Computational cytometer based on magnetically modulated coherent imaging and deep learning

Yibo Zhang; Mengxing Ouyang; Aniruddha Ray; Tairan Liu; Janay Kong; Bijie Bai; Donghyuk Kim; Alexander Guziak; Yi Luo; Alborz Feizi; Katherine Tsai; Zhuoran Duan; Xuewei Liu; Danny Kim; Chloe Cheung; Sener Yalcin; Hatice Ceylan Koydemir; Omai B. Garner; Dino Di Carlo; Aydogan Ozcan

doi:10.1038/s41377-019-0203-5

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Computational cytometer based on magnetically modulated coherent imaging and deep learning

Articles

- Computational cytometer based on magnetically modulated coherent imaging and deep learning
- Light: Science & Applications Vol. 8, Issue 5, Pages: 845-859(2019)
- Affiliations：
  
  1.Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA
  2.Department of Bioengineering, University of California, Los Angeles, CA 90095, USA
  3.California NanoSystems Institute, University of California, Los Angeles, CA 90095, USA
  4.Department of Physics and Astronomy, University of Toledo, Toledo, OH 43606, USA
  5.Department of Physics and Astronomy, University of California, Los Angeles, CA 90095, USA
  6.Yale School of Medicine, New Haven, CT 06510, USA
  7.Department of Biochemistry, University of California, Los Angeles, CA 90095, USA
  8.Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA 90095, USA
  9.Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, CA 90095, USA
  10.Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA 90095, USA
  11.Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Author bio：
  
  Aydogan Ozcan (ozcan@ucla.edu)
- Funds：
- DOI：10.1038/s41377-019-0203-5
  CLC：
- Published：2019，
  
  Published Online：02 October 2019，
  
  Received：26 May 2019，
  
  Revised：05 September 2019，
  
  Accepted：12 September 2019
- Accepted：
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Zhang, Y. B. et al. Computational cytometer based on magnetically modulated coherent imaging and deep learning. Light: Science & Applications, 8, 845-859 (2019).
DOI：

Zhang, Y. B. et al. Computational cytometer based on magnetically modulated coherent imaging and deep learning. Light: Science & Applications, 8, 845-859 (2019). DOI： 10.1038/s41377-019-0203-5.

摘要

Abstract

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here

we present a computational cytometer based on magnetically modulated lensless speckle imaging

which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells

detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN)

which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique

we built a high-throughput

compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments

we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood

which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact

cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications.

关键词

Keywords

references

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