A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry
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 cyto...
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| Veröffentlicht in: | Analytical and bioanalytical chemistry 2020-06, Vol.412 (16), p.3835-3845 |
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| creator | Honrado, Carlos McGrath, John S. Reale, Riccardo Bisegna, Paolo Swami, Nathan S. Caselli, Frederica |
| description | 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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| doi_str_mv | 10.1007/s00216-020-02497-9 |
| format | Article |
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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Analytical and bioanalytical chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Honrado, Carlos</au><au>McGrath, John S.</au><au>Reale, Riccardo</au><au>Bisegna, Paolo</au><au>Swami, Nathan S.</au><au>Caselli, Frederica</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry</atitle><jtitle>Analytical and bioanalytical chemistry</jtitle><stitle>Anal Bioanal Chem</stitle><addtitle>Anal Bioanal Chem</addtitle><date>2020-06-01</date><risdate>2020</risdate><volume>412</volume><issue>16</issue><spage>3835</spage><epage>3845</epage><pages>3835-3845</pages><issn>1618-2642</issn><eissn>1618-2650</eissn><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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| subjects | Analysis Analytical Chemistry Beads Bioanalytics and Higher Order Electrokinetics Biochemistry Characterization and Evaluation of Materials Chemistry Chemistry and Materials Science Cytometry Data transmission Electric Impedance Electric properties Erythrocytes Erythrocytes - cytology Flow Cytometry - methods Food Science Humans Impedance Laboratory Medicine Learning algorithms Machine learning Microfluidic Analytical Techniques - methods Microfluidics Monitoring/Environmental Analysis Neural networks Neural Networks, Computer Paper in Forefront Particle sorting Real time Recurrent neural networks Yeasts |
| title | A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry |
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