Advance of microfluidic flow cytometry enabling high-throughput characterization of single-cell electrical and structural properties
© 2023 International Society for Advancement of Cytometry..
This paper reported a micro flow cytometer capable of high-throughput characterization of single-cell electrical and structural features based on constrictional microchannels and deep neural networks. When single cells traveled through microchannels with constricted cross-sectional areas, they effectively blocked concentrated electric field lines, producing large impedance variations. Meanwhile, the traveling cells were confined within the cross-sectional areas of the constrictional microchannels, enabling the capture of high-quality images without losing focuses. Then single-cell features from impedance profiles and optical images were extracted from customized recurrent and convolution networks (RNN and CNN), which were further fused for cell-type classification based on support vector machines (SVM). As a demonstration, two leukemia cell lines (e.g., HL60 vs. Jurkat) were analyzed, producing high-classification accuracies of 99.3% based on electrical features extracted from Long Short-Term Memory (LSTM) of RNN, 96.7% based on structural features extracted from Resnet18 of CNN and 100.0% based on combined features enabled by SVM. The microfluidic flow cytometry developed in this study may provide a new perspective for the field of single-cell analysis.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:105 |
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| Enthalten in: |
Cytometry. Part A : the journal of the International Society for Analytical Cytology - 105(2024), 2 vom: 04. Feb., Seite 139-145 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Huang, Xukun [VerfasserIn] |
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| Links: |
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| Themen: |
Constrictional microchannel |
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| Anmerkungen: |
Date Completed 19.02.2024 Date Revised 08.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/cyto.a.24806 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM363077421 |
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| 245 | 1 | 0 | |a Advance of microfluidic flow cytometry enabling high-throughput characterization of single-cell electrical and structural properties |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023 International Society for Advancement of Cytometry. | ||
| 520 | |a This paper reported a micro flow cytometer capable of high-throughput characterization of single-cell electrical and structural features based on constrictional microchannels and deep neural networks. When single cells traveled through microchannels with constricted cross-sectional areas, they effectively blocked concentrated electric field lines, producing large impedance variations. Meanwhile, the traveling cells were confined within the cross-sectional areas of the constrictional microchannels, enabling the capture of high-quality images without losing focuses. Then single-cell features from impedance profiles and optical images were extracted from customized recurrent and convolution networks (RNN and CNN), which were further fused for cell-type classification based on support vector machines (SVM). As a demonstration, two leukemia cell lines (e.g., HL60 vs. Jurkat) were analyzed, producing high-classification accuracies of 99.3% based on electrical features extracted from Long Short-Term Memory (LSTM) of RNN, 96.7% based on structural features extracted from Resnet18 of CNN and 100.0% based on combined features enabled by SVM. The microfluidic flow cytometry developed in this study may provide a new perspective for the field of single-cell analysis | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a constrictional microchannel | |
| 650 | 4 | |a deep neural network | |
| 650 | 4 | |a high performance | |
| 650 | 4 | |a microfluidic flow cytometry | |
| 700 | 1 | |a Chen, Xiao |e verfasserin |4 aut | |
| 700 | 1 | |a Tan, Huiwen |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Minruihong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yimin |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Yuanchen |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Deyong |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Junbo |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yueying |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Jian |e verfasserin |4 aut | |
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