616-627. DOI： 10.1038/s41377-018-0067-0.

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

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