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A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry

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Abstract

Microfluidic applications such as active particle sorting or selective enrichment require particle classification techniques that are capable of working in real time. In this paper, we explore the use of neural networks for fast label-free particle characterization during microfluidic impedance cytometry. A recurrent neural network is designed to process data from a novel impedance chip layout for enabling real-time multiparametric analysis of the measured impedance data streams. As demonstrated with both synthetic and experimental datasets, the trained network is able to characterize with good accuracy size, velocity, and cross-sectional position of beads, red blood cells, and yeasts, with a unitary prediction time of 0.4 ms. The proposed approach can be extended to other device designs and cell types for electrical parameter extraction. This combination of microfluidic impedance cytometry and machine learning can serve as a stepping stone to real-time single-cell analysis and sorting.

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Funding

This study received funding from the Italian Ministry of Education, University and Research (SIR 2014, Grant RBSI14TX20); from the University of Rome Tor Vergata (Mission Sustainability, Grant E81I18000540005); from the U.S. National Center for Advancing Translational Sciences of the National Institutes of Health (Award Number UL1TR003015); and from Advanced Regenerative Medicine Institute’s BioFab-USA (Subcontract T0163).

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Author notes

  1. Carlos Honrado, John S. McGrath and Riccardo Reale contributed equally to this work.

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA

    Carlos Honrado, John S. McGrath & Nathan S. Swami

  2. Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Via del Politecnico 1, 00133, Rome, Italy

    Riccardo Reale, Paolo Bisegna & Frederica Caselli

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  1. Carlos Honrado
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  2. John S. McGrath
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  4. Paolo Bisegna
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The manuscript was written through contributions of all authors and all authors approved the final version.

Corresponding authors

Correspondence to Nathan S. Swami or Frederica Caselli.

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The reported studies on blood samples have been approved by the University of Virginia Institutional Review Board for Health Sciences Research (IRB-HSR protocol no. 21081) and have been performed in accordance with ethical standards.

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The authors declare that they have no conflicts of interest.

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Published in the topical collection Bioanalytics and Higher Order Electrokinetics with guest editors Mark A. Hayes and Federica Caselli.

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Honrado, C., McGrath, J.S., Reale, R. et al. A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry. Anal Bioanal Chem 412, 3835–3845 (2020). https://doi.org/10.1007/s00216-020-02497-9

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