Deciphering impedance cytometry signals with neural networks
Deciphering impedance cytometry signals with neural networks
Microfluidic impedance cytometry is a label-free technique for high-throughput single-cell analysis. Multi-frequency impedance measurements provide data that allows full characterisation of cells, linking electrical phenotype to individual biophysical properties. To efficiently extract the information embedded in the electrical signals, potentially in real-time, tailored signal processing is needed. Artificial intelligence approaches provide a promising new direction. Here we demonstrate the ability of neural networks to decipher impedance cytometry signals in two challenging scenarios: (i) to determine the intrinsic dielectric properties of single cells directly from raw impedance data streams, (ii) to capture single-cell signals that are hidden in the measured signals of coincident cells. The accuracy of the results and the high processing speed (fractions of ms per cell) demonstrate that neural networks can have an important role in impedance-based single-cell analysis.
1714-1722
Caselli, Federica
1df2ca59-725a-453b-99af-c8f4baf0cf44
Reale, Riccardo
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Ninno, Adele De
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Spencer, Daniel
c84608fb-1c80-427c-aaa2-671bdb416085
Morgan, Hywel
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Bisegna, Paolo
1754a0ce-0ba6-43f6-8b86-ad5df7c47743
24 March 2022
Caselli, Federica
1df2ca59-725a-453b-99af-c8f4baf0cf44
Reale, Riccardo
22c6c837-e07c-4991-a281-8f78d8299e6e
Ninno, Adele De
5b3f045f-92c5-4645-909f-535425986109
Spencer, Daniel
c84608fb-1c80-427c-aaa2-671bdb416085
Morgan, Hywel
de00d59f-a5a2-48c4-a99a-1d5dd7854174
Bisegna, Paolo
1754a0ce-0ba6-43f6-8b86-ad5df7c47743
Caselli, Federica, Reale, Riccardo, Ninno, Adele De, Spencer, Daniel, Morgan, Hywel and Bisegna, Paolo
(2022)
Deciphering impedance cytometry signals with neural networks.
Lab on a Chip, 22 (9), .
(doi:10.1039/D2LC00028H).
Abstract
Microfluidic impedance cytometry is a label-free technique for high-throughput single-cell analysis. Multi-frequency impedance measurements provide data that allows full characterisation of cells, linking electrical phenotype to individual biophysical properties. To efficiently extract the information embedded in the electrical signals, potentially in real-time, tailored signal processing is needed. Artificial intelligence approaches provide a promising new direction. Here we demonstrate the ability of neural networks to decipher impedance cytometry signals in two challenging scenarios: (i) to determine the intrinsic dielectric properties of single cells directly from raw impedance data streams, (ii) to capture single-cell signals that are hidden in the measured signals of coincident cells. The accuracy of the results and the high processing speed (fractions of ms per cell) demonstrate that neural networks can have an important role in impedance-based single-cell analysis.
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Accepted/In Press date: 23 March 2022
Published date: 24 March 2022
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Funding Information:
This work was supported by Regione Lazio under grant E85F21002390002 (MicroSystemQ project, Research Groups 2020 Programme).
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© 2022 The Royal Society of Chemistry
Identifiers
Local EPrints ID: 457528
URI: http://eprints.soton.ac.uk/id/eprint/457528
ISSN: 1473-0197
PURE UUID: 763ffa5e-fb4c-4771-a60c-ac1b12c70aff
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Date deposited: 10 Jun 2022 16:35
Last modified: 17 Mar 2024 07:19
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Contributors
Author:
Federica Caselli
Author:
Riccardo Reale
Author:
Adele De Ninno
Author:
Daniel Spencer
Author:
Hywel Morgan
Author:
Paolo Bisegna
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