The throughput of Coulter-type microfluidic devices for single-particle analysis is limited by the problem of coincidences, i.e., two or more particles visiting the sensing zone in close proximity. Here, we report a novel microfluidic impedance cytometer able to provide a throughput as high as 2500 particles/s. This is possible thanks to an original strategy that enables the arbitration of coincidences, i.e., their resolution into the composing single events. In order to achieve real-time processing of the recorded electrical fingerprints, an innovative neural-network approach is implemented. The present system, besides providing high-throughput counting, also enables accurate cell characterization. In particular, it is possible to discern whether an event with abnormally high amplitude is a coincidence or an unusually large (possibly pathological) cell.

Caselli, F., de Ninno, A., Reale, R., Businaro, L., Bisegna, P. (2020). Machine learning-enabled high-speed impedance cytometry. In MicroTAS 2020 - 24th International Conference on Miniaturized Systems for Chemistry and Life Sciences (pp.801-802). Chemical and Biological Microsystems Society.

Machine learning-enabled high-speed impedance cytometry

Caselli F.
;
Reale R.;Bisegna P.
2020-01-01

Abstract

The throughput of Coulter-type microfluidic devices for single-particle analysis is limited by the problem of coincidences, i.e., two or more particles visiting the sensing zone in close proximity. Here, we report a novel microfluidic impedance cytometer able to provide a throughput as high as 2500 particles/s. This is possible thanks to an original strategy that enables the arbitration of coincidences, i.e., their resolution into the composing single events. In order to achieve real-time processing of the recorded electrical fingerprints, an innovative neural-network approach is implemented. The present system, besides providing high-throughput counting, also enables accurate cell characterization. In particular, it is possible to discern whether an event with abnormally high amplitude is a coincidence or an unusually large (possibly pathological) cell.
24th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2020
2020
Chemical and Biological Microsystems Society (CBMS)
Rilevanza internazionale
2020
Settore ING-IND/34 - BIOINGEGNERIA INDUSTRIALE
English
Coincidence arbitration
Microfluidic impedance cytometry
Neural networks
Real-time processing
Single-cell analysis
Intervento a convegno
Caselli, F., de Ninno, A., Reale, R., Businaro, L., Bisegna, P. (2020). Machine learning-enabled high-speed impedance cytometry. In MicroTAS 2020 - 24th International Conference on Miniaturized Systems for Chemistry and Life Sciences (pp.801-802). Chemical and Biological Microsystems Society.
Caselli, F; de Ninno, A; Reale, R; Businaro, L; Bisegna, P
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/292180
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